Data Analyst Projects: 21 Ideas to Grow Your Skills in 2025

Diving into data analysis projects is your golden ticket to gaining practical experience. It’s not just about crunching numbers. It’s a journey that will take you from uncovering data sources to polishing (and even presenting) the final details.

Dreaming of a data analysis job? Start here. These projects are your playground and we’re here to guide you. From eye-catching visualizations to deep-dive analyses, this post is your roadmap to success with any data analytics project!

What’s the Point of a Data Analysis Project?

Doing data analytics projects is critical to landing a job, as they show hiring managers that you have the data analytics skills for the role. Professionals in this field must master a myriad of skills, from data cleaning and data visualization, as well as programming languages like SQL, R, and Python. A data analytics project can demonstrate your aptitude with all of these skills. Furthermore, personal data analytics projects are a great way to practice a variety of data analysis techniques, especially if you lack real-world experience.

Data Analysis Project Ideas

There’s no such thing as a typical data analyst’s job! Data analytics plays an important role in our lives, and once you start looking for project ideas, you’ll realize data is everywhere. Working on projects is an excellent way to gain experience with the end-to-end data analysis process, especially if you’re new to the field.

Beginner	Intermediate	Advanced
Web Scraping	Sentiment Analysis	Machine Learning
Exploratory Data Analysis (EDA)	Data Cleaning	Deep Learning
Data Visualization	Data Collection and Visualization

Beginner

Web Scraping

Web scraping involves extracting data from websites, transforming unstructured information into structured datasets suitable for analysis. This skill is crucial for gathering real-time data when APIs are unavailable.​

Why It Matters to Employers:

• Automation Skills: Developing web scraping scripts showcases your ability to automate repetitive tasks, enhancing efficiency and reducing the potential for human error.
• Data Acquisition: Companies often need data that isn’t readily available through standard channels. Proficiency in web scraping demonstrates your ability to collect and curate data from diverse sources, a valuable asset for data-driven decision-making.​
• Competitive Analysis: By scraping data from competitors’ websites, businesses can gain insights into market trends, pricing strategies, and product offerings, informing their strategic planning.​

Project Ideas:

• Reddit Analysis: Scrape posts and comments using PRAW, a Python library for accessing Reddit’s API. For datasets, check out Reddit datasets on data.world.
• Real Estate Dashboard: Use the Zillow API to collect data on properties, including prices, taxes, and nearby schools. Visualize the best options using Matplotlib or Tableau.

Tools and Libraries:

• Beautiful Soup: A Python library for parsing HTML and XML documents, useful for web scraping tasks.​
• Scrapy: An open-source Python framework designed for large-scale web scraping projects.

Exploratory Data Analysis (EDA)

EDA allows you to explore and summarize datasets, uncovering patterns, spotting anomalies, and testing hypotheses. It’s a foundational step in any data analysis process.​

Why It Matters to Employers:

• Hypothesis Generation: EDA helps in formulating hypotheses that can be tested in further analyses, driving strategic business decisions.
• Data Familiarization: Employers value professionals who can quickly understand and derive insights from new datasets, facilitating informed decision-making.​
• Problem Identification: Through EDA, potential issues such as data quality problems or outliers can be identified early, ensuring more accurate analyses downstream.​

Project Ideas:

• McDonald’s Nutrition Facts: Analyze this Kaggle dataset to visualize sugar, fiber, and sodium content. Tools like Pandas, Seaborn, and Matplotlib are essential.
• Global Happiness Trends: Use the World Happiness Report dataset to examine the differences in happiness across regions. SQLite or Pandas are great for exploring and summarizing this dataset.
• Global Suicide Rates: Analyze the relationship between GDP per capita and suicide rates using this Kaggle dataset.

Tools and Libraries:

• Pandas: A Python library providing data structures and functions designed to work with structured data easily and intuitively.​
• Seaborn: A Python visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.

Data Visualization

Data visualization is the graphical representation of information and data. Effective visualization helps in understanding complex datasets and communicating insights clearly.​

Why It Matters to Employers:

• Identifying Trends and Patterns: Visual tools help in spotting trends, outliers, and patterns that might not be evident in raw data, facilitating proactive business strategies.
• Enhanced Communication: Visualizations translate complex data findings into intuitive graphics, making it easier for stakeholders to grasp insights and make informed decisions.​
• Data Storytelling: The ability to craft compelling narratives around data findings is crucial. It aids in persuading stakeholders and driving strategic initiatives.​

Project Ideas:

• Pollution in the U.S.: Visualize EPA data on pollution from this Kaggle dataset to find trends in emissions over time using Seaborn or Tableau.
• Astronomical Data: Explore asteroids near Earth using a NASA-provided dataset. Use heatmaps and line charts for visualization.
• Instagram Analytics: Perform time series analysis on Instagram engagement using the Instagram dataset.

Tools and Libraries:

• Tableau: A leading data visualization tool known for its ability to create interactive and shareable dashboards.​
• Matplotlib and Seaborn: Python libraries for creating static, animated, and interactive visualizations.

Intermediate

Sentiment Analysis

Sentiment analysis involves determining the emotional tone behind a body of text. It’s widely used to understand customer opinions, feedback, and reviews.​

Why It Matters to Employers:

• Market Research: Analyzing sentiments across social media and reviews provides insights into market trends and consumer preferences.
• Customer Insights: Understanding customer sentiment helps businesses improve products, services, and overall customer satisfaction.​
• Brand Monitoring: Companies can track public perception of their brand in real-time, allowing for swift responses to negative sentiments.​

Project Ideas:

• Twitter Sentiment Analysis: Use the Twitter API to scrape tweets on a trending topic and classify them using TextBlob or NLTK.
• Google Reviews: Extract customer reviews with the Google My Business API and analyze them for sentiment with Python’s TextBlob library.
• Quora Question Matching: Work with duplicate question pair detection using this Kaggle dataset.

Tools and Libraries:

• TextBlob: A Python library for processing textual data, providing a simple API for diving into common natural language processing (NLP) tasks.​
• NLTK: The Natural Language Toolkit is a suite of libraries and programs for symbolic and statistical natural language processing for English.

Data Cleaning

Data cleaning is the process of detecting, correcting, or removing corrupt or inaccurate records from a dataset.

Why It Matters to Employers:

• Preprocessing for Modeling: Clean data is foundational for machine learning, forecasting, and other advanced analytics.
• Data Integrity: Employers rely on clean data for accurate business insights. If data is messy, it leads to bad decisions.
• Workflow Efficiency: Cleaning large datasets shows that you’re comfortable handling real-world, unstructured data — a daily challenge in many roles.

Project Ideas:

• Airbnb Open Data: Analyze Airbnb stays in NYC using Python to clean, categorize, and visualize data trends.
• YouTube Trends: Use this Kaggle dataset to clean data on trending videos, focusing on views, likes, and tags.
• Education Data: Explore U.S. Department of Education data for insights into student demographics and performance.

Tools and Libraries:

• Pandas: A powerful Python library for data manipulation, cleaning, and transformation — especially good for tabular data.
• NumPy: Useful for handling numerical operations and missing values at scale.
• Regex: Handy for cleaning text fields like video titles, user comments, or freeform survey inputs.
• Openpyxl: Lets you work with Excel files directly when cleaning raw spreadsheets — a common real-world scenario.

Advanced

Machine Learning

Machine learning (ML) enables systems to learn from data and make predictions without explicit programming. It’s a must-have skill for advanced analytics roles.

Why It Matters to Employers:

• Technical Rigor: Knowing ML algorithms demonstrates analytical depth and the ability to turn raw data into strategic tools.
• Predictive Insights: Companies use ML for forecasting revenue, customer behavior, fraud, and more — having these skills lets you move beyond descriptive analytics.
• Custom Solutions: Many business problems require tailored models (vs. off-the-shelf BI tools), so hands-on ML experience is key.

Project Ideas:

• Fraud Detection: Train machine learning models for fraud detection using Amazon SageMaker or Scikit-learn.
• Movie Recommendations: Build a recommender using the MovieLens dataset and collaborative filtering techniques.
• Wine Quality Prediction: Use this Kaggle dataset to predict wine quality with Random Forest or SVM models.

Tools and Libraries:

• Scikit-learn: The go-to Python library for classical ML algorithms — linear regression, decision trees, k-means, and more.
• XGBoost: A high-performance gradient boosting framework, great for competitions and real-world performance.
• Amazon SageMaker: A cloud-based platform to train and deploy models — shows readiness for production-level ML workflows.
• Matplotlib: Used for model evaluation visualizations like confusion matrices, ROC curves, and feature importance plots.

Deep Learning

Deep learning (a subset of ML) uses neural networks with many layers to solve complex problems like image recognition, natural language processing, and audio classification.

Why It Matters to Employers:

• Portfolio Differentiator: Deep learning projects are less common at the junior level — showcasing one gives you a competitive edge.
• AI-Powered Applications: Companies working on computer vision, recommendation engines, voice assistants, etc. need deep learning practitioners.
• Modern Tech Stack Alignment: Mastery of CNNs, RNNs, and transformers puts you at the frontier of what top tech companies are building.

Project Ideas:

• Breast Cancer Classification: Use a convolutional neural network (CNN) to classify biopsy images. Start with the Breast Cancer Wisconsin dataset.
• Image Recognition: Work with the CIFAR-10 dataset to build a CNN that classifies images into categories like cars, animals, or airplanes.
• Gender and Age Detection: Use the Adience dataset to develop a model using OpenCV and CNNs to predict gender and age based on facial images.
• Customer Churn Prediction: Use the Telco Customer Churn dataset to predict which customers are likely to cancel their service. Apply classification models like logistic regression, random forest, or XGBoost, and explore key churn indicators such as contract type, tenure, and monthly charges.

Tools and Libraries:

• TensorFlow: A leading open-source platform for building and deploying deep learning models at scale.
• Keras: A high-level neural network API built on top of TensorFlow, great for rapid prototyping.
• PyTorch: Another popular deep learning library, favored for flexibility and research use.
• OpenCV: Ideal for image preprocessing, facial detection, and computer vision workflows.

These data projects offer a mix of beginner, intermediate, and advanced ideas to help you develop skills in data collection, analysis, visualization, and machine learning. With links to resources and datasets, you’ll have everything you need to get started and build a portfolio that stands out!

What Skills Should You Focus on With Your Data Analytics Project?

Regardless of your level or skillset, data analysts can always improve on the following skills:

• SQL  – SQL is mainly used for storing and retrieving data from databases, writing queries, and modifying the schema (structure) of a database system. In your data analysis project, be sure to make use of some of the most important SQL commands, such as SELECT, DELETE, CREATE DATABASE, INSERT INTO, ALTER DATABASE, CREATE TABLE, and CREATE INDEX. 
• Programming – While data analysts don’t need to have advanced coding skills, the ability to program in R or Python lets you use more advanced data science techniques such as machine learning and natural language processing. 
• Data Cleaning Skills – Data cleaning is the process of preparing data for analysis by removing or modifying data that is incomplete, duplicated, incorrect, or improperly formatted. Fixing spelling and syntax errors, standardizing naming conventions, and correcting mistakes are key skills. 
• Visualization – As a data analyst, it’s important to communicate your findings with strong visuals that appeal to both technical and non-technical stakeholders. To visualize your data effectively, you need to know the specific use cases for each type of visual, from bar charts to histograms and more. 
• Microsoft Excel – Data analysts use Excel and other spreadsheet tools to sort, filter, and clean their data. Excel is also a useful tool for doing simple calculations (eg: SUMIF and AVERAGEIF) or combining data using VLOOKUP. 
• Familiarity With Machine Learning, AI, and NPL – Data analysts with machine learning skills are incredibly valuable, even though machine learning is not an expected skill for most data analyst jobs. While data analytics is primarily concerned with data modeling and applied statistics, machine learning algorithms go a step further in obtaining insights and predicting future trends. 

Related Read: 65 Excel Interview Questions for Data Analysts

How To Present and Promote Your Data Project

A strong data analytics portfolio doesn’t just showcase technical skills—it tells a clear, compelling story. Each project should demonstrate your ability to solve real problems, communicate insights, and use the right tools effectively.

For every project, include:

• A clear problem statement: What question were you trying to answer?
• Tools and methods used: Explain why you chose specific languages, libraries, or platforms.
• Your process: Outline your steps—data collection, cleaning, analysis, modeling, and visualization.
• Key findings and recommendations: Summarize the results and what actions they support.
• Challenges and solutions: Briefly describe any obstacles you encountered and how you solved them.

Aim for variety in your portfolio. A well-rounded collection should include:

• One exploratory data analysis (EDA) project
• One data cleaning project
• One SQL-based project
• One focused on data visualization

If possible, include one end-to-end project that takes raw data all the way to a final recommendation or model.

To promote your work:

• Upload code and documentation to GitHub with clear, readable README files.
• Use Tableau Public for dashboards so employers can view your visualizations online.
• Post summaries of your projects on LinkedIn, linking to GitHub or Tableau.
• Consider creating a personal website or portfolio to organize your work in one place.

Presenting your work with clarity and purpose helps you stand out—and signals to employers that you’re ready to work in a professional data role.

Data Analytics Project FAQs

Since you’re here…
Interested in a career in data analytics? You will be after scanning this data analytics salary guide. When you’re serious about getting a job, look into our 40-hour Intro to Data Analytics Course for total beginners, or our mentor-led Data Analytics Bootcamp.