20 Data Analysis Projects to Boost 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 is the process of extracting data from web pages. It’s a great way to get hands-on with data collection, cleaning, and analysis. Tools like Python libraries (BeautifulSoup, Scrapy) or web scraping platforms (ParseHub) simplify the process.

• 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.

Exploratory Data Analysis (EDA)

EDA allows you to explore and summarize datasets.

• 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.

Data Visualization

Visualization helps communicate trends effectively.

• 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.

Intermediate

Sentiment Analysis

Analyze text data to uncover trends in user sentiment.

• 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.

Data Cleaning

Clean messy datasets to extract meaningful insights.

• 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.

Advanced

Machine Learning

Develop predictive models for advanced analytics.

• 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.

Deep Learning

Practice advanced neural networks for real-world applications.

• 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.

These 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

Essential Tools and Libraries for Data Analysis Projects

Mastering key tools and libraries is vital for any data analyst. Below are the most widely-used resources, categorized into three stages of the data analysis process:

Data Manipulation and Cleaning

• Pandas: A must-have Python library for data wrangling, Pandas allows efficient manipulation of structured datasets through its powerful DataFrame object. It’s perfect for filtering, aggregating, and cleaning data.
• NumPy: Ideal for numerical operations, NumPy provides fast computations for arrays and matrices, which is especially useful in large datasets.
• OpenRefine: A free tool for cleaning messy datasets, OpenRefine supports deduplication and transformations for large-scale data cleaning tasks.

Visualization and Communication

• Matplotlib: A versatile Python library for creating detailed static and interactive visualizations.
• Seaborn: Built on Matplotlib, Seaborn simplifies creating advanced statistical graphs like heatmaps and violin plots.
• Tableau: A leading business intelligence tool for building interactive dashboards with no coding required, making it ideal for professional reporting.

Advanced Analytics and Machine Learning

• Scikit-learn: This Python library is a go-to for machine learning, offering tools for regression, classification, and clustering.
• TensorFlow and PyTorch: These libraries are essential for deep learning projects, allowing you to build and train neural networks for advanced tasks.
• BeautifulSoup and APIs: BeautifulSoup enables easy web scraping, while APIs like Twitter and Zillow provide structured datasets for analysis.

How To Present and Promote Your Data Project

A good data analytics portfolio showcases your abilities. Each project should articulate the value of the data product or model you’ve built. Describe the technical challenge and how you overcame it, what tools you leveraged and why, and explain your findings using well-chosen visuals. 

Your portfolio should feature a diverse collection of projects, including exploratory data analysis projects, a data cleaning project, a project that uses SQL, and data visualization projects. Promote your projects by uploading them on Github. If you use Tableau for data visualization, set your project to ‘Public’ so that it is searchable online by potential employers. 

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.