When you start learning data analytics, one of the best ways to improve your skills is by working on data analytics projects. These projects help you to get practical experience and also help you to understand how you can handle real-world datasets. Working on these projects is a powerful way to show your skills, both as a beginner and an advanced learner.
In this blog, we will discuss the different data analytics that will help you strengthen your data analytics skills. By the end of this blog, you will get a clear understanding of what projects you should work on and add to your portfolio.
Table of Contents
Beginner Data Analytics Projects
In this section, we will discuss the best data analytics projects for beginners.
1. Salary Analysis Project
It is one of the most important data analytics projects for beginners. You can build this project to study how salaries vary based on different job roles, years of experience, education, location, and other reasons. It helps you to see the factors that affect salaries and provides clear insights into pay trends, fairness, and salary predictions.
Skills required: The skills required for this project are Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Data Cleaning, Data Visualization, and Machine Learning.
Time taken: 1-2 weeks
Source code: https://github.com/AdilShamim8/Data-Science-Salary-Predictor
2. Word Frequency in Classic Novels
It is one of the most interesting data analytics projects. You can build this project to analyze the words that are used in the famous novels, and find out which words are used the most often.
Skills required: Python, BeautifulSoup, NLTK (or any similar NLP library), Text Processing, Data Cleaning, and Data Visualization.
Time taken: 3-5 days
Source Code: https://github.com/brk-cn/word-frequency-in-classic-novels
3. Titanic Dataset Survival Prediction
It is one of the most popular data analytics projects for beginners. You can build this project to predict the chances of survival of passengers on the Titanic. It depends on various factors like age, gender, class, family members, and ticket details.
Skills required: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Data Cleaning, Data Visualization, and Machine Learning ( for classification).
Time taken: 1 week
Source code: https://github.com/Esai-Keshav/titanic-survival-prediction
4. Data Cleaning & Preprocessing Project
It is one of the essential data analytics projects for beginners. You can build this project so that you can practice handling datasets by removing duplicates, filling the missing values, correcting data types, and standardizing formats. It also helps you to improve the quality of data and prepare the dataset for accurate analysis and modeling.
Skills required: Python, Pandas, Data Wrangling, Data Cleaning, Feature Engineering, and Exploratory Data Analysis.
Time taken: 3-5 days
Source code: https://github.com/venkat-0706/Sugarcane-Production
5. Customer Churn Analysis
It is one of the most valuable data analytics projects for students and professionals. You can build this project to analyze the behavior of customers and predict whether a customer will stop using a company’s service or not. It also helps you to understand the key factors behind churn, improve the retention strategies of customers, and boost the growth of businesses.
Skills required: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Data Cleaning, Data Visualization, and Machine Learning (for classification & prediction).
Time taken: 1-2 weeks
Source code: https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction
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6. Movie Review Sentiment Analysis
It is one of the most interesting data analytics projects for beginners. This project will help you analyze movie reviews and classify them as positive or negative by using various NLP techniques. It helps you to learn how to work with text data, detect the emotions behind the reviews, and use those insights to understand movies better.
Skills required: Python, Pandas, NumPy, NLTK, Scikit-learn, Data Cleaning, Text Preprocessing, Data Visualization, and Machine Learning.
Time taken: 1-2 weeks
Source code: https://github.com/SkyThonk/Movie-Reviews-Sentiment-Analysis
7. News Data Analysis
It is one of the most insightful data analytics projects for beginners and students. You can build this project to collect news articles and analyze trends, topics, or sentiments over time. It also helps you to understand how information spreads, identify popular objects, and make data-driven solutions from news content.
Skills required: Python, Pandas, NumPy, BeautifulSoup (or requests), NLTK, Data Cleaning, Data Visualization, and Text Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/Decodo/Google-News-scraper
8. Olympic Medals Data Analysis
It is one of the most engaging data analytics projects for beginners and students. You can build this project to study the data of Olympic medals across multiple countries, years, and sports so that you can identify trends and top-performing nations. It also helps you to understand the patterns in performance, compare achievements, and gain insights from historical sports data.
Skills required: Python, Pandas, NumPy, Seaborn, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time taken: 3-5 days
Source code: https://github.com/rajatrawal/olympic-data-analysis
In this section, we are going to discuss the best intermediate data analytics projects:
9. Uber Trip Data Analysis
Uber Trip Data Analysis is one of the most valuable data analytics projects for intermediate learners. You can build this project to analyze your Uber Trip in depth. It includes peak traveling hours, popularity of routes, rider demographics, and revenue patterns. It also helps you to understand travel patterns in a better way, improve ride operation, and use more advanced analytics to get meaningful insights.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, SQL, Data Cleaning, Data Visualization, Exploratory Data Analysis, and Feature Engineering.
Time taken: 1-2 weeks
Source code: https://github.com/Geo-y20/Uber-Rides-Data-Analysis
Twitter Sentiment Analysis is one of the most powerful data analytics projects for intermediate learners. With the help of this project, you can analyze tweets and determine whether they are positive, negative, or neutral using various NLP techniques. It also helps you understand public opinion, track trends, and apply more advanced text analytics methods.
Skills required: Python, Pandas, NumPy, Tweepy (or Twitter API), NLTK, Scikit-learn, Data Cleaning, Text Preprocessing, Data Visualization, and Machine Learning.
Time taken: 1-2 weeks
Source code:https://github.com/Chulong-Li/Real-time-Sentiment-Tracking-on-Twitter-for-Brand-Improvement-and-Trend-Recognition
11. House Price Prediction
House Price Prediction is one of the practical data analytics projects for intermediate learners. You can build this project to predict the price of houses depending on features like location, size, number of bedrooms, and other property attributes. It also helps you to understand regression modeling, identify key factors that are affecting the prices, and apply more advanced data analysis techniques.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Data Cleaning, Feature Engineering, Regression Analysis, and Data Visualization.
Time taken: 1-2 weeks
Source code: https://github.com/Viveckh/LilHomie
12. iPhone Sales Data Analysis
It is one of the most insightful data analytics projects for intermediate learners. With the help of this project, you can analyze the sales data of iPhones over time, across different regions. It also helps you to understand the performance in sales and customer preferences, and apply more advanced techniques to make data-driven business decisions.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Data Visualization, Exploratory Data Analysis, and SQL.
Time taken: 1-2 weeks
Source code: https://github.com/Guermoud98/iPhone-Sales-Analysis-Project
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13. Zomato Restaurant Data Analysis
Zomato Restaurant Data Analysis is one of the engaging data analytics projects for intermediate learners. This project will help you analyze Zomato restaurant data to find patterns in types of cuisines, ratings, locations, and pricing. It also helps you to understand the preferences of customers, identify trends in the food industry, and apply more advanced analytics techniques to get meaningful insights.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Data Visualization, Exploratory Data Analysis, and SQL
Time taken: 1-2 weeks
Source code: https://github.com/chiragsamal/Zomato
14. Product Price Tracking & Analysis
It is one of the most practical data analytics projects for intermediate learners. You can build this project to track the prices of products from e-commerce websites and analyze how they change over time across different platforms. It also helps you to understand the pricing strategies, identify market trends, and apply advanced analytics to support smarter decisions.
Skills required: Python, Pandas, NumPy, BeautifulSoup, Matplotlib, Seaborn, Data Cleaning, Data Visualization, SQL, and Exploratory Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/AgentOps-AI/tokencost
15. Indian Election Data Analytics
It is one of the most insightful data analytics projects. With the help of this project, you can analyze the data of the Indian election, the performance of a particular party, candidate details, and regional trends. It also helps you to understand the voting patterns, compare historical results, and apply analytics to cover meaningful political insights.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Data Visualization, Exploratory Data Analysis, and SQL.
Time taken: 1-2 weeks
Source code: https://github.com/datameet/india-election-data
16. IPL Data Analysis
It is one of the most popular data analytics projects for intermediate learners. You can build this project to analyze the data of IPL, study the performance of each player, team statistics, outcome of matches, and season trends. It also helps you to understand sports analytics, identify winning patterns, and apply advanced techniques to generate valuable insights.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Data Visualization, Exploratory Data Analysis, and SQL.
Time taken: 1-2 weeks
Source code: https://github.com/bprasad26/ipl_data_analysis
Data Visualization Projects
In this section, we are going to discuss the best data visualization projects:
17. Visualizing Covid-19 Trends
It is one of the most important data analytics projects under the data visualization category. With this project, you can visualize COVID-19 data, including daily cases, recoveries, deaths, and the progress of vaccinations across regions. It helps you to understand the trends of the pandemic, compare regions, and present critical information with the help of interactive dashboards and charts.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau/Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/ArunavaKumar/Covid-19-Trend-Analysis-and-Time-Series-Forecasting
18. Pollution Levels & Air Quality Visualization
It is one of the most impactful data visualization projects. You can build this project to visualize the data of air quality, including the pollution levels (PM2.5, PM10, CO2, NO2, etc.) across different cities and time periods. It also helps you to understand the pollution patterns, compare regions, and provide insights through interactive charts and dashboards.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau/Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/Ozon3Org/Ozon3
19. Gender Pay Gap Visualization
It is one of the most important data analytics projects under the category of data visualization. This project helps you to analyze and visualize the differences in salary between genders across industries, job roles, and regions. It also helps you to highlight disparities, identify patterns, and present findings with the help of visual reports and dashboards that are easy to understand.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau/Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time-taken: 1-2 weeks
Source code: https://github.com/034adarsh/Gender-Pay-Gap-Analysis-And-Prediction
20. Nobel Prize Winners Data Analysis
It is one of the most insightful data analytics projects under the category of data visualization. You can use this project to analyze Nobel Prize winners across different years, categories, countries, and demographics. It also helps you to uncover patterns, such as which countries dominate in certain fields, how diversity has evolved, and shows the findings in interactive charts and dashboards.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau, Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/nafisalawalidris/Analyzing-Nobel-Prize-Dataset-Demographics-and-Trends
21. Visualizing Global Movie Trends
It is one of the most exciting data visualization projects. This project will help you to study how movies have changed over the years based on the genres, budget, revenues, ratings, and preferences of global audiences. It also helps you to see which genres are popular in different regions, how the film industry has evolved, and provides insights through interactive charts and dashboards.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau/Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time Taken: 1-2 weeks
Source code: https://github.com/280220/Netflix-Trend-Analysis
22. Super Bowl Advertising and Viewership Analysis
It is one of the most engaging data visualization projects that helps you to study the relationship between Super Bowl commercials and audience engagement. You can also analyze the advertising costs, brand strategies, viewership ratings, and social media buzz over the years.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Tableau/Power BI, Data Cleaning, Data Visualization, and Exploratory Data Analysis.
Time taken: 1-2 weeks
Source code: The source code for this project is given below:
https://github.com/akthammomani/Analyzing-TV-Super-Bowl-Data
23. Music Trends & Spotify/KPOP Data Analytics
It is one of the most exciting data analytics projects where you analyze music streaming data to discover listener preferences and global trends. You can study factors like genres, popularity scores, release years, artist performance, and audience engagement across platforms like Spotify or K-pop charts. It helps you understand how data shapes the music industry and gives insights into what makes songs popular worldwide.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn, APIs, Data Cleaning, Data Visualization, Exploratory Data Analysis, Machine Learning Models.
Time taken: 1-2 weeks
Source code: https://github.com/subhojitdas859/Music_Trends_Analysis
24. Real-Time Stock Price Visualization
It is one of the most practical data visualization projects that allows you to track and display live stock price movements. With the help of this project, you can create interactive dashboards that show stock trends, volume changes, and historical comparisons in real-time. It also helps you to understand fluctuations in the market better and provides you with proper hands-on experience along with live data visualization.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, APIs, Tableau/Power BI, Data Cleaning, Data Visualization, and Time Series Analysis.
Time taken: 1-2 weeks
Source code: https://github.com/peterajhgraham/Real_Time_Stock_Price_Dashboard
Advanced Data Analytics Projects
In this section, we are going to discuss the advanced data analytics projects:
25. Credit Card Approvals Prediction & Analysis
It is one of the most advanced data analytics projects where you can build a model that helps to predict whether a credit card application will be approved or not. You can also analyze factors like income, credit history, employment, and spending behavior so that you can make accurate predictions. It also helps you to learn how you can apply machine learning in banking and finance to make smarter decisions.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Data Cleaning, Data Visualization, Feature Engineering, Logistic Regression, Random Forest, Gradient Boosting, and Model Evaluation.
Time taken: 2-3 weeks
Source code: https://github.com/semasuka/Credit-card-approval-prediction-classification
26. Fraud Detection using Machine Learning Models
It is also an advanced data analytics project with which you can build models to detect fraudulent activities during transactions. You can work with different datasets that consist of transaction details, customer behavior, and payment history to identify unusual patterns. It also helps you to understand anomaly detection, classification techniques, and how machine learning protects financial systems from fraud.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, XGBoost, Data Cleaning, Data Visualization, Feature Engineering, Anomaly Detection, Classification Models, and Model Evaluation.
Time taken: 2-3 weeks
Source code: https://github.com/shakiliitju/Credit-Card-Fraud-Detection-Using-Machine-Learning
27. Time Series Forecasting
It is one of the most important data analytics projects. With this project, you can predict the future values based on the past data trends. You can apply it in areas like stock prices, weather conditions, energy usage, or sales forecasting. It also helps you to understand seasonality, trends, and advanced predictive models that are used widely in real-world industries.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, Scikit-learn, ARIMA, SARIMA, LSTM, Data Cleaning, Data Visualization, Time Series Analysis, and Forecasting Models
Time taken: 2-3 weeks
Source code: https://github.com/amazon-science/chronos-forecasting
28. Climate Change Data Analysis & Prediction
It is one of the most impactful data analytics projects where you can analyze historical climate data and predict future climate patterns. With the help of this project, you can predict the changes in temperatures, CO2 levels, rise in sea levels, and extreme weather events, which help to understand trends and potential impacts. It also helps you to apply advanced analytics and predictive modeling, which helps to solve real-world environmental changes.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels, Time Series Analysis, Machine Learning, Data Cleaning, Data Visualization, and Predictive Modeling.
Time taken: 2-3 weeks
Source code: https://github.com/devangdayal/Climate-Change-Analysis
29. World Population Growth & Trends Analytics
It is one of the most insightful data analytics projects where you have to analyze the global population data to understand growth patterns, demographic changes, and regional trends. You can also apply advanced analytics and forecasting models to gain insights into demographic shifts worldwide.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn, Time Series Analysis, Data Cleaning, Data Visualization, and Predictive Modeling.
Time taken: 2-3 weeks
Source code: The source code for this project is given below:
https://github.com/yassnemo/population-growth-dashboard
30. Financial Market Basket Analysis
It is one of the most practical data analytics projects where you can analyze financial transaction data to find patterns of co-occurring events or purchases. You can also study which stocks, commodities, or financial products are frequently bought together and then identify the hidden relationships.
Skills required: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, MLxtend (for association rules), Data Cleaning, Data Visualization, Exploratory Data Analysis, and Market Basket Analysis.
Time taken: 2-3 weeks
Source code: https://github.com/KewJS/Instacart_Customer_Segmentation
How to Choose the Right Data Analytics Project for Your Skill Level
Given below are 5 simple points on how to choose the right data analytics projects according to your skill level:
1. Assess your Skills: You should always pick a project that manages your current knowledge of tools and programming languages.
2. Start Simple if you are a Beginner: You should start with small datasets and basic analysis so that you can build your confidence.
3. Challenge Yourself Gradually: Slowly, you should try doing intermediate projects after you have understood the basics.
4. Focus on your Interests: You should always select projects in areas that you enjoy, like sports, movies, or finance.
5. Consider Real-World Applications: You should always work on those projects that provide practical insights or solutions.
Tips for Building Data Analytics Projects with Real-World Data
1. Choose Reliable Data Sources: You should always use trusted websites, APIs, or open datasets to ensure accuracy.
2. Clean the Data First: You should always remove errors, missing values, and duplicates before starting to analyze your data.
3. Understand the Context: You should always have an idea about what the data represents and what insights you want to extract from the data.
4. Visualize your Findings: You should use charts and graphs to make patterns and trends that are easy to understand.
5. Document your Process: You should always keep notes of your steps, code, and insights to get clarity and future reference.
1. Python & R: For data analysis, you should learn Python and R for analysis, visualization, and machine learning.
2. Pandas and NumPy: You should have knowledge of Pandas and NumPy for handling and processing large datasets effectively.
3. Tableau and Power BI: You should also know the visualization tools like Tableau and Power BI that help to create interactive dashboards and reports.
4. SQL: SQL is a must-have skill because it helps to manage, query, and analyze structured data from databases.
5. Scikit-learn & TensorFlow: You should also know about these 2 machine learning libraries, Scikit-learn and TensorFlow, which help you to build predictive and advanced analytical models.
Conclusion
Working on data analytics projects is one of the best ways to practice your skills and learn how you can use data in real life. Choosing the right project will always help you to grow step-by-step, both as a beginner and at an advanced level. These projects help you build your technical skills and also give you confidence to solve real-world problems with data. By using the right tools, techniques, and creativity, you can turn simple raw data into meaningful insights, which makes a real impact.
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Top 30 Data Analytics Project Ideas – FAQs
Q1. Do I need coding skills to start data analytics projects?
Coding is not always necessary because tools like Tableau or Power BI are available. But coding provides you with more flexibility.
Q2. How do I find datasets for my data analytics projects?
To get datasets, you may consider data platforms like Kaggle, government websites, or free APIs.
Q3. Can data analytics projects help me get a job?
Yes, by showcasing your projects in the portfolio, you can get a job easily.
Q4. How much time should I spend on the data analytics project?
For a data analytics project, it depends on the size of the project. But beginners can complete small projects in 1-2 weeks.
Q5. Do I need to use machine learning in every data analytics project?
No, not all the projects need machine learning. Simple analysis and visualization of data can also be used.