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20 Data Analytics Projects for All Levels
After learning the fundamentals of data analytics, it is time to apply your skills by working on projects. Companies prefer recruiting students with multiple project experiences, and they are looking for employees who are good at data ingestion and cleaning, data manipulation, probability and statistics, predictive analytics, and reporting.
It is not about learning a new language or tools. It is all about understanding the data and extracting important information. You need to work on multiple projects to get better at understanding the data and producing reports for non-technical people.
This blog will cover data analytics projects for beginners, professionals, and final-year students. Furthermore, you will learn about end-to-end projects that involve all essential steps, from data importing to reporting.
If you're looking for projects that are more focused on artificial intelligence, check out our separate guide to some of the top AI projects you can start working on today.
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Why Choose Data Analytics Projects
Data analytics projects are crucial stepping stones for anyone looking to excel in today's data-centric world. Here’s why they are essential:
- Practical skill application: They offer hands-on experience, bridging the gap between theoretical knowledge and real-world practice.
- Industry versatility: Data analytics is vital across various sectors. Working on diverse projects broadens your understanding and adaptability.
- Critical thinking: These projects develop your ability to analyze complex issues, identify patterns, and create data-driven solutions.
- Technical proficiency: Engaging in projects hones your skills in key tools and languages, making you a more competent and versatile analyst.
- Effective communication: They teach you to translate complex data insights into understandable and actionable information, a skill highly valued in any professional setting.
- Career advancement: Completing projects enhances your portfolio, showcasing your abilities to potential employers and expanding your career opportunities.
In essence, data analytics projects not only sharpen your technical skills but also prepare you for the challenges and demands of the modern workplace.
Data Analytics Projects for Beginners
As a beginner, you need to focus on importing, cleaning, manipulating, and visualizing the data.
- Data Importing: learn to import the data using SQL, Python, R, or web scraping.
- Data Cleaning: use various Python and R libraries to clean and process the data.
- Data Manipulation: using various techniques to shape the dataset for data analysis and visualization.
- Data Visualization: display the data using plots and graphs.
Data Importing and Cleaning Projects
1. Exploring the NYC Airbnb Market
In the Exploring the NYC Airbnb Market project, you will apply data importing and cleaning skills to analyze the Airbnb market in New york. You will ingest and combine the data from multiple file types, and clean strings and format dates to extract accurate information.
Image by Author | Code from the project
The project is perfect for beginners who want to get data importing and cleaning experience. You can apply similar methods to this Online Ticket Sales dataset to get even better at handling and processing the data.
Learn more about data importing and cleaning by taking short courses:
2. Word Frequency in Classic Novels
In the Word Frequency in Classic Novels project, you will use requests
and BeautifulSoup
to scrape a novel from the Project Gutenberg website. After scraping and cleaning the text data, you will use NLP to find the most frequent words in Moby Dick. The project introduces you to the world of Python web scraping and natural language processing.
Image by Author | Code from the project
For data analysts and data scientists, web scraping is an essential skill to learn. You can take a short Web Scraping with Python course to understand the tools and components of an HTML web page.
Master NLP in Python Today
Data Manipulation Projects
3. Exploring the Bitcoin Cryptocurrency Market
In the Exploring the Bitcoin Cryptocurrency Market project, you will explore bitcoin and other cryptocurrency data. You will clean the dataset by discarding cryptocurrencies without market capitalization, comparing Bitcoin with other currencies, and preparing data for visualization.
Image by Author | Code from the project
You can apply similar methods to Stock Exchange Data and learn to manipulate the data for data analysis. Furthermore, you can learn data transformation, aggregation, slicing, and indexing by taking Data Manipulation with pandas course.
4. Visualizing the History of Nobel Prize Winners
In the Visualizing the History of Nobel Prize Winners project, you'll examine over a century of Nobel Prize history. Using Python, you'll analyze and visualize data to uncover patterns and potential biases in how prestigious honors are awarded across categories like physics, chemistry, literature, and peace.
You'll apply data manipulation techniques with pandas and craft compelling visualizations with Seaborn to tell a story with the data. This project is perfect for enhancing your data analysis and visualization skills while exploring one of the world's most famous accolades.
Data Visualization Projects
5. Visualizing COVID-19
In the Visualizing COVID-19 project, you will visualize COVID-19 data using the most popular R library ggplot
. You will analyze confirmed cases worldwide, compare China with other countries, learn to annotate the graph, and add a logarithmic scale. The project will teach you skills that are in high demand for R programmers.
Image from the project
You can apply ggplot methods to Measles Data and gain more experience in data visualization and analysis. Moreover, you can take Intermediate Data Visualization with the ggplot2 course to learn the best data visualization practices.
6. Analyzing Super Bowl Viewership and Advertising
In the Analyzing Super Bowl Viewership and Advertising project, you’ll explore the drama behind the Super Bowl—from the games and advertisements to the halftime shows. Using R, you’ll manipulate and visualize data to uncover how these elements interact with one another. Perfect for building your skills in data analysis with tools like ggplot2 and dplyr.
Using the code to display interactive data visualization is easy, but understanding and interpreting the data is hard. Take the Understanding Data Visualization course to explain visualization distribution and learn the best data visualization techniques to communicate complex data.
Advanced Data Analytics Projects
For more advanced data analytics projects, you need command over mathematics, probability, and statistics. Furthermore, you will perform exploratory data and predictive analytics to understand the data in detail.
- Probability & Statistics: perform mean, median, standard deviation, probability distribution algorithms, and correlation on the data.
- Exploratory Data Analysis: explore the data distribution, understand various types of columns, and understand trends and patterns.
- Predictive Analytics: perform regression, classification, clustering, and forecasting using machine learning algorithms.
Probability & Statistics Projects
7. Modeling Car Insurance Claim Outcomes
In the Modeling Car Insurance Claim Outcomes project, you’ll use Python and logistic regression to predict insurance claims. Working with data from On the Road car insurance, you’ll identify key features that lead to the most accurate predictions. This project will help you apply machine learning techniques to real-world business problems in the insurance industry.
8. Hypothesis Testing with Men's and Women's Soccer Matches
In the Hypothesis Testing with Men's and Women's Soccer Matches project, you’ll analyze historical soccer data to test if women’s international matches result in more goals than men’s. With Python, you’ll sharpen your statistical testing skills and uncover patterns in global soccer trends.
If you are interested in learning about the most common statistical techniques, probability, data distribution, correlation, and experimental design, take the Introduction to Statistics in Python course.
Exploratory Data Analysis (EDA) Projects
9. Analyze International Debt Statistics
In the Analyze International Debt Statistics project, you will write SQL queries to explore and analyze international debt using the World Bank dataset. SQL is the most popular and essential tool for performing data analytics on the go.
In the project, you will be finding the:
- Distinct countries
- Distinct debt indicators
- Total amount of debt owed by the countries
- Country with the highest debt
- Average amount of debt across indicators
- The highest amount of principal repayments
- The most common debt indicator
Image by Author | Code from the project
You will connect World Nations MariaDB dataset and apply similar queries to get additional experience in handling and analyzing SQL databases. Additionally, you can Exploratory Data Analysis in SQL course to advance techniques and queries in handling various SQL databases.
10. Investigating Netflix Movies and Guest Stars in The Office
In the Investigating Netflix Movies and Guest Stars in The Office project, you will use data manipulation and visualization to solve a real-world data science problem. You will perform deep exploratory data analysis and draw conclusions from detailed graphs.
Image from the project
You can work on a portfolio project by applying similar skills to a new dataset: Netflix Movie Data. Furthermore, you can take Exploratory Data Analysis in Python to learn more about data cleaning and validation, understand the relationship and distribution, and explore multivariate relationships.
Predictive Analytics Projects
11. Will This Customer Purchase Your Product?
In the Will This Customer Purchase Your Product? project, you’ll analyze customer shopping behaviors using statistics and probability techniques. With Python, you’ll uncover insights into the differences between new and returning customers, helping marketing teams better understand engagement on e-commerce platforms.
12. Predicting Credit Card Approvals
In the Predicting Credit Card Approvals project, you will build the best-performing machine learning model for predicting credit card application approvals.
First, you will understand the data and impute missing values. After that, you will preprocess the data and train a logistic regression model on the training set. In the end, you will evaluate the results and improve the model performance using Grid searching.
Image by Author | Code from the project
Applying simple machine learning algorithms is an essential part of a data analyst’s life. You can gain more experience by applying similar methods to a new dataset: Bank Marketing.
Learn more about classification, regression, fine-tuning, and preprocessing by taking a short Supervised Learning with the scikit-learn course.
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Data Analytics Projects for Final Year Students
Final year student projects are usually research-based and require at least 2-3 months to complete. You will be working on a specific topic and trying to improve the results using various statistical and probability techniques.
Note: there is a growing trend for machine learning projects for data analytics final-year projects.
13. Reducing Traffic Mortality in the USA
For the Reducing Traffic Mortality in the USA project, you will find a good strategy for reducing traffic-related deaths in the USA. You will be importing, cleaning, manipulating, and visualizing the data. Furthermore, you will perform feature engineering and apply various machine learning models (multivariate linear regression, KMeans clustering) to come up with stately and communicate the results.
Image from the project
If you want to learn more about unsupervised learning, check out Cluster Analysis in Python course.
14. Assessing the Effectiveness of Medical Treatments
In the Assessing the Effectiveness of Medical Treatments project, you’ll explore the fascinating case of Simpson’s Paradox in a kidney stone treatment study. Using R, you’ll apply regression analysis to uncover hidden insights and better understand how outcomes vary across patient groups.
15. World Population Analysis
The World Population Analysis project is the best example of performing deep exploratory analysis. You will be exploring various columns, visualizing the least and most populated countries, and exploring population density and growth rate. Furthermore, you will display the country rank distribution and correlation map.
Image from the project
Learn easy ways to plot data visualization in Python by completing Intermediate Data Visualization with Seaborn course.
16. Data Science and MLOps Landscape in Industry
The Data Science and MLOps Landscape in Industry project is a holy grail for all data manipulation, visualizations, and exploratory and geospatial analysis. You will learn to effectively use box plots, doughnut charts, bar charts, heatmaps, parallel categorical graphs, bubble charts, funnel charts, radar charts, icicle charts, and maps. Furthermore, you will learn to interpret various types of graphs.
Image from the project
Take Introduction to Data Visualization with Plotly in Python course to learn about advanced Plotly features and customization.
End-to-end Data Analytics Projects
End-to-end projects are great for your resume and understanding of the data analytic project life cycle.
In general, you will be:
- Dealing with multiple datasets
- Understanding the data distribution
- Applying data cleaning and manipulation
- Applying probability and statical techniques
- Performing data analysis and visualization
- Using machine learning model for predictive analysis
- Creating the report or dashboard
17. Analyzing Unicorn Companies
In the Analyzing Unicorn Companies project, you’ll use SQL to explore unicorn companies valued at over $1 billion. You’ll analyze which industries have the highest valuations and identify emerging trends, such as the yearly growth of new unicorns between 2019 and 2021.
18. Monitoring a Financial Fraud Detection Model
In the Monitoring a Financial Fraud Detection Model project, you’ll take on the role of a post-deployment data scientist for a major UK bank. Using Python, you’ll monitor the performance of a fraud detection model and investigate why it may not be working as expected, ensuring the safety of customers’ finances.
19. An End-to-End Project on Time Series Analysis and Forecasting with Python
In the Time Series Analysis and Forecasting project, you will dive deep into analyzing the trends, apply the ARIMA model for forecasting, compare the results, and visualize the results to understand the sales for both furniture and office supplies.
Time-series analysis and forecasting projects are in high demand in financial sectors, and they will help you land a high-paying job. The only thing you need to do is to interpret various trends and accurately forecast the numbers.
Note: financial analysis and forecasting is a high-paying job, but it is the hardest job too.
Image from the project
If you are struggling to analyze and forecast, try completing ARIMA Models in Python course to learn about ARMA models, fitting the future, selecting the best models, and training seasonal ARIMA models.
20. Build a Multi-Objective Recommender System
The goal of Build a multi-objective recommender system project is to predict e-commerce clicks, cart additions, and orders. In short, you will be creating a multi-objective recommender system based on previous events in a user session.
Upon completing the project, you will master:
- Data manipulation and analysis
- Understand sessions and events
- Data visualization and reporting
- Handling time series data
- Analyze time series data to explore user behavior
- Predict top clicks, carts, and orders
Image from the project
Supporting Your Team's Growth with DataCamp for Business
While individual projects are essential for personal skill development, organizations also need to ensure their teams are well-equipped to handle the complexities of data analytics. DataCamp for Business offers tailored solutions that help companies upskill their employees in data science, analytics, and machine learning. With access to a vast library of interactive courses, custom learning tracks, and real-world projects, teams can advance their skills in data ingestion, cleaning, manipulation, visualization, and predictive analytics—all key areas highlighted in this blog.
Whether you're a small startup or a large enterprise, DataCamp for Business provides the tools to upskill, reskill, and create a data-driven culture to stay competitive in today's market. You can request a demo today to learn more.
Conclusion
After learning essential skills, you need to build a strong portfolio to showcase your knowledge. Furthermore, you will be learning new tools, features, and concepts that are beneficial for your professional life.
In this post, we have learned about beginner-friendly projects, advanced projects, final-year student projects, and end-to-end data analytics projects. Moreover, we have covered projects on data ingestion and cleaning, probability and statistics, data manipulation and visualization, and exploratory data and predictive analysis.
So, what’s next? After completing at least 12 projects, try to Get certified as a Professional Data Analyst. It will increase your odds of getting hired. You can also check out our post on how to become a data analyst for more career tips.
Become an ML Scientist

As a certified data scientist, I am passionate about leveraging cutting-edge technology to create innovative machine learning applications. With a strong background in speech recognition, data analysis and reporting, MLOps, conversational AI, and NLP, I have honed my skills in developing intelligent systems that can make a real impact. In addition to my technical expertise, I am also a skilled communicator with a talent for distilling complex concepts into clear and concise language. As a result, I have become a sought-after blogger on data science, sharing my insights and experiences with a growing community of fellow data professionals. Currently, I am focusing on content creation and editing, working with large language models to develop powerful and engaging content that can help businesses and individuals alike make the most of their data.
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