data research projects

12 Data Science Projects for Beginners and Experts

Data science is a booming industry. Try your hand at these projects to develop your skills and keep up with the latest trends.

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills. The purpose of this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in  enhancing your skills .

12 Data Science Projects to Experiment With

• Building chatbots
• Credit card fraud detection
• Fake news detection
• Forest fire prediction
• Classifying breast cancer
• Driver drowsiness detection
• Recommender systems
• Sentiment analysis
• Exploratory data analysis
• Customer churn analysis
• Recognizing speech emotion
• Customer segmentation

Top Data Science Projects

The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive. In this section, we’ll share a handful of fun and interesting projects designed for all skill levels.

More on Data Science: How to Build Optical Character Recognition (OCR) in Python

1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code:  Build Your First Python Chatbot Project

Chatbots automate a majority of the customer service process,  single-handedly reducing the customer service workload. They utilize a variety of techniques backed by artificial intelligence ,  machine learning and data science.

Chatbots analyze customer inputs and reply with an appropriate mapped response. To train the chatbot, you can use  recurrent neural networks with the  intents JSON dataset , while the implementation can be handled using Python . Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase. 

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code:  Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think. In fact, it’s an issue that has now impacted around 60 percent of credit card holders in the United States. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

The idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either  R or Python with the customer’s transaction history as the data set and ingest it into decision trees ,  artificial neural networks  and logistic regression . As you feed more data to your system, you should be able to increase its overall accuracy. 

3. Fake News Detection

• Data set/Packages: news.csv
• Source code:  Detecting Fake News

In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence .

To curb the spread of fake news , it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with  TfidfVectorizer and  PassiveAggressiveClassifier to separate the real news from the fake one. Some  Python libraries best suited for this project are  pandas ,  NumPy and  scikit-learn . For the data set, you can use News.csv. 

4. Forest Fire Prediction

• Language: Python 
• Data set: Algerian forest fires data set
• Source code: Forest Fire Predictor

Building a forest fire and wildfire  prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

More on Data Science: K-Nearest Neighbor Algorithm: An Introduction

5. Classifying Breast Cancer

• Data set: IDC (Invasive Ductal Carcinoma)
• Source code:  Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can build a breast cancer detection system using Python. Breast cancer cases have been on the rise , and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the  invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use  NumPy ,  OpenCV ,  TensorFlow ,  Keras ,  scikit-learn and  Matplotlib . 

6. Driver Drowsiness Detection

• Source code:  Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers . A  driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives.

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a  deep learning model and libraries such as  OpenCV ,  TensorFlow ,  Pygame  and  Keras .

More on Data Science: 8 Data Visualization Tools That Every Data Scientist Should Know

7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code:  Movie Recommendation System Project in R

Media platforms like YouTube and Netflix recommend what to watch next using a tool called the  recommender/recommendation system . It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that generates what the user might like to watch next.

Based on your preferences and input data, you can build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use  recommenderlab ,  ggplot2 ,  reshape2 and  data.table . 

8. Sentiment Analysis

• Data set: janeaustenR
• Source code:  Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is an AI-powered technique that allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews and survey responses, and span a range of emotions such as happy, angry, positive, love, negative and excitement.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them critical insights into customers’ reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with  janeaustenR ’s data set along with the  tidytext package . 

9. Exploratory Data Analysis

• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code:  Exploratory data analysis in Python

Exploratory data analysis (EDA) plays a key role in data analysis as it helps you make sense of your data and often involves visualizing data points for better exploration. You can pick from a range of visuals , including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data . Once you have identified patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy,  seaborn and matplotlib.

A great source for EDA data sets is the  IBM TechXchange Community . 

10. Customer Churn Analysis

• Data set: Telco Customer Churn
• Source code: Telco Customer Churn options

Customer churn refers to the percentage of customers who stop using a company’s products or services during a specific time period. Businesses analyze churn to understand what led customers to leave, looking at factors like demographic information, services selected and customer account details. This way, they can identify other at-risk customers likely to leave and take measures to retain them. 

One way to approach this problem is to use Scikit-learn to build a decision tree , which can help predict which customers are at risk of leaving after being trained on churn data. Kaggle offers a churn data set (listed above) to get started, along with various data set notebooks containing unique source code that you can experiment with.   

11. Recognizing Speech Emotions

• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code:  Speech Emotion Recognition with librosa

Speech contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By  analyzing the emotions behind speech , companies can use this information to restructure their actions, services and products to offer more personalized services.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the  Librosa ,  SoundFile , NumPy, Scikit-learn and  PyAudio packages. For the data set, you can use the  Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) , which contains over 7,300 files. 

12. Customer Segmentation

• Source code:  Customer Segmentation using Machine Learning

Modern businesses strive to deliver highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region and interests. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with  fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology — a field that holds much promise and opportunity but also comes with looming challenges .

Frequently Asked Questions

What projects can be done in data science.

• Build a chatbot using Python.
• Create a movie recommendation system using R.
• Detect credit card fraud using R or Python.

How do I start a data science project?

To start a data science project, first decide what sort of data science project you want to undertake, such as data cleaning, data analysis or data visualization. Then, find a good dataset on a website like data.world or data.gov. From there, you can analyze the data and communicate your results.

How long does a data science project take to complete?

Data science projects vary in length and depend on several variables like the data source, the complexity of the problem you’re trying to solve and your skill level. It could take a few hours or several months.

Recent Artificial Intelligence Articles

Top 10 Data Science Project Ideas in 2024

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Data science is a practical field. You need various hands-on skills to stand out and advance your career. One of the best ways to obtain them is by building end-to-end data science projects that solve complex problems using real-world datasets.

Not sure where to start?

In this article, we provide 10 case studies from finance, healthcare, marketing, manufacturing, and other industries. You can use them as inspiration and adapt them to the domain of your interest.

All projects involve real business cases. Each one starts with a brief description of the problem, followed by an outline of the methodology, then the expected output, and finally, a recommended dataset and a relevant research paper. Most of the datasets are available on Kaggle or can be web scraped.

If you wish to start a project without the trouble of selecting and locating resources, we've prepared a series of engaging and relevant projects on our platform. These projects offer valuable hands-on practice to test your skills.

You can also include them in your portfolio to demonstrate to potential employers your experience in tackling everyday job challenges. For more information, check out the projects page on our website.

Below, we present 10 data science project ideas with step-by-step solutions. But first, we’ll explain what the data science life cycle is and how to execute an end-to-end project. Continue reading to learn to how to recognize and use your resources to turn information into a data science project.

Top 10 Data Science Project Ideas: Table of Contents

The data science life cycle, hospital treatment pricing prediction, youtube comments analysis, illegal fishing classification.

• Bank Customer Segmentation

Dogecoin Cryptocurrency Prices Predictor with LSTM

Book recommendation system, gender detection and age prediction using deep learning, speech emotion recognition for customer satisfaction, traveling agency customer service chatbots, detection of metallic surface defects.

• Data Science Project Ideas: Next Steps\

End-to-end projects involve real-world problems which you solve using the 6 stages of the data science life cycle:

• Business understanding
• Data understanding
• Data preparation

Here’s how to execute a data science project from end to end in more detail.

First, you define the business questions, requirements, and performance measurement. After that, you collect data to answer these questions. Then come the cleaning and preparation processes to get the data ready for exploration and analysis. These are the understanding stages.

But we’re not done yet.

Next comes the data preparation process. It involves the preprocessing and engineering of the features to prepare for the modeling step. Once that’s done, you can train the models on the prepared data. Depending on the task you are working on, you can do one of two things:

• Deploy the model on a live server and integrate it into a mobile or web application; then, monitor it and iterate again if needed, or
• Build dashboards based on the insights extracted from the data and the modeling step.

That wraps up the data science life cycle. Before you start working, you need some ideas for a data science project.

For starters, select a domain you are interested in. You can choose one that fits your educational background or previous work experience. This will give you a head start as you will know the field.

After that, you need to explore the common problems in this domain and how data science can solve them. Finally, choose a case study and formulate the business questions. Only then can you apply the life cycle we discussed above.

Now, let’s get started with a few project ideas.

The increasing cost of healthcare services is a major concern, especially for patients in the US. However, if planned properly, it can be reduced significantly.

The purpose of this project is to predict hospital charges before admitting a patient. Data science projects like this one are a great addition to your portfolio, especially if you want to pursue a career in healthcare .

Project Description

This will allow people to compare the costs at different medical institutions and plan their finances accordingly in case of elective admissions. It will also enable insurance companies to predict how much a patient with a particular medical condition might claim after a hospitalization.

You can solve this project using predictive analysis . This type of advanced analytics allows us to make predictions about future outcomes based on historical data. Typically, it involves statistical modeling, data mining, and machine learning techniques. In this case, we estimate hospital treatment costs based on the patient’s clinical data at admission.

Methodology

• Collect the hospital package pricing dataset
• Explore and understand the data
• Clean the data
• Perform engineering and preprocessing to prepare for the modeling step
• Select the suitable predictive model and train it with the data
• Deploy the model on a live server and integrate it into a web application to predict the pricing in real time
• Monitor the model in production and iterate

Expected Output

There are two expected outputs from this project:

• Analytical dashboard with insights extracted from the data that can be delivered to hospital and insurance companies
• Deployed predictive model into production on a live server that can be integrated into a web or mobile application and predict treatment costs in real time

Suggest Dataset:

• Package Pricing at Mission Hospital

Research Paper:

• Predicting the Inpatient Hospital Cost Using Machine Learning

This following example is form the marketing and finance domain .

Sentiment analysis or opinion mining refers to the analysis of the attitudes, feedback, and emotions users express on social media and other online platforms. It involves the detection of patterns in natural language that allude to people’s attitudes toward certain products or topics.

YouTube is the second most popular website in the world. Its comments section is a great source of user opinions on various topics. There are many examples of how you can approach such a data science project.

Let’s explore one of them.

You can analyze YouTube comments with natural language processing techniques. Begin by scraping text data using the library YouTube-Comment-Scraper-Python. It fetches comments utilizing browser automation.

Then, apply natural processing and text processing techniques to extract features, analyze them, and find the answers to the business questions you posed. You can build a dashboard to present the insights.

• Define the business questions you want to answer
• Build a web scrapper to collect data
• Clean the scraped data
• Text preprocessing to extract features
• Exploratory data analysis to extract insights from the data
• Build dashboards to present the insights interactively

Dashboards with insights from the scraped data.

Suggested Data

• Most Liked Comments on YouTube
• Analysis and Classification of User Comments on YouTube Videos
• Sentiment Analysis on YouTube Comments: A Brief Study

Marine life has a significant impact on our planet, providing food, oxygen, and biodiversity. Unfortunately, 90% of the large fish are gone primarily as a result of overfishing . In addition, many major fisheries notice increases in illegal fishing, undermining the efforts to conserve and manage fish stocks.

Detecting fishing activities in the ocean is a crucial step in achieving sustainability. It’s also an excellent big data project to add to your portfolio.

Identifying whether a vessel is fishing illegally and where this activity is likely to occur is a major step in ending illegal, unreported, and unregulated (IUU) fishing. However, monitoring the oceans is costly, time-consuming, and logistically difficult.

To overcome these challenges, we must improve the ability to detect and predict illegal fishing. This can be done using classification machine learning models to recognize and trace illegal fishing activity by collecting and processing GPS data from ships, as well as other pieces of information. The classification algorithm can distinguish these ships by type, fishing gear, and fishing behaviors.

• Collect the fishing watch dataset
• Perform data exploration to understand it better
• Perform engineering to extract features from the data
• Train classification models to categorize the fishing activity
• Deploy the trained model on a live server and integrate it into a web application
• Finish by monitoring the model in production and iterating

Deployed model running in a live server and used within a web service or mobile application to predict illegal fishing in real time.

Suggested Dataset

• Global Fishing Watch datasets

Research Papers

• Fishing Activity Detection from AIS Data Using Autoencoders
• Predicting Illegal Fishing on the Patagonia Shelf from Oceanographic Seascapes

The competition in the banking sector is increasing. To improve their services and retain and attract clients, banking and non-bank institutions need to modernize their marketing and customer strategies through personalization.

There are various data science models that could aid these efforts. Here, we focus on customer segmentation analysis .

Customer or market segmentation helps develop more effective investment and personalization strategies with the available information about clients. This is the process of grouping customers based on common characteristics, such as demographics or behaviors. This substantially improves targeting.

In this project, we segment Indian bank customers using data from more than one million transactions. We extract valuable information from these clusters and build dashboards with the insights. The final outputs can be used to improve products and marketing strategies.

• Define the questions you would like to answer with the data
• Collect the customer dataset
• Perform exploratory data analysis to have a better understanding of the data
• Perform feature preprocessing
• Train clustering models to segment the data into a selected number of groups
• Conduct cluster analysis to extract insights
• Build dashboards with the insights

Dashboards with marketing insights extracted from the segmented customers.

• A Customer Segmentation Approach in Commercial Banks

Dogecoin became one of the most popularity cryptocurrencies in recent years. Its price peaked in 2021, and it’s been slowly decreasing in 2022. That’s the case with most cryptocurrencies in the current economic situation.

However, the constant fluctuations make it hard for a human being to predict with accuracy the future prices. As such, automated algorithms are commonly used in finance .

This is an extremely valuable data science project for your resume if you want to pursue a career in this domain. If that’s your goal, you also need to learn how to use Python for Finance .

In this section, we discuss a time series forecasting project, commonly encountered in the financial sector .

A time series is a sequence of data points distributed over a time span. With forecasting, we can recognize patterns and predict future incidents based on historical trends. This type of data analytics projects can be conducted using several models, including ARIMA (autoregressive integrated moving average), regression algorithms, and long short-term memory (LSTM).

• Collect the historical price data of the Dogecoin cryptocurrency
• Manipulate and clean the data
• Explore the data to have a better understanding
• Train a deep learning model to predict the future change in prices
• Deploy the model on a live server to predict the changes in real time

Deployed model into production integrated into a cryptocurrency trading web or mobile application. You can also build a dashboard based on the data insights to help understand the dynamics of Dogecoin.

• Dogecoin Historical Price Data

Project Overview

Flawed products can result in substantial financial losses, so defect detection is crucial in manufacturing. Although human detection systems are still the traditional method employed, computer vision techniques are more effective.

In this example, we build a system to detect defects in metallic objects or surfaces during different phases of the production processes.

The types of defects can be aesthetic, such as stains, or potentially damaging the product’s functionality, such as notches, scratches, burns, lack of rectification, bumps, burrs, flatness, lack of thread, countersunk, rust, or cracks.

Since the appearance of metallic surfaces changes substantially with different lighting, defects are hard to detect even using computer vision. For this reason, lighting is a crucial component in solving such types of data science problems. Otherwise, the methodology of this project is standard.

• Collect the metal surface defects dataset
• Data cleaning and exploration
• Feature extraction
• Train models for defects detection and classification
• Deploy the model into production on an embedded system

A deployed model on an embedded system that can detect and classify metallic surface defects in different conditions and environments.

• Metal Surface Defects Dataset
• Online Metallic Surface Defect Detection Using Deep Learning

Data Science Project Ideas: Next Steps

Having diverse and complex data science projects in your portfolio is a great way to demonstrate your skills to future employers. You can choose one from the list above or use it as inspiration and come up with your own idea.

But first, make sure you have the necessary skills to solve these problems. If you want to start with something simpler, try the 365 Data Science Career Track . That way, you can build your foundational knowledge and gradually progress to more advanced topics. In the meantime, the instructors will guide you through the completion of real-life data science projects. Sign up and start your learning journey with a selection of free courses.

Youssef Hosni

Computer Vision Researcher / Data Scientist

Youssef is a computer vision researcher working towards his Ph.D. His research focuses on developing real-time computer vision algorithms for healthcare applications. He also worked as a data scientist, using customers' data to gain a better understanding of their behavior. Youssef is passionate about data and believes in AI's power to improve people's lives. He hopes to transfer his passion to others and guide them into this wide field through his writings.

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Research Topics & Ideas: Data Science

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Private Coaching service, the perfect starting point for developing a unique, well-justified research topic.

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⭐ ⭐ Use ML to classify flows and packets as benign or malicious. ⭐ ⭐

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mrsaeeddev / free-ai-resources

🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!

• Updated May 29, 2024

SUKHMAN-SINGH-1612 / Data-Science-Projects

Explore my diverse collection of projects showcasing machine learning, data analysis, and more. Organized by project, each directory contains code, datasets, documentation, and resources. Dive in, to discover insights and techniques in data science. Reach out for collaborations and feedback.

• Updated Sep 22, 2024
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asad70 / reddit-sentiment-analysis

This program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.

• Updated Mar 28, 2023

durgeshsamariya / Data-Science-Machine-Learning-Project-with-Source-Code

Data Science and Machine Learning projects with source code.

• Updated Jun 30, 2024

Alex-Lekov / AutoML_Alex

State-of-the art Automated Machine Learning python library for Tabular Data

• Updated Oct 4, 2023

juniorcl / transaction-fraud-detection

A data science project to predict whether a transaction is a fraud or not.

• Updated Sep 2, 2024

rodrigo-arenas / portfolio

Personal website, Data scientist portfolio template

• Updated Sep 21, 2024

drshahizan / special-topic-data-engineering

This course presents to the students recent research and industrial issues pertaining to data engineering, database systems and technologies. Various topics of interests that are directly or indirectly affecting or are being influenced by data engineering, database systems and technologies are explored and discussed.

• Updated Nov 5, 2024

imsanjoykb / Data-Science-Regular-Bootcamp

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

• Updated Jan 29, 2023

shsarv / Data-Analytics-Projects-in-python

A collection of data analysis and visualization projects designed to uncover insights from diverse datasets. These projects include analyses on COVID-19 trends, stock trading patterns, housing market prices, IoT data, and more, showcasing the power of data-driven storytelling.

• Updated Jan 7, 2024

AnshuTrivedi / Data-Scientist-In-Python

This repository contains notes and projects of Data scientist track from dataquest course work.

• Updated Dec 23, 2021

yusufcinarci / Data-Science-Projects

In this repo, there are (beginner-upper) level projects in the field of data science. I will host these projects that I have done in this field every day in this repo. With the hope that it will be useful to those who are interested in the field of data science like me and will just start...

• Updated Mar 9, 2024

ibrahim-Sobh / heart_stroke_prediction

Heart Strokes Predictions ML Model In Production

• Updated Jun 22, 2022

Amey-Thakur / OPTIMIZING-STOCK-TRADING-STRATEGY-WITH-REINFORCEMENT-LEARNING

https://youtu.be/Q82a93hjxJE

• Updated Mar 13, 2024

Shantanu-Gupta-au16 / Data-Science-Portfolio

Portfolio including my data science projects for academic, self-learning, and hobby.

• Updated Jul 20, 2020

inboxpraveen / Coursera_capstone

This repo contains Applied datascience capstone project offered by IBM through cousera.

• Updated Feb 8, 2019

MMBazel / Classifying-Sales-Calls

Turning salesforce lead, oppty, & sales activities data => Sales predictions using pandas, Scikit-learn, SQLAlchemy, Redshift, XGBoost Classifier

• Updated Feb 26, 2021

SHIRSENDU-KONER / Customer-Service-Request-Analysis

Customer Service Requests Analysis is one of the practical life problems that an analyst may face. This Project is one such take. The project is a beginner to intermediate level project. This repository has a Source Code, README file, Dataset, Image and License file.

• Updated Jan 7, 2022

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OLD PORTFOLIO <> <> <> My personal Website, where I share my blog and project. Build using Nextjs and Tailwind CSS

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Top 100 Data Science Project Ideas For Final Year

Are you a final year student diving into the world of data science, seeking inspiration for your final project? Look no further! In this blog, we’ll explore a variety of engaging and practical data science project ideas for final year that are perfect for showcasing your skills and creativity. Whether you’re interested in analyzing data trends, building machine learning models, or delving into natural language processing, we’ve got you covered. Let’s dive in!

What is Data Science?

Table of Contents

Data science is a multidisciplinary field that combines various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. At its core, data science involves the use of statistical analysis, machine learning, data mining, and data visualization to uncover patterns, trends, and correlations within datasets.

In simpler terms, data science is about turning raw data into actionable insights. It involves collecting, cleaning, and organizing data, analyzing it to identify meaningful patterns or relationships, and using those insights to make informed decisions or predictions.

Data science encompasses a wide range of applications across industries and domains, including but not limited to:

• Business: Analyzing customer behavior, optimizing marketing strategies, and improving operational efficiency.
• Healthcare: Predicting patient outcomes, diagnosing diseases, and personalized medicine.
• Finance: Fraud detection, risk management, and algorithmic trading.
• Technology: Natural language processing, image recognition, and recommendation systems.
• Environmental Science: Climate modeling, predicting natural disasters, and analyzing environmental data.

In summary, data science is a powerful discipline that leverages data-driven approaches to solve complex problems, drive innovation, and generate value in various fields and industries.

It plays a crucial role in today’s data-driven world, enabling organizations to make better decisions, improve processes, and create new opportunities for growth and development.

How to Select Data Science Project Ideas For Final Year?

Selecting the right data science project idea for your final year is crucial as it can shape your learning experience, showcase your skills to potential employers, and contribute to solving real-world problems. Here’s a step-by-step guide on how to select data science project ideas for your final year:

• Understand Your Interests and Strengths

Reflect on your interests within the field of data science. Are you passionate about healthcare, finance, social media, or environmental issues? Consider your strengths as well. 

Are you proficient in programming languages like Python or R? Do you have experience with statistical analysis, machine learning, or data visualization? Identifying your interests and strengths will help narrow down project ideas that align with your skills and passions.

• Consider the Impact

Think about the impact you want your project to have. Do you aim to address a specific problem or challenge in society, industry, or academia?

Consider the potential beneficiaries of your project and how it can contribute to positive change. Projects with a clear and measurable impact are often more compelling and rewarding.

• Assess Data Availability

Check the availability of relevant datasets for your project idea. Are there publicly available datasets that you can use for analysis? Can you collect data through web scraping, APIs, or surveys?

Ensure that the data you plan to work with is reliable, relevant, and adequately sized to support your analysis and modeling efforts.

• Define Clear Objectives

Clearly define the objectives of your project. What do you aim to accomplish? Are you exploring trends, building predictive models, or developing new algorithms?

Establishing clear objectives will guide your project’s scope, methodology, and evaluation criteria.

• Explore Project Feasibility

Evaluate the feasibility of your project idea given the resources and time constraints of your final year.

Consider factors such as data availability, computational requirements, and the complexity of the techniques you plan to use. Choose a project idea that is challenging yet achievable within your timeframe and resources.

• Seek Inspiration and Guidance

Look for inspiration from existing data science projects, research papers, and industry case studies. Attend workshops, conferences, or webinars related to data science to stay updated on emerging trends and technologies.

Seek guidance from your professors, mentors, or industry professionals who can provide valuable insights and feedback on your project ideas.

• Brainstorm and Refine

Brainstorm multiple project ideas and refine them based on feedback, feasibility, and alignment with your interests and goals.

Consider interdisciplinary approaches that combine data science with other fields such as healthcare, finance, or environmental science. Iterate on your ideas until you find one that excites you and meets the criteria outlined above.

• Plan for Iterative Development

Recognize that data science projects often involve iterative development and refinement.

Plan to iterate on your project as you gather new insights, experiment with different techniques, and incorporate feedback from stakeholders. Embrace the iterative process as an opportunity for continuous learning and improvement.

By following these steps, you can select a data science project idea for your final year that is engaging, impactful, and aligned with your interests and aspirations. Remember to stay curious, persistent, and open to exploring new ideas throughout your project journey.

Exploratory Data Analysis Projects

• Analysis of demographic trends using census data
• Social media sentiment analysis
• Customer segmentation for marketing strategies
• Stock market trend analysis
• Crime rates and patterns in urban areas

Machine Learning Projects

• Healthcare outcome prediction
• Fraud detection in financial transactions
• E-commerce recommendation systems
• Housing price prediction
• Sentiment analysis for product reviews

Natural Language Processing (NLP) Projects

• Text summarization for news articles
• Topic modeling for large text datasets
• Named Entity Recognition (NER) for extracting entities from text
• Social media comment sentiment analysis
• Language translation tools for multilingual communication

Big Data Projects

• IoT data analysis
• Real-time analytics for streaming data
• Recommendation systems using big data platforms
• Social network data analysis
• Predictive maintenance for industrial equipment

Data Visualization Projects

• Interactive COVID-19 dashboard
• Geographic information system (GIS) for spatial data analysis
• Network visualization for social media connections
• Time-series analysis for financial data
• Climate change data visualization

Healthcare Projects

• Disease outbreak prediction
• Patient readmission rate prediction
• Drug effectiveness analysis
• Medical image classification
• Electronic health record analysis

Finance Projects

• Stock price prediction
• Credit risk assessment
• Portfolio optimization
• Fraud detection in banking transactions
• Financial market trend analysis

Marketing Projects

• Customer churn prediction
• Market segmentation analysis
• Brand sentiment analysis
• Ad campaign optimization
• Social media influencer identification

E-commerce Projects

• Product recommendation systems
• Customer lifetime value prediction
• Market basket analysis
• Price elasticity modeling
• User behavior analysis

Education Projects

• Student performance prediction
• Dropout rate analysis
• Personalized learning recommendation systems
• Educational resource allocation optimization
• Student sentiment analysis

Environmental Projects

• Air quality prediction
• Climate change impact analysis
• Wildlife conservation modeling
• Water quality monitoring
• Renewable energy forecasting

Social Media Projects

• Trend detection
• Fake news detection
• Influencer identification
• Social network analysis
• Hashtag sentiment analysis

Retail Projects

• Inventory management optimization
• Demand forecasting
• Customer segmentation for targeted marketing
• Price optimization

Telecommunications Projects

• Network performance optimization
• Fraud detection
• Call volume forecasting
• Subscriber segmentation analysis

Supply Chain Projects

• Inventory optimization
• Supplier risk assessment
• Route optimization
• Supply chain network analysis

Automotive Projects

• Predictive maintenance for vehicles
• Traffic congestion prediction
• Vehicle defect detection
• Autonomous vehicle behavior analysis
• Fleet management optimization

Energy Projects

• Predictive maintenance for equipment
• Energy consumption forecasting
• Renewable energy optimization
• Grid stability analysis
• Demand response optimization

Agriculture Projects

• Crop yield prediction
• Pest detection
• Soil quality analysis
• Irrigation optimization
• Farm management systems

Human Resources Projects

• Employee churn prediction
• Performance appraisal analysis
• Diversity and inclusion analysis
• Recruitment optimization
• Employee sentiment analysis

Travel and Hospitality Projects

• Demand forecasting for hotel bookings
• Customer sentiment analysis for reviews
• Pricing strategy optimization
• Personalized travel recommendations
• Destination popularity prediction

Embarking on data science projects in their final year presents students with an excellent opportunity to apply their skills, gain practical experience, and make a tangible impact.

Whether it’s exploring demographic trends, building predictive models, or visualizing complex datasets, these projects offer a platform for innovation and learning.

By undertaking these data science project ideas for final year, final year students can hone their data science skills and prepare themselves for a successful career in this rapidly evolving field.

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Research Areas

Main navigation.

The world is being transformed by data and data-driven analysis is rapidly becoming an integral part of science and society. Stanford Data Science is a collaborative effort across many departments in all seven schools. We strive to unite existing data science research initiatives and create interdisciplinary collaborations, connecting the data science and related methodologists with disciplines that are being transformed by data science and computation.

Our work supports research in a variety of fields where incredible advances are being made through the facilitation of meaningful collaborations between domain researchers, with deep expertise in societal and fundamental research challenges, and methods researchers that are developing next-generation computational tools and techniques, including:

Data Science for Wildland Fire Research

In recent years, wildfire has gone from an infrequent and distant news item to a centerstage isssue spanning many consecutive weeks for urban and suburban communities. Frequent wildfires are changing everyday lives for California in numerous ways -- from public safety power shutoffs to hazardous air quality -- that seemed inconceivable as recently as 2015. Moreover, elevated wildfire risk in the western United States (and similar climates globally) is here to stay into the foreseeable future. There is a plethora of problems that need solutions in the wildland fire arena; many of them are well suited to a data-driven approach.

Seminar Series

Data Science for Physics

Astrophysicists and particle physicists at Stanford and at the SLAC National Accelerator Laboratory are deeply engaged in studying the Universe at both the largest and smallest scales, with state-of-the-art instrumentation at telescopes and accelerator facilities

Data Science for Economics

Many of the most pressing questions in empirical economics concern causal questions, such as the impact, both short and long run, of educational choices on labor market outcomes, and of economic policies on distributions of outcomes. This makes them conceptually quite different from the predictive type of questions that many of the recently developed methods in machine learning are primarily designed for.

Data Science for Education

Educational data spans K-12 school and district records, digital archives of instructional materials and gradebooks, as well as student responses on course surveys. Data science of actual classroom interaction is also of increasing interest and reality.

Data Science for Human Health

It is clear that data science will be a driving force in transitioning the world’s healthcare systems from reactive “sick-based” care to proactive, preventive care.

Data Science for Humanity

Our modern era is characterized by massive amounts of data documenting the behaviors of individuals, groups, organizations, cultures, and indeed entire societies. This wealth of data on modern humanity is accompanied by massive digitization of historical data, both textual and numeric, in the form of historic newspapers, literary and linguistic corpora, economic data, censuses, and other government data, gathered and preserved over centuries, and newly digitized, acquired, and provisioned by libraries, scholars, and commercial entities.

Data Science for Linguistics

The impact of data science on linguistics has been profound. All areas of the field depend on having a rich picture of the true range of variation, within dialects, across dialects, and among different languages. The subfield of corpus linguistics is arguably as old as the field itself and, with the advent of computers, gave rise to many core techniques in data science.

Data Science for Nature and Sustainability

Many key sustainability issues translate into decision and optimization problems and could greatly benefit from data-driven decision making tools. In fact, the impact of modern information technology has been highly uneven, mainly benefiting large firms in profitable sectors, with little or no benefit in terms of the environment. Our vision is that data-driven methods can — and should — play a key role in increasing the efficiency and effectiveness of the way we manage and allocate our natural resources.

Ethics and Data Science

With the emergence of new techniques of machine learning, and the possibility of using algorithms to perform tasks previously done by human beings, as well as to generate new knowledge, we again face a set of new ethical questions.

The Science of Data Science

The practice of data analysis has changed enormously. Data science needs to find new inferential paradigms that allow data exploration prior to the formulation of hypotheses.

37 Data Analytics Project Ideas and Datasets (2024 UPDATE)

Introduction.

Data analytics projects help you to build a portfolio and land interviews. It is not enough to just do a novel analytics project however, you will also have to market your project to ensure it gets found.

The first step for any data analytics project is to come up with a compelling problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the strongest categories for data analytics project ideas include:

• Beginner Analytics Projects  - For early-career data analysts, beginner projects help you practice new skills.
• Python Analytics Projects - Python allows you to scrape relevant data and perform analysis with pandas dataframes and SciPy libraries.
• Rental and Housing Data Analytics Projects - Housing data is readily available from public sources, or can be simple enough to create your own dataset. Housing is related to many other societal forces, and because we all need some form of it, the topic will always be of interest to many people.
• Sports and NBA Analytics Projects - Sports data can be easily scraped, and by using player and game stats you can analyze strategies and performance.
• Data Visualization Projects - Visualizations allow you to create graphs and charts to tell a story about the data.
• Music Analytics Projects - Contains datasets for music-related data and identifying music trends.
• Economics and Current Trends - From exploring GDPs of respective countries to the spread of the COVID-19 virus, these datasets will allow you to explore a wide variety of time-relevant data.
• Advanced Analytics Projects - For data analysts looking for a stack-filled project.

A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?

Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:

• Learn new tools and techniques.
• Work with complex datasets.
• Practice packaging your work and results.
• Prep for a case study and take-home interviews.
• Give you inbound interviews from hiring managers that have read your blog post!

Beginner Data Analytics Projects

Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.

1. Relax Predicting User Adoption Take-Home

This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.

Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.

How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.

2. Salary Analysis

Are you in some sort of slump, or do you find the other projects a tad too challenging? Here’s something that’s really easy; this is a salary dataset from Kaggle that is easy to read and clean, and yet still has many dimensions to interpret.

This salary dataset is a good candidate for descriptive analysis , and we can identify which demographics experience reduced or increased salaries. For example, we could explore the salary variations by gender, age, industry, and even years of prior work.

How you can do it: The first step is to grab the dataset from Kaggle. You can either use it as-is and use spreadsheet tools such as Excel to analyze the data, or you can load it into a local SQL server and design a database around the available data. You can then use visualization tools such as Tableau to visualize the data; either through Tableau MySQL Connector, or Tableau’s CSV import feature.

3. Skilledup Messy Product Data Analysis Take-Home

This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.

Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:

• Is there redundancy in the categorization?
• How can redundancy be identified and removed?
• Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?

How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.

4. Marketing Analytics Exploratory Data Analysis

This  marketing analytics dataset  on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:

• What factors are significantly related to the number of store purchases?
• Is there a significant relationship between the region the campaign is run in and that campaign’s success?
• How does the U.S. compare to the rest of the world in terms of total purchases?

How you can do it:  This  Kaggle Notebook from user Jennifer Crockett  is a good place to start, and includes quite a few visualizations and analyses.

If you want to take it a step further, there is quite a bit of statistical analysis you can perform as well.

5. UFO Sightings Data Analysis

The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a robust source for a beginner EDA project, and you can create insights into where sightings are reported most frequently sightings in the U.S. vs the rest of the world, and more.

How you can do it:  Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an  interactive map with sighting data .

6. Data Cleaning Practice

This  Kaggle Challenge asks you to clean data as well as perform a variety of data cleaning tasks. This is a perfect beginner data analytics project, which will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.

How you can do it:  You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.

Python Data Analytics Projects

Python is a powerful tool for data analysis projects. Whether you are web scraping data - on sites like the New York Times and Craigslist - or you’re conducting EDA on Uber trips, here are three Python data analytics project ideas to try:

7. Enigma Transforming CSV file Take-Home

This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.

As you work through this practice challenge, focus specifically on the grading criteria, which include:

• How well you solve the problems.
• The logic and approach you take to solving them.
• Your ability to produce, document, and comment on code.
• Ultimately, the ability to write clear and clean scripts for data preparation.

8. Wedding Crunchers

Todd W. Schneider’s  Wedding Crunchers  is a prime example of a data analysis project using Python. Todd  scraped wedding announcements  from the New York Times, performed analysis on the data, and found intriguing tidbits like:

• Distribution of common phrases.
• Average age trends of brides and grooms.
• Demographic trends.

Using the data and his analysis Schneider created a lot of cool visuals, like this one on Ivy League representation in the wedding announcements:

How you can do it:  Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.

9. Scraping Craigslist

Craigslist is a classic data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.

Riley Predum created a handy tutorial  that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some interesting analysis of pricing when segmented by neighborhood and price distributions. When graphed, his analysis looked like this:

How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.

10. Uber Trip Analysis

Here’s a cool project from Aman Kharwal: An  analysis of Uber trip data from NYC.  The project used this  Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.

Here’s a data visualization on pickup times by hour of the day from Aman:

How you can do it:  This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and  Uber datasets on data.world,  including one for Austin, TX.

11. Twitter Sentiment Analysis

Twitter (now X) is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.

One option would be to measure sentiment in your dataset over time like this:

How you can do it:  This tutorial from Natassha Selvaraj  provides step-by-step instructions to do sentiment analysis on Twitter data. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets.

12. Home Pricing Predictions

This project has been featured in our list of  Python data science projects . With this project, you can take the classic  California Census dataset , and use it to predict home prices by region, zip code, or details about the house.

Python can be used to produce some stunning visualizations, like this heat map of price by location.

How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python. Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.

13. Delivery Time Estimator

This take-home exercise - which requires 5-6 hours to complete - is a two-part task involving both machine learning model development and application engineering. You’re tasked with building a model to predict delivery times based on historical data, followed by writing an application to make predictions using this model. An exercise like this is excellent practice for the type of challenges that are typically given to machine learning engineers and data scientists.

As you work through this exercise, focus specifically on the evaluation criteria, which include:

• The performance of your model on the test data set.
• The feature engineering choices and data processing techniques you employ.
• The clarity and thoroughness of your explanations and write-up.
• Your ability to write modular, well-documented, and production-ready code for the prediction application.

14. Trucking in High Winds

This take-home exercise - which is intended to take 2-3 hours to complete - is focused on estimating the mean distance to failure for wind-induced rollover events on a specified route. You’re asked to analyze historical weather data to assess the frequency of high wind events and to use this information to estimate the risk of rollover incidents. A task like this is good practice for the type of data-driven safety analyses that are relevant to data science roles in the logistics and transportation industry.

Rental and Housing Data Analytics Project Ideas

There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.

If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:

15. Airbnb Data Analytics Take-Home Assignment

• Overview:  Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
• Time Required:  6 hours
• Skills Tested:  Analytics, EDA, growth marketing, data visualization
• Deliverable:  Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).

This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.

This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:

• Analytical approach and clarity of visualizations.
• Your data sense and decision-making, as well as the reproducibility of the analysis.
• Strength of your recommendations
• Your ability to communicate insights in your presentation.
• Your ability to follow directions.

16. Zillow Housing Prices

Check out  Zillow’s free datasets.  The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

Here’s an  analytics project based in R  that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:

How you can do it:  There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.

17. Inside Airbnb

On  Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. There is data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.

Agratama Arfiano has extensively examined Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really striking visualizations for this project, including the following:

How you can do it:  Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.

18. Car Rentals

Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:

How you can do it: Using the dataset, you could analyze rental cars by make and model, a particular location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets:  Cash for Clunkers cars ,  Carvana sales data or used cars on eBay .

19. Analyzing NYC Property Sales

This  real estate dataset  shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.

A beginner analytics project you can try with this data would be a missing values analysis project like:

How you can do it: There are a ton of  helpful Kaggle notebooks  you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.

Sports and NBA Data Analytics Projects

Sports data analytics projects are fun if you’re a fan, and also, because there are quite a few free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull a wide range of statistics and build your own unique dataset to investigate a problem.

20. NBA Data Analytics Project

Check out this  NBA data analytics project  from Jay at Interview Query. Jay analyzed data from  Basketball Reference  to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to an end-of-quarter strategy where a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the  source code on GitHub).

The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:

How you can do it: Read this tutorial on  scraping Basketball Reference data . You can analyze in-game statistics, career statistics, playoff performance, and much more. An idea could be to analyze a player’s high school ranking  vs. their success in the NBA. Or you could visualize a player’s career.

21. Olympic Medals Analysis

This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there is plenty of data to analyze, and it’s useful for identifying performance trends by country and sport. Here’s a visualization from Didem Erkan :

How you can do it: Check out the  Olympics medals dataset . Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g., how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.

22. Soccer Power Rankings

FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of  working with the SPI data .

How you can do it:  Check out the  SPI data . Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?

23. Home Field Advantage Analysis

Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from  Pro-Football-Reference.com . Then you can perform a simple linear regression model to measure the impact.

There are a ton of projects you can do with NFL data. One would be to  determine WR rankings, based on season performance .

How you can do it:  See this Github repository on performing a  linear regression to quantify home field advantage .

24. Daily Fantasy Sports

Creating a model to perform in daily fantasy sports requires you to:

• Predict which players will perform best based on matchups, locations, and other indicators.
• Build a roster based on a “salary cap” budget.
• Determine which players will have the top ROI during the given week.

If you’re interested in fantasy football, basketball, or baseball, this would be a strong project.

How you can do it: Check out the  Daily Fantasy Data Science course , if you want a step-by-step look.

Data Visualization Projects

All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!

25. Supercell Data Scientist Pre-Test

This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.

You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.

26. Visualizing Pollution

This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:

How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.

27. Visualizing Top Movies

There are a ton of movie and media datasets on Kaggle:  The Movie Database 5000 ,  Netflix Movies and TV Shows ,  Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for fantastic visualizations.

Take a look at this  visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:

How you can do it: Take a Kaggle movie dataset, and create a visualization that shows one of the following: gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.

28. Gender Pay Gap Analysis

Salary is a subject everyone is interested in, and it makes it a relevant subject for visualization. One idea: Take this dataset from the  U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.

You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:

How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or  data science salaries by the company .

29. Visualize Your Favorite Book

Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an  Italo Calvo book into cool visualizations . The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:

How you can do it: This  Shakespeare dataset , which features all of the lines from his plays, would be ripe for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script.

Music Analytics Projects

If you’re a music fan, music analytics projects are a good way to jumpstart your portfolio. Of course, analyzing music through digital signal processing is out of our scope, so the best way to go around music-related projects is through exploring trends and charts. Here are some resources that you may use.

30. Popular Music Analysis

Here’s one way to analyze music features without explicit feature extraction. This dataset from Kaggle contains a list of popular music from the 1960s. A feature of this dataset is that it is currently being maintained. Here are a few approaches you can use.

How you can do it: You can grab this dataset from Kaggle. This dataset has classifications for popularity, release date, album name, and even genre. You can also use pre-extracted features such as time signature, liveness, valence, acoustic-ness, and even tempo.

Load this dataset into a Pandas DataFrame and do your appropriate processes there. You can analyze how the features move over time (i.e., did songs over time get a bit more mellow, livelier, or louder), or you can even explore the rise and fall of artists over time.

31. KPOP Melon Music Charts Analysis

If you’re interested in creating a KPOP-related analytics project, here’s one for you. While this is not a dataset, what we have here is a data source that scrapes data from the Melon charts and shows you the top 100 songs in the weekly, daily, rising, monthly, and LIVE charts.

How you can do it: The problem with this data source is that it is scraped, so gathering previous data might be a bit problematic. In order to do historical analysis, you will need to compile and store the data yourself.

So for this approach, we will prefer a locally hosted infrastructure. Knowing how to use cloud services to automate and store data might introduce additional layers of complexity for you to show off to a recruiter. Here’s a local approach to conducting this project.

The first step is to decide which database solution to use. We recommend XAMPP’s toolkit with MySQL Server and PHPMyAdmin as it provides an easy-to-use frontend while also providing a query builder that allows you to construct table schemas, so learning DDL (Data Definition Language) is not as much of a necessity.

The second step is to create a Python script that scrapes data from Melon’s music charts. Thankfully, we have a module that scrapes data from the charts. First, install the melonapi module. Then, you can gather the data and store it in your database. Here’s a step-by-step guide to loading the data from the site.

Of course, running this script over a period of time manually opens the door to human forgetfulness or boredom. To avoid this, you can use an automation service to automate your processes. For Windows systems, you can use the built-in Windows Task Scheduler. If you’re using Mac, you can use Automator.

When you have the appropriate data, you can then perform analytics, such as examining how songs move over time, classifying songs by album, and so on.

Economic and Current Trends Analytics Projects

One of the most valuable analytics projects is those that delve into economic and current trends. These projects, which make use of data from financial market trends, public demographic data, and social media behavior, are powerful tools not only for businesses and policymakers but also for individuals who aim to better understand the world around them.

When discussing current trends, COVID-19 is a significant phenomenon that continues to profoundly impact the status quo. An in-depth analysis of COVID-19 datasets can provide valuable insights into public health, global economies, and societal behavior.

How you can do it: These datasets, readily available for download, focus on different geographical areas. Here are a few:

• EU COVID-19 Dataset - dataset from the European Centre for Disease Prevention and Control, contains COVID-19 data for EU territories.
• US COVID-19 Dataset - US COVID-19 data provided by the New York Times. However, data might be outdated.
• Mexico COVID-19 Dataset - A COVID-19 dataset provided by the Mexican government.

These datasets provide opportunities to develop predictive algorithms and to create visualizations depicting the virus’s spread over time. Despite COVID-19 being less deadly today, it has become more contagious , and insights derived from these datasets can be crucial for understanding and combating future pandemics. For instance, a time-series analysis could identify key periods of infection rates’ acceleration and slow-down, highlighting effective and ineffective public health measures.

32. News Media Dataset

The News Media Dataset provides valuable information about the top 43 English media channels on YouTube, including each of their top 50 videos. This dataset, although limited in its scope, can offer intriguing insights into viewer preferences and trends in news consumption.

How you can do it: Grab the dataset from Kaggle and use the dataset which contains the top 50 viewed videos per channel. There are a lot of insights you can gain here, such as using a basic sentiment analysis tool to determine whether the top-performing headlines were positive or negative.

For sentiment analysis, you don’t necessarily need to train a model. You can load the CSV file and loop through all the tags. Use the TextBlob module to conduct sentiment analysis. Here’s how you can go about doing it:

Then, by using the subjectivity and polarity metrics, you can create visualizations that reflect your findings.

33. The Big Mac Index Analytics

The Big Mac Index offers an intriguing approach to comparing purchasing power parity (PPP) between different countries. The index shows how the U.S. dollar compares to other currencies, through a standardized, identical product, the McDonald’s Big Mac. The dataset, provided by Andrii Samoshyn, contains a lot of missing data, offering a real-world exercise in data cleaning. The data goes back to April 2000 up until January 2020.

How you can do it: You can download the dataset from Kaggle here . One common strategy for handling missing data is by using measures of central tendency like mean or median to fill in gaps. More advanced techniques, such as regression imputation, could also be applicable depending on the nature of the missing data.

Using this cleaned dataset, you can compare values over time or between regions. Introducing a “geographical proximity” column could provide additional layers of analysis, allowing comparisons between neighboring countries. Machine Learning techniques like clustering or classification could reveal novel groupings or patterns within the data, providing a richer interpretation of global economic trends.

When conducting these analyses, it’s important to keep in mind methods for evaluating the effectiveness of your work. This might involve statistical tests for significance, accuracy measures for predictive models, or even visual inspection of plotted data to ensure trends and patterns have been accurately captured. Remember, any analytics project is incomplete without a robust method of evaluation.

34. Global Country Information Dataset

This dataset offers a wealth of information about various countries, encompassing factors such as population density, birth rate, land area, agricultural land, Consumer Price Index (CPI), Gross Domestic Product (GDP), and much more. This data provides ample opportunity for comprehensive analysis and correlation studies among different aspects of countries.

How you can do it : Download this dataset from Kaggle. This dataset includes diverse attributes, ranging from economic to geographic factors, creating an array of opportunities for analysis. Here are some project ideas:

• Correlation Analysis: Investigate the correlations between different attributes, such as GDP and education enrollment, population density and CO2 emissions, birth rate, and life expectancy. You can use libraries like pandas and seaborn in Python for these tasks.
• Geospatial Analysis: With latitude and longitude data available, you could visualize data on a world map to understand global patterns better. Libraries such as geopandas and folium can be helpful here.
• Predictive Modeling: Try to predict an attribute based on others. For instance, could you predict a country’s GDP based on factors like population, education enrollment, and CO2 emissions?
• Cluster Analysis: Group countries based on various features to identify patterns. Are there groups of countries with similar characteristics, and if so, why?

Remember to perform EDA before diving into modeling or advanced analysis, as this will help you understand your data better and could reveal insights or trends to explore further.

35. College Rankings and Tuition Costs Dataset

This dataset offers valuable information regarding various universities, including their rankings and tuition fees. It allows for a comprehensive analysis of the relationship between a university’s prestige, represented by its ranking, and its cost.

How you can do it: First, download the dataset from Kaggle . You can then use Python’s pandas for data handling, and matplotlib or seaborn for visualization.

Possible analyses include exploring the correlation between college rankings and tuition costs, comparing tuition costs of private versus public universities, and studying trends in tuition costs over time. For a more advanced task, try predicting college rankings based on tuition and other variables.

Advanced Data Analytics Project

Ready to take your data skills to the next level? Advanced projects are a way to do just that. They’re all about handling larger datasets, really digging into data cleaning and preprocessing, and getting your hands dirty with a range of tech stacks. It’s a two-in-one deal – you’ll dip your toes inside the roles of both a data engineer and a data scientist. Here are some project ideas to consider.

36. Analyzing Google Trends Data

Google Trends, a free service provided by Google, can serve as a treasure trove for data analysts, offering insights into popular trends worldwide. But there’s a hitch. Google Trends does not support any official API, making direct data acquisition a bit challenging. However, there’s a workaround — web scraping. This guide will walk you through the process of using a Python module for scraping Google Trends data.

How you can do it: Of course, we would not want to implement a web scraper ourselves. Simply put, it’s too much work. For this project, we will utilize a Python module that will help us scrape the data. Let’s view an example:

This code should print out the data in the following format:

You should use an automation service to automate scraping at least once per hour (see: KPOP Melon Music Charts Analysis) . Then, you should store the results in a CSV file that you can query later. There are many points of analysis, such as keyword rankings, website rankings for articles, and more.

Taking it a step further:

If you want to make an even more robust project that’s bound to wow your recruiters, here are some ideas to make the scraping process easier to maintain, albeit with a higher difficulty in setting up.

The first problem in our previous approach is the hardware issue. Simply put, the automation service we used earlier is moot if our device is off or if it was not instantiated during device startup. To solve this, we can utilize the cloud.

Using a function service (i.e., GCP Cloud Functions, AWS Lambda), you can execute Python scripts. Now, you will need to orchestrate this service, and you can use a Pub/Sub service such as GCP Pub/Sub and AWS SNS. These will alert your cloud functions to run, and you can modify the Pub/Sub service to run at a specified time gap.

Then, when your script successfully scrapes the data, you will need a SQL server instance. The flavor of SQL does not really matter, but you can use the available databases provided by your cloud provider. For example, AWS offers RDS, while GCP offers Cloud SQL.

Once your data is pulled together, you can then start analyzing your data and employing analysis techniques to visualize and interpret data.

37. New York Times (NYT) Movie Reviews Sentiment Analysis

Sentiment Analysis is a critical tool in gauging public opinion and emotional responses towards various subjects, and in this case, movies. With a substantial number of movie reviews published daily in well-circulated publications like the NYT, proper sentiment analysis can provide valuable insights into the perceived quality of films and their reception among critics.

How you can do it: As a data source, NYT has an API service that allows you to query their databases. Create an account at this link and enable the ‘Movie Reviews’ service. Then, using your API key, you can start querying using the following script:

The query looks up the titles and returns movie reviews matching those in the query. You can then use the review summaries to do sentiment analysis.

Other NY Times APIs you can explore include the Most Popular API , and the Top Stories API .

More Analytics Project Resources

If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.

You should also read our guide on the data analyst career path , how to become a data analyst without a degree , how to build a data science project from scratch and list of 30 data science project ideas .

You can also check out our blog for more resources like:

How to Get a Data Science Internship

How Hard Is It to Get a Google Internship?

Highest Paying Data Science Jobs

• Python For Data Analysis
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30+ Top Data Analytics Projects in 2024 [With Source Codes]

Are you an aspiring data analyst? Dive into 40+ FREE Data Analytics Projects packed with the hottest 2024 tech. Data Analytics Projects for beginners , final-year students , and experienced professionals to Master essential data analytical skills.

These top data analytics projects serve as a simple yet powerful gateway for beginners. Learn with free source code , mastering the art of data analytics. Make informed choices, reduce costs, and innovate for business success.

Building these data analytics projects helps you incorporate your theoretical knowledge with practical applications. These are the best data analytics projects for resumes , as they focus on real-world problems.

Let's understand the need to build data analytics projects, and how they can help you in building your career.

Why Build Data Analytics Projects?

There are many applications of data analytics, and building data analytics projects helps you learn these applications and build a strong fundamental understanding of the subject.

Apart from adding value to your resume, data science projects also help you in building skills and solve real-world problems. Some benefits of building data analytics projects:

• Smart Decisions: Data analytics helps you make smart choices by turning data into actionable insights.
• Identify Trends: It gives you an edge by spotting trends and opportunities before others.
• Cost Analysis: Identifies areas to cut costs and make operations more efficient.
• Customer Insights: Reveals customer habits and preferences for better service and loyalty.
• Business Growth: Pinpoints where and how your business can grow successfully.
• Risk Management: Helps in foreseeing and managing potential risks effectively.
• Performance Tracking: Keeps you updated on how well your business is doing in real time.
• Personalized Marketing: Allows tailored marketing for better customer engagement.
• Work Efficiency: Streamlines processes for overall operational efficiency.
• Innovation: Fosters a culture of innovation through data-driven insights.

Big Data Analytics Projects with Source Codes

We have shortlisted some of the big data analytics Projects and categorized them into 3 categories. You can choose a single category to build projects or multiple categories to diversify your knowledge of data analytics.

We have provided multiple data analytics projects in each category. Combined there are over 30 projects to choose from.

Let's look at these categories below, and the fun projects in them.

Data Analytics Project Categories

WebScraping Data Analytics Projects

Data analysis and visualization projects.

• Time Series Data Analysis Projects

Explore these top web scraping projects with source code.

• Movies Review Scraping And Analysis
• Product Price Scraping and Analysis
• News Scraping and Analysis
• Real-time Share Price scrapping and analysis

Here are the top Data Analysis and Visualization projects with source code.

• Zomato Data Analysis Using Python
• IPL Data Analysis
• Airbnb Data Analysis
• Global Covid-19 Data Analysis and Visualizations
• Housing Price Analysis & Predictions
• Market Basket Analysis
• Titanic Dataset Analysis and Survival Predictions
• Iris Flower Dataset Analysis and Predictions
• Customer Churn Analysis
• Car Price Prediction Analysis
• Indian Election Data Analysis
• HR Analytics to Track Employee Performance
• Product Recommendation Analysis
• Credit Card Approvals Analysis & Predictions
• Uber Trips Data Analysis
• iPhone Sales Analysis
• Google Search Analysis

Time Series Data Analytics Projects

Here are the top 10 Data Analytics Projects with source code based on Time Series

• Time Series Analysis with Stock Price Data
• Weather Data Analysis
• Time Series Analysis with Cryptocurrency Data
• Climate Change Data Analysis
• Anomaly Detection in Time Series Data
• Predictive Modeling for Sales or Demand Forecasting
• Air Quality Data Analysis and Dynamic Visualizations
• Gold Price Analysis and Forcasting Over Time
• Food Price Forecasting
• Time wise Unemployement Data Analysis

Now that you've decided on the project that you will be building, let's look at some platforms that will help you in building projects.

Best Platforms to Build Data Analyst Projects

Here are some best platforms for making data analysis projects:

• Microsoft Excel : Widely used for data manipulation and analysis, particularly suitable for beginners.
• Python ( Pandas and NumPy ): A versatile coding environment for advanced analytics and machine learning.
• RStudio : Ideal for statistical analysis, offering a comprehensive platform for data exploration.
• Tableau : Renowned for its data visualization capabilities, making complex datasets more accessible.
• Jupyter Notebooks : An interactive and collaborative environment, facilitating code execution and documentation.
• Google Colab : A cloud-based solution offering scalable computing resources for efficient data processing.
• Microsoft Azure : Another cloud-based option providing extensive computing power, especially beneficial for handling large datasets.

Choose a platform based on your project's specific needs, your familiarity with the tools, and the desired level of collaboration and visualization.

Also Explore:

Data Analyst Salary In India 2024 Data Scientist Salary in India 2024 Business Analyst Salary in India 2024: Fresher & Experienced

In conclusion, our collection of top data analytics projects offers a hands-on journey for beginners and experienced individuals into the realm of data analytics. With free source code on project problems, you can learn to master data analytics and begin your journey to be a data analyst.

These projects cover a variety of areas, from web scraping to predictive modeling, enabling you to understand and implement data analytics straightforwardly. Elevate your skills, dive into these projects, and unlock the potential of data analytics to drive your career forward.

Data Analytics Projects - FAQs

What is a data analytics project.

A data analytics project involves analyzing data to extract insights for informed decision-making, often addressing specific business challenges or questions.

What are the types of data analytics?

There are 4 Types of Data Analytics: Descriptive: Summarizes past data. Diagnostic : Examines why past events occurred. Predictive : Forecasts future trends. Prescriptive : Recommends actions based on analysis.

How do you build a data analytics project?

To build a data analytics project, you need to: Understand Problem Gather data Preprocess and clean data Analyze data Conclude findings

How do you present a data analytics project?

Share findings through clear visuals, like charts or graphs. Explain insights in simple language, emphasizing key takeaways for easy understanding.

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Social Value Research Project

Social value research project for community transport (ct) operators , empowering community transport with impactful data .

The Social Value Research Project is an initiative by the Community Transport Association (CTA) in partnership with Ealing Community Transport (ECT) to equip CT operators with a unique toolkit to measure and demonstrate their social impact. This project will generate valuable data to highlight the contributions of the CT sector and help organisations communicate their value to stakeholders, including funders and local and national governments. Participating organisations will receive tools, support, and insights to strengthen their position and attract greater investment in community transport.

What is the Social Value Toolkit?

  ECT Charity  has been hard at work on a completely revised version of their pioneering  CT Social Value Toolkit – Version 2.0 .   The original CT Social Value Toolkit has been helping CTs communicate the difference they make since its launch in 2018. 

The toolkit enables organisations to capture the Social Value created by CTs more accurately and completely than ever, ensuring it will add even more strength to our arguments that CT is essential to UK transport. New features of Version 2.0 include:

• National data  – all of the Social Value data gathered by CTs is aggregated. This means it can be used to inform national campaigns of the value that Community Transport brings
• Environmental impact  – the toolkit now fully accounts for the positive difference of travelling together by CT rather than individual car/taxi use, and the impact of electric vehicles
• Updated methods and values  - the calculations use the very latest methods and values based on recent research
• CTs and the economy  – the toolkit now takes better account of the impact CTs have on their local economy, both as employers and affordable transport providers
• The power of volunteering  – the toolkit now adds the benefits of wellbeing created by CT volunteering opportunities
• Urban-rural  – the toolkit now accounts for the real differences between these two settings, increasing accuracy.

Why Join the Social Value Research Project?

By joining this project, your organisation will:

• Demonstrate Impact: Use the Social Value Toolkit to showcase your contributions and present a compelling case for support and funding.
• Influence Stakeholders: Effectively communicate your social value to stakeholders and decision-makers, driving greater engagement.
• Gain Valuable Insights: Access resources and support to improve your data collection, analysis, and reporting capabilities.
• Be Part of a National Effort: Contribute to a UK-wide study shaping future support and policies for community transport.

Who Should Register?

We are inviting community transport organisations that:

• Are current members of CTA and will maintain membership throughout the project.
• Have operated in the UK, delivering services for at least one year.
• Already have some data collection processes in place or are ready to implement them.
• Operate as primary or secondary transport providers with a significant community transport component.

Selection Criteria: We are aiming for a representative sample across the UK, including England, Scotland, Wales, and Northern Ireland, as well as various income levels and operational sizes. 

Priority will be given to operators that align with these criteria.

What Will Be Required from Participating Organisations?

To ensure the success of this project and meaningful insights, participating organisations will be required to:

• Data Collection and Reporting: Commit to collecting and inputting at least 12 months of data into the Social Value Toolkit. The first data submission should be completed by the end of April 2025, with quarterly data returns afterward.
• Continuous Participation: Maintain active CTA membership and engage with the toolkit throughout the 12-month project period.

We encourage organisations to actively participate in:

• Engagement with Support Resources: Attend three webinars designed to enhance knowledge in data collection, analysis, and application. CTA and ECT will also provide technical support and one-on-one assistance to help you meet the project’s data requirements.

Project Benefits & Support

Participating organisations will receive:

• Financial Support: CTA and ECT are offering discounts on toolkit access, making this resource more affordable.
• Technical Assistance: Dedicated support from ECT to set up and use the toolkit.
• Training and Development: Access to three expert-led webinars focused on data collection, analysis, and leveraging insights.
• One-on-One Guidance: CTA and ECT staff will provide individualised assistance with data gathering and toolkit use.

Cost to CT Organisations

To make this initiative accessible, CTA and ECT are offering subsidised rates for the toolkit access based on your organisation’s income level:

Participating organisations can benefit from these reduced costs, making it easier to access the toolkit and maximise your impact data.

How to Register

• Confirm Eligibility: Review the criteria to ensure your organisation meets the project requirements.

Apply by Wed 18th December: Submit your application today, we aim to select the organisations by mid-December.

  complete the Application form

Please note that we may close this form early if there is high interest so apply soon.  

If you are in Wales or Northern Ireland please contact the relevant team to discuss opportunities

• Begin Participation: Following selection, CTA and ECT will provide onboarding materials, including project briefing, toolkit setup, and training sessions.

For questions about eligibility, requirements, or the application process, please contact Nick Mills, Research and Insight Manager.   [email protected]

For technical questions on the Social Value Toolkit please contact ECT directly on  [email protected].  You can also find out more about the toolkit  here .

Project Data Manager

• Location: United States
• Categories Clinical Data Management, Clinical Data Scientist Lead, Clinical Systems, Data Standards Consultant
• __vacancyopjusttionswidget.opt-Business Area__ ICON Strategic Solutions
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TA Business Partner

• Icon Strategic Solutions

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About the role.

ICON plc is a world-leading healthcare intelligence and clinical research organization. We’re proud to foster an inclusive environment driving innovation and excellence, and  we welcome you to join us on our mission to shape the future of clinical development as a Data Project Manager. You will Execute Data Management (DM) activities per set timelines with quality and consistency for a given product or multiple products.

What you will be doing:

• Ensuring clinical projects are executed according to set timelines with quality and consistency • Leading DM activities for a given product or multiple products • Ensuring that DM procedures and processes are adhered to by FSP staff through oversight of quality, cycle times, metrics and use of the Issue CAPA process • Co-ordination and mentoring of lead data managers within assigned projects Key Activities: • Training and mentoring of DM TA staff on processes, projects and programs • Lead or participate in the development, review and implementation of processes, policies, SOPs and associated documents affecting DM • Participate in and/or lead DM and cross functional working groups • Contribute to the continuous improvement of DM and the wider Development organization through information sharing, training and education • Contribute to development of DM outsourcing strategies and long-term relationships with CRO partners / external vendors • Oversight of FSP vendors with respect to quality, Issue & CAPA tracker & KPI metrics • Promote and be an advocate of DM internally and externally • Represent DM at project team meetings i.e., GCST • Project level coordination of and day to day oversight of DM tasks including: o Co-ordination of lead DM’s within the project o Review of all DM documents within a project area to ensure a consistent approach o Overview of project timelines and metrics to ensure databases are delivered to set timelines o Approve database locks and unlocks o Actively monitor progress of clinical projects within assigned product area to ensure delivery to set timelines and quality standards • Provide DM product level input to developing and managing resource plans and budgets for DM • Ensure that quality control checks are occurring such that quality databases are delivered • Develop and co-ordinate project level training for data management staff • Review and approve study specific training • Manage vendor deliverables and relationship at the project level • Communication and escalation of project level issues including processes, timelines, resourcing, performance, etc. • Review of all study level non DM documents for awareness and project level consistency • Lead electronic submission activities • Assist with response to questions and findings from Clinical Quality • Assurance (Quality Assurance) and other audits at the study / vendor level

Basic qualifications • Doctorate degree OR • Master’s degree & 3 years of clinical experience OR • Bachelor’s degree & 5 years of clinical experience OR • Associate’s degree & 10 years of clinical experience OR • High school diploma / GED & 12 years of clinical experience Preferred Qualifications • Bachelors degree or equivalent in life science, computer science, business administration or related discipline • 6+ years work experience in data management in the Pharmaceutical or Biotech arena • 3+ years project management and planning experience • Experience in oversight of outside vendors (CROs, central labs, imaging vendors, etc.)

What ICON can offer you: Our success depends on the quality of our people. That’s why we’ve made it a priority to build a diverse culture that rewards high performance and nurtures talent. In addition to your competitive salary, ICON offers a range of additional benefits. Our benefits are designed to be competitive within each country and are focused on well-being and work life balance opportunities for you and your family. Our benefits examples include:

• Various annual leave entitlements
• A range of health insurance offerings to suit you and your family’s needs
• Competitive retirement planning offerings to maximise savings and plan with confidence for the years ahead
• Global Employee Assistance Programme, TELUS Health, offering 24-hour access to a global network of over 80,000 independent specialised professionals who are there to support you and your family’s well-being
• Life assurance
• Flexible country-specific optional benefits, including childcare vouchers, bike purchase schemes, discounted gym memberships, subsidised travel passes, health assessments, among others

Visit our careers website to read more about the benefits of working at ICON: https://careers.iconplc.com/benefits ICON, including subsidiaries, is an equal opportunity and inclusive employer and is committed to providing a workplace free of discrimination and harassment. All qualified applicants will receive equal consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please let us know or submit a request here . Interested in the role, but unsure if you meet all of the requirements? We would encourage you to apply regardless – there’s every chance you’re exactly what we’re looking for here at ICON whether it is for this or other roles.

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New research project investigates potential link between professional football and MND

13 November 2024 News

New funding from the MND Association, My Name'5 Doddie Foundation and the Darby Rimmer MND Foundation will provide support towards an 18 month research project investigating a potential link between playing professional football and developing MND.  

The project will analyse death certificates from thousands of footballers in the UK and Italy to discover whether they had a higher risk of dying from MND than the general public.  

Professor Ammar Al-Chalabi of King’s College London and Dr Elisabetta Pupillo of the Mario Negri Institute for Pharmacological Research in Milan, Italy are leading the study.  

Large studies, better data

MND affects more than 5,000 adults in the UK at any time. To determine whether a specific activity increases your risk of developing MND, scientists must analyse a massive set of patient details. If they only looked at a small number of people, it might give biased results that make the risk seem greater or lesser than it really is. Studies of rare diseases can also have different conclusions depending on the statistical tests used to analyse data.  

A previous study of professional footballers suggested playing football professionally can increase your risk of MND sixfold, but other studies have shown exercise might not increase risk, or might even be protective.  

Death certificate analysis

Prof Al-Chalabi and Dr Pupillo have designed a study to ask the question again but with a bigger sample size. These replication studies are important for separating true scientific findings from statistical quirks that happen by chance. They have gathered data from footballers who played in Series A and B in Italy and from those who were part of the Professional Football Association in the UK.  

The data includes date of birth, position, team, and length of playing career. The researchers have already accessed the death certificates of Italian players in the cohort but, to date, have been reliant on data already available online to estimate causes of death for the 26,235 footballers who played in the UK.  

This new funding will enable the research team to access death certificates to allow for proper scientific analysis.  

Why is this study important?

Sport and exercise are important and reduce our risk of certain diseases. However, if certain sports have risks, players and authorities must be aware so that safer ways of playing can be explored. If analysis in this study indicates a potential link, this research could be used to help make recommendations for football governing bodies.

Prof Al-Chalabi said the analysis may find no potential link between MND and professional football or may even suggest that playing the sport is protective against disease. Even if a link is discovered, Prof Al-Chalabi is clear that playing sports and exercising remains a beneficial activity to your health overall.  

If we show it does increase the risk, even then we have to be very clear that the lifetime risk of MND is about one in 300. Any findings from this study are limited to professional footballers and not the general population. The risk of heart disease, stroke or cancer – which exercise protects against – is about one in three each. These are common and serious risks to your health; you need to exercise Prof Ammar Al-Chalabi, Professor of Neurology at King's College London

We are delighted to be supporting the work of Professor Al-Chalabi and Dr Pupillo, alongside My Name’5 Doddie Foundation and the Darby Rimmer MND Foundation. A potential link between sport or exercise and the risk of developing MND has long been debated, but while several studies have been carried out, the evidence for a link has not been conclusive.  This new investigation builds on previous studies, extending the research into larger populations, and will improve our understanding of the potential interaction between football and MND. However, it is still very clear exercise has a huge health benefit to the vast majority of people and this study in no way suggests that exercise should be avoided. The benefits of exercise far outweigh the possible risks. Dr Brian Dickie, Director of Research Development at the MND Association

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Facility for Rare Isotope Beams

At michigan state university, frib research team identifies flaw in physics models of massive stars and supernovae, an international team of researchers led by scientists from the facility for rare isotope beams (frib) at michigan state university (msu) uncovered evidence that astrophysics models of massive stars and supernovae are inconsistent with observational gamma-ray astronomy. the discovery came after the team used an innovative new experimental method to investigate uncertain nuclear properties of an unstable isotope. .

Artemis Spyrou , professor of physics at the Facility for Rare Isotope Beams (FRIB) and in the Michigan State University (MSU) Department of Physics and Astronomy, led an international research team to investigate iron-60, an unstable  isotope , by using a new experimental method. The team—which included  Sean Liddick , associate professor of chemistry at FRIB and in MSU’s Department of Chemistry and Experimental Nuclear Science Department head at FRIB, and 11 FRIB graduate students and postdoctoral researchers—published its findings in  Nature Communications .

Iron-60 interests astrophysicists because it originates inside massive stars and is ejected from supernovae across the galaxy. To investigate the isotope, Spyrou’s team conducted an experiment at the National Superconducting Cyclotron Laboratory (FRIB’s predecessor) using a novel method developed jointly with Ann-Cecilie Larsen, professor of nuclear and energy physics, and Magne Guttormsen, professor emeritus, both at the  University of Oslo in Norway. 

“The unique thing that we brought into this collaboration was that we combined our expertise in nuclear reactions, isotope beams, and  beta decay to learn about a reaction that we can’t measure directly,” Spyrou said. “For this paper, we sought to measure enough of the properties surrounding the reaction we were interested in so that we could constrain it better than before.” 

Models are essential for predicting rare astrophysical events

Iron-60 has a long half-life for an unstable isotope—more than 2 million years—so it leaves a lasting signature of the supernova from which it originated. Specifically, iron-60 emits gamma rays as it decays that scientists can measure and analyze for clues about the life cycle of stars and the mechanisms of their explosive deaths. Physicists rely on this data to create and improve astrophysical models.

“One of the overarching goals of nuclear science is to achieve a comprehensive, predictive model of a nucleus that will accurately describe the nuclear properties of any atomic system,” said Liddick, “but we just don’t have that yet. We have to experimentally measure these processes first.” Scientists need to produce these rare isotopes, observe them, and then compare their findings with the model’s prediction to check for accuracy. 

“To study these nuclei, we can’t just find them naturally on Earth,” said Spyrou. “We have to make them. And that is the specialty of FRIB—to get stable isotopes that we can find, accelerate them, fragment them, and then produce these exotic isotopes, which might only live for a few milliseconds, so we can study them.” To that end, Spyrou and her team devised an experiment that served two purposes: First, they aimed to constrain the  neutron -capture process that transforms the isotope iron-59 into iron-60; second, they wanted to use the resulting data to investigate long-standing discrepancies between supernova model predictions and the observed traces of these isotopes. 

New method enables better study of short-lived isotopes

While iron-60 has a relatively long half-life, its neighbor iron-59 is less stable and will decay with a half-life of 44 days. This makes the neutron capture on iron-59 especially challenging to measure in the laboratory since it decays away before reasonable measurements can be performed. To overcome this problem, the scientists developed their own indirect methods of constraining this reaction experimentally. 

Spyrou and Liddick worked closely with their colleagues at the University of Oslo to develop a new method for studying these highly unstable isotopes. The result, called the  beta-Oslo Method , is a variation of the Oslo Method first developed by project co-author Guttormsen at the Oslo Cyclotron Laboratory. Guttormsen’s approach uses a nuclear reaction to populate a nucleus so that researchers can measure its properties. Though it has proven over several decades to have many astrophysics and nuclear structure applications, it was only possible to apply to (near-) stable isotopes. By combining their expertise in detection, beta decay, and reactions, the researchers devised a way to populate a target nucleus using the process of beta decay itself rather than a reaction. This innovative approach produced the isotope they were looking for much more efficiently and provided a path to constraining neutron-capture reactions on short-lived  nuclei .

“The beta-Oslo method is still the only technique that can give us some of these constraints on very exotic nuclei that are far from stability,” said Spyrou.

Correcting the models will take time

After constraining these key uncertainties about the nuclear reaction network that produces iron-60, Spyrou’s team concluded that the likelihood of that reaction happening inside a massive star is higher than model predictions by as much as a factor of two. The researchers now believe that theoretical models of  supernovae are flawed, and that there are specific stellar properties that are still incorrectly represented. In their paper’s conclusion, the researchers stated, “The solution to the puzzle must come from the stellar modeling by, for example, reducing stellar rotation, assuming smaller explodability mass limits for massive stars, or modifying other stellar parameters.”

This discovery not only has far-reaching implications for the theoretical understanding of massive stars and the conditions inside them, but it also further demonstrated that the beta-Oslo Method will be a valuable tool for scientists moving forward. “This wouldn’t have worked without our project partners at the University of Oslo, who inspired Artemis and me when they presented the Oslo method at a 2014 seminar at MSU,” said Liddick. “We approached them that day with our question about using beta decay, and discussions took off from there. We’ve worked together ever since, and I have no doubt we will continue to collaborate long into the future.”

Sarah Waldrip is a freelance science writer.

Michigan State University (MSU) operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), with financial support from and furthering the mission of the DOE-SC Office of Nuclear Physics. Hosting the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.