You're here because you're serious about becoming a data analyst. You’ve probably noticed that just about every data analytics job posting asks for experience. But how do individuals get experience if they’re just starting out?! The answer: you do it by building a solid portfolio of data analytic projects so that you can land a job as a junior data analyst, even with no experience.

Your portfolio is your ticket to proving your capabilities to a potential employer. Even without previous job experience, a well-curated collection of data analytics projects can set you apart from the competition. They demonstrate your ability to tackle real-world problems with real data, showcasing your ability to clean datasets, create compelling visualizations, and extract meaningful insights—skills that are in high demand.

You just have to pick the ones that speak to you and get started!

Getting started with data analytics projects

So, you're ready to tackle your first data analytics project? Awesome! Let's break down what you need to know to set yourself up for success.

Our curated list of 20 projects below will help you develop the most sought-after data analysis skills and practice using the most frequently used data analysis tools. Namely:

• Python
• R
• Microsoft Excel
• Tableau
• Power BI
• SQL

Setting up an effective development environment is also vital. Begin by creating a Python environment with Conda or venv. Use version control like Git to track project changes. Combine an IDE like Jupyter Notebook with core Python libraries to boost your productivity.

Remember, Rome wasn't built in a day! Start your data analysis journey with bite-sized projects to steadily build your skills. Keep learning, stay curious, and enjoy the ride. Before you know it, you'll be tackling real-world data challenges like the professionals do.

Each project listed below will help you apply what you've learned to real data, growing your abilities one step at a time. While they are tailored towards beginners, some will be more challenging than others. By working through them, you'll create a portfolio that shows a potential employer you have the practical skills to analyze data on the job.

The data analytics projects below cover a range of analysis techniques, applications, and tools:

1. Learn and Install Jupyter Notebook
2. Profitable App Profiles for the App Store and Google Play Markets
3. Exploring Hacker News Posts
4. Clean and Analyze Employee Exit Surveys
5. Star Wars Survey
6. Word Raider
7. Install RStudio
8. Creating An Efficient Data Analysis Workflow
9. Creating An Efficient Data Analysis Workflow, Part 2
10. Preparing Data with Excel
11. Visualizing the Answer to Stock Questions Using Spreadsheet Charts
12. Identifying Customers Likely to Churn for a Telecommunications Provider
13. Data Prep in Tableau
14. Business Intelligence Plots
15. Data Presentation
16. Modeling Data in Power BI
17. Visualization of Life Expectancy and GDP Variation Over Time
18. Building a BI App
19. Analyzing Kickstarter Projects
20. Analyzing Startup Fundraising Deals from Crunchbase

In the following sections, you'll find step-by-step guides to walk you through each project. These detailed instructions will help you apply what you've learned and solidify your data analytics skills.

1. Learn and Install Jupyter Notebook

Overview

In this beginner-level project, you'll assume the role of a Jupyter Notebook novice aiming to gain the essential skills for real-world data analytics projects. You'll practice running code cells, documenting your work with Markdown, navigating Jupyter using keyboard shortcuts, mitigating hidden state issues, and installing Jupyter locally. By the end of the project, you'll be equipped to use Jupyter Notebook to work on data analytics projects and share compelling, reproducible notebooks with others.

Tools and Technologies

• Jupyter Notebook
• Python

Prerequisites

Before you take on this project, it's recommended that you have some foundational Python skills in place first, such as:

• Working with Python variables and data types
• Manipulating Python lists and dictionaries
• Writing Python functions

Step-by-Step Instructions

1. Get acquainted with the Jupyter Notebook interface and its components
2. Practice running code cells and learn how execution order affects results
3. Use keyboard shortcuts to efficiently navigate and edit notebooks
4. Create Markdown cells to document your code and communicate your findings
5. Install Jupyter locally to work on projects on your own machine

Expected Outcomes

Upon completing this project, you'll have gained practical experience and valuable skills, including:

• Familiarity with the core components and workflow of Jupyter Notebook
• Ability to use Jupyter Notebook to run code, perform analysis, and share results
• Understanding of how to structure and document notebooks for real-world reproducibility
• Proficiency in navigating Jupyter Notebook using keyboard shortcuts to boost productivity
• Readiness to apply Jupyter Notebook skills to real-world data projects and collaborate with others

Relevant Links and Resources

• How to Use Jupyter Notebook: A Beginner's Tutorial

Additional Resources

• 28 Jupyter Notebook Tips, Tricks, and Shortcuts
• Tutorial: Advanced Jupyter Notebooks

2. Profitable App Profiles for the App Store and Google Play Markets

Overview

In this guided project, you'll assume the role of a data analyst for a company that builds ad-supported mobile apps. By analyzing historical data from the Apple App Store and Google Play Store, you'll identify app profiles that attract the most users and generate the most revenue. Using Python and Jupyter Notebook, you'll clean the data, analyze it using frequency tables and averages, and make practical recommendations on the app categories and characteristics the company should target to maximize profitability.

Tools and Technologies

• Python
• Data Analytics
• Jupyter Notebook

Prerequisites

This is a beginner-level project, but you should be comfortable working with Python functions and Jupyter Notebook:

• Writing functions with arguments, return statements, and control flow
• Debugging functions to ensure proper execution
• Using conditional logic and loops within functions
• Working with Jupyter Notebook to write and run code

Step-by-Step Instructions

1. Open and explore the App Store and Google Play datasets
2. Clean the datasets by removing non-English apps and duplicate entries
3. Isolate the free apps for further analysis
4. Determine the most common app genres and their characteristics using frequency tables
5. Make recommendations on the ideal app profiles to maximize users and revenue

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Cleaning real-world data to prepare it for analysis
• Analyzing app market data to identify trends and success factors
• Applying data analysis techniques like frequency tables and calculating averages
• Using data insights to inform business strategy and decision-making
• Communicating your findings and recommendations to stakeholders

Relevant Links and Resources

• Python Functions and Jupyter Notebook course
• How to Use Jupyter Notebook: A Beginner's Tutorial

Additional Resources

• Basic Data Science Portfolio Project Tutorial
• Dataquest community where you can view and share this project with others
• Example Solution Code

3. Exploring Hacker News Posts

Overview

In this project, you'll explore and analyze a dataset from Hacker News, a popular tech-focused community site. Using Python, you'll apply skills in string manipulation, object-oriented programming, and date management to uncover trends in user submissions and identify factors that drive community engagement. This hands-on project will strengthen your ability to interpret real-world datasets and enhance your data analysis skills.

Tools and Technologies

• Python
• Data cleaning
• Object-oriented programming
• Data Analytics
• Jupyter Notebook

Prerequisites

To get the most out of this project, you should have some foundational Python and data cleaning skills, such as:

• Employing loops in Python to explore CSV data
• Utilizing string methods in Python to clean data for analysis
• Processing dates from strings using the datetime library
• Formatting dates and times for analysis using strftime

Step-by-Step Instructions

1. Remove headers from a list of lists
2. Extract 'Ask HN' and 'Show HN' posts
3. Calculate the average number of comments for 'Ask HN' and 'Show HN' posts
4. Find the number of 'Ask HN' posts and average comments by hour created
5. Sort and print values from a list of lists

Expected Outcomes

After completing this project, you'll have gained practical experience and skills, including:

• Applying Python string manipulation, OOP, and date handling to real-world data
• Analyzing trends and patterns in user submissions on Hacker News
• Identifying factors that contribute to post popularity and engagement
• Communicating insights derived from data analysis

Relevant Links and Resources

• Data Analyst in Python
• Original Hacker News Posts dataset on Kaggle
• Hacker News

Additional Resources

• Dataquest community where you can view and share this project with others
• Example Solution Code

4. Clean and Analyze Employee Exit Surveys

Overview

In this hands-on project, you'll play the role of a data analyst for the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. Your task is to clean and analyze employee exit surveys from both institutes to identify insights into why employees resign. Using Python and pandas, you'll combine messy data from multiple sources, clean column names and values, analyze the data, and share your key findings.

Tools and Technologies

• Python
• Pandas
• Data cleaning
• Data Analytics
• Jupyter Notebook

Prerequisites

Before starting this project, you should be familiar with:

• Exploring and analyzing data using pandas
• Aggregating data with pandas groupby operations
• Combining datasets using pandas concat and merge functions
• Manipulating strings and handling missing data in pandas

Step-by-Step Instructions

1. Load and explore the DETE and TAFE exit survey data
2. Identify missing values and drop unnecessary columns
3. Clean and standardize column names across both datasets
4. Filter the data to only include resignation reasons
5. Verify data quality and create new columns for analysis
6. Combine the cleaned datasets into one for further analysis
7. Analyze the cleaned data to identify trends and insights

Expected Outcomes

By completing this project, you will:

• Clean real-world, messy HR data to prepare it for analysis
• Apply core data cleaning techniques in Python and pandas
• Combine multiple datasets and conduct exploratory analysis
• Analyze employee exit surveys to understand key drivers of resignations
• Summarize your findings and share data-driven recommendations

Relevant Links and Resources

• Pandas and NumPy Fundamentals Course

Additional Resources

• Community Feedback on Guided Project
• Example Solution Code

5. Star Wars Survey

Overview

In this project designed for beginners, you'll become a data analyst exploring FiveThirtyEight's Star Wars survey data. Using Python and pandas, you'll clean messy data, map values, compute statistics, and analyze the data to uncover fan film preferences. By comparing results between demographic segments, you'll gain insights into how Star Wars fans differ in their opinions. This project provides hands-on practice with key data cleaning and analysis techniques essential for data analyst roles across industries.

Tools and Technologies

• Python
• Pandas
• Jupyter Notebook

Prerequisites

Before starting this project, you should be familiar with the following:

• Exploring and cleaning data using pandas
• Combining datasets and performing joins in pandas
• Applying functions over columns in pandas DataFrames
• Analyzing survey data using pandas

Step-by-Step Instructions

1. Map Yes/No columns to Boolean values to standardize the data
2. Convert checkbox columns to lists and get them into a consistent format
3. Clean and rename the ranking columns to make them easier to analyze
4. Identify the highest-ranked and most-viewed Star Wars films
5. Analyze the data by key demographic segments like gender, age, and location
6. Summarize your findings on fan preferences and differences between groups

Expected Outcomes

After completing this project, you will have gained:

• Experience cleaning and analyzing a real-world, messy dataset
• Hands-on practice with pandas data manipulation techniques
• Insights into the preferences and opinions of Star Wars fans
• An understanding of how to analyze survey data for business insights

Relevant Links and Resources

• FiveThirtyEight's Star Wars Survey Data
• Introduction to Python Programming

Additional Resources

• Dataquest Community - Star Wars Survey Project
• Example Solution Code

6. Word Raider

Overview

In this beginner-level Python project, you'll step into the role of a developer to create "Word Raider," an interactive word-guessing game. Although this project won't have you perform any explicit data analysis, it will sharpen your Python skills and make you a better data analyst. Using fundamental programming skills, you'll apply concepts like loops, conditionals, and file handling to build the game logic from the ground up. This hands-on project allows you to consolidate your Python knowledge by integrating key techniques into a fun application.

Tools and Technologies

• Python
• Jupyter Notebook

Prerequisites

Before diving into this project, you should have some foundational Python skills, including:

• Familiarity with Python basics like variables, data types, and functions
• Ability to work with loops and conditional statements to control program flow
• Experience with reading data from files and manipulating strings
• Basic understanding of object-oriented programming concepts

Step-by-Step Instructions

1. Build the word bank by reading words from a text file into a Python list
2. Set up variables to track the game state, like the hidden word and remaining attempts
3. Implement functions to receive and validate user input for their guesses
4. Create the game loop, checking guesses against the hidden word and providing feedback
5. Update the game state after each guess and check for a win or loss condition

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Strengthened proficiency in fundamental Python programming concepts
• Experience building an interactive, text-based game from scratch
• Practice with file I/O, data structures, and basic object-oriented design
• Improved problem-solving skills and ability to translate ideas into code

Relevant Links and Resources

• Junior Data Analyst career path
• How to Become a Junior Data Analyst in 2024

Additional Resources

• Dataquest Community: My Take on the Word Raider Game
• Selecting and Installing an IDE
• Example Solution Code

7. Install RStudio

Overview

In this beginner-level project, you'll take the first steps in your data analysis journey by installing R and RStudio. As an aspiring data analyst, you'll set up a professional programming environment and explore RStudio's features for efficient R coding and analysis. Through guided exercises, you'll write scripts, import data, and create visualizations, building key foundations for your career.

Tools and Technologies

• R
• RStudio

Prerequisites

To complete this project, it's recommended to have basic knowledge of:

• R syntax and programming fundamentals
• Variables, data types, and arithmetic operations in R
• Logical and relational operators in R expressions
• Importing, exploring, and visualizing datasets in R

Step-by-Step Instructions

1. Install the latest version of R and RStudio on your computer
2. Practice writing and executing R code in the Console
3. Import a dataset into RStudio and examine its contents
4. Write and save R scripts to organize your code
5. Generate basic data visualizations using ggplot2

Expected Outcomes

By completing this project, you'll gain essential skills including:

• Setting up an R development environment with RStudio
• Navigating RStudio's interface for data science workflows
• Writing and running R code in scripts and the Console
• Installing and loading R packages for analysis and visualization
• Importing, exploring, and visualizing data in RStudio

Relevant Links and Resources

• R Basics for Data Analysis
• The Comprehensive R Archive Network

Additional Resources

• Top Tips for Learning R from Africa R’s Shelmith Kariuki
• The most trusted IDE for open source data science

8. Creating An Efficient Data Analysis Workflow

Overview

In this hands-on project, you'll step into the role of a data analyst hired by a company selling programming books. Your mission is to analyze their sales data to determine which titles are most profitable. You'll apply key R programming concepts like control flow, loops, and functions to develop an efficient data analysis workflow. This project provides valuable practice in data cleaning, transformation, and analysis, culminating in a structured report of your findings and recommendations.

Tools and Technologies

• R
• RStudio
• Data Analytics

Prerequisites

To successfully complete this project, you should have the following foundational control flow, iteration, and functions in R skills:

• Implementing control flow using if-else statements
• Employing for loops and while loops for iteration
• Writing custom functions to modularize code
• Combining control flow, loops, and functions in R

Step-by-Step Instructions

1. Get acquainted with the provided book sales dataset
2. Transform and prepare the data for analysis
3. Analyze the cleaned data to identify top performing titles
4. Summarize your findings in a structured report
5. Provide data-driven recommendations to stakeholders

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Applying R programming concepts to real-world data analysis
• Developing an efficient, reproducible data analysis workflow
• Cleaning and preparing messy data for analysis
• Analyzing sales data to derive actionable business insights
• Communicating findings and recommendations to stakeholders

Relevant Links and Resources

• Getting Started with R and RStudio - Dataquest Blog
• Download the Book Reviews dataset - CSV file

Additional Resources

• Example Solution Code
• Dataquest community where you can view and share this project with others

9. Creating An Efficient Data Analysis Workflow, Part 2

Overview

In this hands-on project, you'll step into the role of a data analyst at a book company tasked with evaluating the impact of a new program launched on July 1, 2019 to encourage customers to buy more books. Using your data analysis skills in R, you'll clean and process the company's 2019 sales data to determine if the program successfully boosted book purchases and improved review quality. This project allows you to apply key R packages like dplyr, stringr, and lubridate to efficiently analyze a real-world business dataset and deliver actionable insights.

Tools and Technologies

• R
• RStudio
• dplyr
• stringr
• lubridate

Prerequisites

To successfully complete this project, you should have some specialized data processing in R skills:

• Manipulating strings using stringr functions
• Working with dates and times using lubridate
• Applying the map function to vectorize custom functions
• Understanding and employing regular expressions for pattern matching

Step-by-Step Instructions

1. Load and explore the book company's 2019 sales data
2. Clean the data by handling missing values and inconsistencies
3. Process the text reviews to determine positive/negative sentiment
4. Compare key sales metrics like purchases and revenue before and after the July 1 program launch date
5. Analyze differences in sales between customer segments

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Cleaning and preparing a real-world business dataset for analysis
• Applying powerful R packages to manipulate and process data efficiently
• Analyzing sales data to quantify the impact of a new initiative
• Translating analysis findings into meaningful business insights

Relevant Links and Resources

• Download the Book Sales dataset - CSV file
• Data Analyst in R Path

Additional Resources

• Example Solution Code
• Dataquest community where you can view and share this project with others

10. Preparing Data with Excel

Overview

In this hands-on project for beginners, you'll step into the role of a data professional in a marine biology research organization. Your mission is to prepare a raw dataset on shark attacks for an analysis team to study trends in attack locations and frequency over time. Using Excel, you'll import the data, organize it in worksheets and tables, handle missing values, and clean the data by removing duplicates and fixing inconsistencies. This project provides practical experience in the essential data preparation skills required for real-world analysis projects.

Tools and Technologies

• Excel

Prerequisites

This project is designed for beginners. To complete it, you should be familiar with preparing data in Excel:

• Importing data into Excel from various sources
• Organizing spreadsheet data using worksheets and tables
• Cleaning data by removing duplicates, fixing inconsistencies, and handling missing values
• Consolidating data from multiple sources into a single table

Step-by-Step Instructions

1. Import the raw shark attack data into an Excel workbook
2. Organize the data into worksheets and tables with a logical structure
3. Clean the data by removing duplicate entries and fixing inconsistencies
4. Consolidate shark attack data from multiple sources into a single table

Expected Outcomes

By completing this project, you will gain:

• Hands-on experience in data preparation and cleaning techniques using Excel
• Foundational skills for importing, organizing, and cleaning data for analysis
• An understanding of how to handle missing values and inconsistencies in a dataset
• Ability to consolidate data from disparate sources into an analysis-ready format
• Practical experience working with a real-world dataset on shark attacks
• A solid foundation for data analysis projects and further learning in Excel

Relevant Links and Resources

• Shark attacks dataset - Kaggle
• Introduction to Data Analysis with Excel Skill Path

Additional Resources

• Dataquest community where you can view and share this project with others

11. Visualizing the Answer to Stock Questions Using Spreadsheet Charts

Overview

In this hands-on project, you'll step into the shoes of a business analyst to explore historical stock market data using Excel. By applying information design concepts, you'll create compelling visualizations and craft an insightful report – building valuable skills for communicating data-driven insights that are highly sought-after by employers across industries.

Tools and Technologies

• Excel
• Data visualization
• Information design principles

Prerequisites

To successfully complete this project, it's recommended to have foundational visualizing data in Excel skills, such as:

• Creating various chart types in Excel to visualize data
• Selecting appropriate chart types to effectively present data
• Applying design principles to create clear and informative charts
• Designing charts for an audience using Gestalt principles

Step-by-Step Instructions

1. Import the dataset to an Excel spreadsheet
2. Create a report using data visualizations and tabular data
3. Represent the data using effective data visualizations
4. Apply Gestalt principles and pre-attentive attributes to all visualizations
5. Maximize data-ink ratio in all visualizations

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Analyzing real-world stock market data in Excel
• Applying information design principles to create effective visualizations
• Selecting the best chart types to answer specific questions about the data
• Combining multiple charts into a cohesive, insightful report
• Developing in-demand data visualization and communication skills

Relevant Links and Resources

• Microsoft Stock Data - Kaggle
• Amazon Stock Data - Kaggle
• INTEL Stock Data - Kaggle
• Bitcoin Historical Data - Kaggle
• Introduction to Data Analysis with Excel Skill Path

Additional Resources

• Dataquest community where you can view and share this project with others

12. Identifying Customers Likely to Churn for a Telecommunications Provider

Overview

In this beginner project, you'll take on the role of a data analyst at a telecommunications company. Your challenge is to explore customer data in Excel to identify profiles of those likely to churn. Retaining customers is crucial for telecom providers, so your insights will help inform proactive retention efforts. You'll conduct exploratory data analysis, calculating key statistics, building PivotTables to slice the data, and creating charts to visualize your findings. This project provides hands-on experience with core Excel skills for data-driven business decisions that will enhance your analyst portfolio.

Tools and Technologies

• Excel

Prerequisites

To complete this project, you should feel comfortable exploring data in Excel:

• Calculating descriptive statistics in Excel
• Analyzing data with descriptive statistics
• Creating PivotTables in Excel to explore and analyze data
• Visualizing data with histograms and boxplots in Excel

Step-by-Step Instructions

1. Import the customer dataset into Excel
2. Calculate descriptive statistics for key metrics
3. Create PivotTables, histograms, and boxplots to explore data differences
4. Analyze and identify profiles of likely churners
5. Compile a report with your data visualizations

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Hands-on practice analyzing a real-world customer dataset in Excel
• Ability to calculate and interpret key statistics to profile churn risks
• Experience building PivotTables and charts to slice data and uncover insights
• Skill in translating analysis findings into an actionable report for stakeholders

Relevant Links and Resources

• Customer Churn Prediction 2020 dataset - Kaggle
• Introduction to Data Analysis with Excel Skill Path

Additional Resources

• Dataquest community where you can view and share this project with others

13. Data Prep in Tableau

Overview

In this hands-on project, you'll take on the role of a data analyst for Dataquest to prepare their online learning platform data for analysis. You'll connect to Excel data, import tables into Tableau, and define table relationships to build a data model for uncovering insights on student engagement and performance. This project focuses on essential data preparation steps in Tableau, providing you with a robust foundation for data visualization and analysis.

Tools and Technologies

• Tableau

Prerequisites

To successfully complete this project, you should have some foundational skills in preparing data in Tableau, such as:

• Connecting to data sources in Tableau to access the required data
• Importing data tables into the Tableau canvas
• Defining relationships between tables in Tableau to combine data
• Cleaning and filtering imported data in Tableau to prepare it for use

Step-by-Step Instructions

1. Connect to the provided Excel file containing key tables on student engagement, course performance, and content completion rates
2. Import the tables into Tableau and define the relationships between tables to create a unified data model
3. Clean and filter the imported data to handle missing values, inconsistencies, or irrelevant information
4. Save the prepared data source to use for creating visualizations and dashboards
5. Reflect on the importance of proper data preparation for effective analysis

Expected Outcomes

By completing this project, you will gain valuable skills and experience, including:

• Hands-on practice with essential data preparation techniques in Tableau
• Ability to connect to, import, and combine data from multiple tables
• Understanding of how to clean and structure data for analysis
• Readiness to progress to creating visualizations and dashboards to uncover insights

Relevant Links and Resources

• Download the dataset - Excel (xlsx) file
• Data Visualization with Tableau Skill Path

Additional Resources

• Tableau Community Forums
• Tableau Documentation

14. Business Intelligence Plots

Overview

In this hands-on project, you'll step into the role of a data visualization consultant for Adventure Works. The company's leadership team wants to understand the differences between their online and offline sales channels. You'll apply your Tableau skills to build insightful, interactive data visualizations that provide clear comparisons and enable data-driven business decisions. Key techniques include creating calculated fields, applying filters, utilizing dual-axis charts, and embedding visualizations in tooltips. By the end, you'll have a set of powerful Tableau dashboards ready to share with stakeholders.

Tools and Technologies

• Tableau

Prerequisites

To successfully complete this project, you should have a solid grasp of data visualization fundamentals in Tableau:

• Navigating the Tableau interface and distinguishing between dimensions and measures
• Constructing various foundational chart types in Tableau
• Developing and interpreting calculated fields to enhance analysis
• Employing filters to improve visualization interactivity

Step-by-Step Instructions

1. Compare online vs offline orders using visualizations
2. Analyze products across channels with scatter plots
3. Embed visualizations in tooltips for added insight
4. Summarize findings and identify next steps

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience:

• Practical experience building interactive business intelligence dashboards in Tableau
• Ability to create calculated fields to conduct tailored analysis
• Understanding of how to use filters and tooltips to enable data exploration
• Skill in developing visualizations that surface actionable insights for stakeholders

Relevant Links and Resources

• Data Visualization with Tableau Skill Path

Additional Resources

• Tableau Community Forums
• Tableau Documentation

15. Data Presentation

Overview

In this project, you'll step into the role of a data analyst exploring conversion funnel trends for a company's leadership team. Using Tableau, you'll build interactive dashboards that uncover insights about which marketing channels, locations, and customer personas drive the most value in terms of volume and conversion rates. By applying data visualization best practices and incorporating dashboard actions and filters, you'll create a professional, usable dashboard ready to present your findings to stakeholders.

Tools and Technologies

• Tableau

Prerequisites

To successfully complete this project, you should be comfortable sharing insights in Tableau, such as:

• Building basic charts like bar charts and line graphs in Tableau
• Employing color, size, trend lines and forecasting to emphasize insights
• Combining charts, tables, text and images into dashboards
• Creating interactive dashboards with filters and quick actions

Step-by-Step Instructions

1. Import and clean the conversion funnel data in Tableau
2. Build basic charts to visualize key metrics
3. Create interactive dashboards with filters and actions
4. Add annotations and highlights to emphasize key insights
5. Compile a professional dashboard to present findings

Expected Outcomes

Upon completing this project, you'll have gained practical experience and valuable skills, including:

• Analyzing conversion funnel data to surface actionable insights
• Visualizing trends and comparisons using Tableau charts and graphs
• Applying data visualization best practices to create impactful dashboards
• Adding interactivity to enable exploration of the data
• Communicating data-driven findings and recommendations to stakeholders

Relevant Links and Resources

• Data Visualization with Tableau Skill Path
• Example Solution

Additional Resources

• Tableau Community Forums
• Tableau Documentation

16. Modeling Data in Power BI

Overview

In this hands-on project, you'll step into the role of an analyst at a company that sells scale model cars. Your mission is to model and analyze data from their sales records database using Power BI to extract insights that drive business decision-making. Power BI is a powerful business analytics tool that enables you to connect to, model, and visualize data. By applying data cleaning, transformation, and modeling techniques in Power BI, you'll prepare the sales data for analysis and develop practical skills in working with real-world datasets. This project provides valuable experience in extracting meaningful insights from raw data to inform business strategy.

Tools and Technologies

• Power BI

Prerequisites

To successfully complete this project, you should know how to model data in Power BI, such as:

• Designing a basic data model in Power BI
• Configuring table and column properties in Power BI
• Creating calculated columns and measures using DAX in Power BI
• Reviewing the performance of measures, relationships, and visuals in Power BI

Step-by-Step Instructions

1. Import the sales data into Power BI
2. Clean and transform the data for analysis
3. Design a basic data model in Power BI
4. Create calculated columns and measures using DAX
5. Build visualizations to extract insights from the data

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

• Hands-on practice modeling and analyzing real-world sales data in Power BI
• Ability to clean, transform and prepare data for analysis
• Experience extracting meaningful business insights from raw data
• Developing practical skills in data modeling and analysis using Power BI

Relevant Links and Resources

• Introduction to Power BI
• Official Power BI documentation

Additional Resources

• Power BI Community
• A CEO Explains How to Learn Power BI (Step-By-Step)

17. Visualization of Life Expectancy and GDP Variation Over Time

Overview

In this project, you'll step into the role of a data analyst tasked with visualizing life expectancy and GDP data over time to uncover trends and regional differences. Using Power BI, you'll apply data cleaning, transformation, and visualization skills to create interactive scatter plots and stacked column charts that reveal insights from the Gapminder dataset. This hands-on project allows you to practice the full life-cycle of report and dashboard development in Power BI. You'll load and clean data, create and configure visualizations, and publish your work to showcase your skills. By the end, you'll have an engaging, interactive dashboard to add to your portfolio.

Tools and Technologies

• Power BI

Prerequisites

To complete this project, you should be able to visualize data in Power BI, such as:

• Creating basic Power BI visuals
• Designing accessible report layouts
• Customizing report themes and visual markers
• Publishing Power BI reports and dashboards

Step-by-Step Instructions

1. Import the life expectancy and GDP data into Power BI
2. Clean and transform the data for analysis
3. Create interactive scatter plots and stacked column charts
4. Design an accessible report layout in Power BI
5. Customize visual markers and themes to enhance insights

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Applying data cleaning, transformation, and visualization techniques in Power BI
• Creating interactive scatter plots and stacked column charts to uncover data insights
• Developing an engaging dashboard to showcase your data visualization skills
• Practicing the full life-cycle of Power BI report and dashboard development

Relevant Links and Resources

• Introduction to Power BI
• Official Power BI Support
• Official Power BI documentation

Additional Resources

• Why Business Analysts Need to Learn Power BI

18. Building a BI App

Overview

In this hands-on project, you'll step into the role of a business intelligence analyst at Dataquest, an online learning platform. Using Power BI, you'll import and model data on course completion rates and Net Promoter Scores (NPS) to assess course quality. You'll create insightful visualizations like KPI metrics, line charts, and scatter plots to analyze trends and compare courses. Leveraging this analysis, you'll provide data-driven recommendations on which courses Dataquest should improve.

Tools and Technologies

• Power BI

Prerequisites

To successfully complete this project, you should have some foundational skills in Power BI, such as how to manage workspaces and datasets in Power BI:

• Creating and managing workspaces
• Importing and updating assets within a workspace
• Developing dynamic reports using parameters
• Implementing static and dynamic row-level security

Step-by-Step Instructions

1. Import and explore the course completion and NPS data, looking for data quality issues
2. Create a data model relating the fact and dimension tables
3. Write calculations for key metrics like completion rate and NPS, and validate the results
4. Design and build visualizations to analyze course performance trends and comparisons

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience:

• Importing, modeling, and analyzing data in Power BI to drive decisions
• Creating calculated columns and measures to quantify key metrics
• Designing and building insightful data visualizations to convey trends and comparisons
• Developing impactful reports and dashboards to summarize findings
• Sharing data stories and recommending actions via Power BI apps

Relevant Links and Resources

• Introduction to Power BI
• Official Power BI documentation

Additional Resources

• What’s the best way to learn Microsoft Power BI?
• Sample datasets from Power BI

19. Analyzing Kickstarter Projects

Overview

In this hands-on project, you'll step into the role of a data analyst to explore and analyze Kickstarter project data using SQL. You'll start by importing and exploring the dataset, followed by cleaning the data to ensure accuracy. Then, you'll write SQL queries to uncover trends and insights within the data, such as success rates by category, funding goals, and more. By the end of this project, you'll be able to use SQL to derive meaningful insights from real-world datasets.

Tools and Technologies

• SQL

Prerequisites

To successfully complete this project, you should be comfortable working with SQL and databases, such as:

• Basic SQL commands and querying
• Data manipulation and joins in SQL
• Experience with cleaning data and handling missing values

Step-by-Step Instructions

1. Import and explore the Kickstarter dataset to understand its structure
2. Clean the data to handle missing values and ensure consistency
3. Write SQL queries to analyze the data and uncover trends
4. Visualize the results of your analysis using SQL queries

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

• Proficiency in using SQL for data analysis
• Experience with cleaning and analyzing real-world datasets
• Ability to derive insights from Kickstarter project data

Relevant Links and Resources

• Original Dataset - Kaggle
• SQL Fundamentals skill path

Additional Resources

• SQL Joins Tutorial: Working with Databases

20. Analyzing Startup Fundraising Deals from Crunchbase

Overview

In this beginner-level guided project, you'll step into the role of a data analyst to explore a dataset of startup investments from Crunchbase. By applying your pandas and SQLite skills, you'll work with a large dataset to uncover insights on fundraising trends, successful startups, and active investors. This project focuses on developing techniques for handling memory constraints, selecting optimal data types, and leveraging SQL databases. You'll strengthen your ability to apply the pandas-SQLite workflow to real-world scenarios.

Tools and Technologies

• Python
• Pandas
• SQLite
• Jupyter Notebook

Prerequisites

Although this is a beginner-level SQL project, you'll need some solid skills in Python and data analysis before taking it on:

• Python fundamentals, including variables, data types, and basic syntax
• Familiarity with pandas for data manipulation and analysis
• Basics of data cleaning techniques to handle missing data and inconsistencies
• Exposure to SQL databases and querying data using SQLite

Step-by-Step Instructions

1. Explore the structure and contents of the Crunchbase startup investments dataset
2. Process the large dataset in chunks and load into an SQLite database
3. Analyze fundraising rounds data to identify trends and derive insights
4. Examine the most successful startup verticals based on total funding raised
5. Identify the most active investors by number of deals and total amount invested

Expected Outcomes

Upon completing this guided project, you'll gain practical skills and experience, including:

• Applying pandas and SQLite to analyze real-world startup investment data
• Handling large datasets effectively through chunking and efficient data types
• Integrating pandas DataFrames with SQL databases for scalable data analysis
• Deriving actionable insights from fundraising data to understand startup success
• Building a project for your portfolio showcasing pandas and SQLite skills

Relevant Links and Resources

• Data Analyst in Python career path
• SQL Fundamentals skill path

Additional Resources

• Dataquest Community Discussion on the Project
• SQL Commands: The Complete List (w/ Examples)

Choosing the right data analyst projects

Since the list of data analytics projects on the internet is exhaustive (and can be exhausting!), no one can be expected to build them all. So, how do you pick the right ones for your portfolio, whether they're guided or independent projects? Let's go over the criteria you should use to make this decision.

Passions vs. Interests vs. In-Demand skills

When selecting projects, it’s essential to strike a balance between your passions, interests, and in-demand skills. Here’s how to navigate these three factors:

• Passions: Choose projects that genuinely excite you and align with your long-term goals. Passions are often areas you are deeply committed to and are willing to invest significant time and effort in. Working on something you are passionate about can keep you motivated and engaged, which is crucial for learning and completing the project.
• Interests: Pick projects related to fields or a topic that sparks your curiosity or enjoyment. Interests might not have the same level of commitment as passions, but they can still make the learning process more enjoyable and meaningful. For instance, if you're curious about sports analytics or healthcare data, these interests can guide your project choices.
• In-Demand Skills: Focus on projects that help you develop skills currently sought after in the job market. Research job postings and industry trends to identify which skills are in demand and tailor your projects to develop those competencies.

Steps to picking the right data analytics projects

1. Assess your current skill level
   • If you’re a beginner, start with projects that focus on data cleaning (an essential skill), exploration, and visualization. Using Python libraries like Pandas and Matplotlib is an efficient way to build these foundational skills.
   • Utilize structured resources that provide both a beginner data analyst learning path and support to guide you through your first projects.
2. Plan before you code
   • Clearly define your topic, project objectives, and key questions upfront to stay focused and aligned with your goals.
   • Choose appropriate data sources early in the planning process to streamline the rest of the project.
3. Focus on the fundamentals
   • Clean your data thoroughly to ensure accuracy.
   • Use analytical techniques that align with your objectives.
   • Create clear, impactful visualizations of your findings.
   • Document your process for reproducibility and effective communication.
4. Start small and scale up
   • Begin with small, manageable projects to build your confidence and skills.
   • When you're ready, take on more challenging projects that use machine learning or web scraping, like Predicting Heart Disease or Web Scraping Football Matches.
5. Seek feedback and iterate
   • Share your projects with peers, mentors, or online communities to get feedback.
   • Use this feedback to improve and refine your work.

Remember, it’s okay to start small and gradually take on bigger challenges. Each project you complete, no matter how simple, helps you gain skills and learn valuable lessons. Tackling a series of focused projects is one of the best ways to grow your abilities as a data professional. With each one, you’ll get better at planning, execution, and communication.

Conclusion

If you're serious about landing a data analytics job, project-based learning is key.

There’s a lot of data out there and a lot you can do with it. Trying to figure out where to start can be overwhelming. If you want a more structured approach to reaching your goal, consider enrolling in Dataquest’s Data Analyst in Python career path. It offers exactly what you need to land your first job as a data analyst or to grow your career by adding one of the most popular programming languages, in-demand data skills, and projects to your CV.

But if you’re confident in doing this on your own, the list of projects we’ve shared in this post will definitely help you get there. To continue improving, we encourage you to take on additional projects and share them in the Dataquest Community. This provides valuable peer feedback, helping you refine your projects to become more advanced and join the group of professionals who do this for a living.

Completing Python projects is the ultimate way to learn the language. When you work on real-world projects, you not only retain more of the lessons you learn, but you'll also find it super motivating to push yourself to pick up new skills. Because let's face it, no one actually enjoys sitting in front of a screen learning random syntax for hours on end―particularly if it's not going to be used right away.

Python projects don't have this problem. Anything new you learn will stick because you're immediately putting it into practice. There's just one problem: many Python learners struggle to come up with their own Python project ideas to work on. But that's okay, we can help you with that!

Are You Ready for This?

If you have some programming experience, you might be ready to jump straight into building a Python project. However, if you’re just starting out, it’s vital you have a solid foundation in Python before you take on any projects. Otherwise, you run the risk of getting frustrated and giving up before you even get going. For those in need, we recommend taking either:

1. Introduction to Python Programming course: meant for those looking to become a data professional while learning the fundamentals of programming with Python.
   • This course is part of the Data Analyst in Python career path
2. Introduction to Python Programming course: meant for those looking to leverage the power of AI while learning the fundamentals of programming with Python.
   • This course is part of the Generative AI Fundamentals in Python skill path

In both courses, the goal is to quickly learn the basics of Python so you can start working on a project as soon as possible. You'll learn by doing, not by passively watching videos.

Selecting a Project

Our list below has 60+ fun and rewarding Python projects for learners at all levels. Some are free guided projects that you can complete directly in your browser via the Dataquest platform. Others are more open-ended, serving as inspiration as you build your Python skills. The key is to choose a project that resonates with you and just go for it!

Now, let’s take a look at some Python project examples. There is definitely something to get you started in this list.

Free Python Projects (Recommended):

These free Dataquest guided projects are a great place to start. They provide an embedded code editor directly in your browser, step-by-step instructions to help you complete the project, and community support if you happen to get stuck.

1. Building an Interactive Word Game — In this guided project, you’ll use basic Python programming concepts to create a functional and interactive word-guessing game.

2. Profitable App Profiles for the App Store and Google Play Markets — In this one, you’ll work as a data analyst for a company that builds mobile apps. You’ll use Python to analyze real app market data to find app profiles that attract the most users.

3. Exploring Hacker News Posts — Use Python string manipulation, OOP, and date handling to analyze trends driving post popularity on Hacker News, a popular technology site.

4. Learn and Install Jupyter Notebook — A guide to using and setting up Jupyter Notebook locally to prepare you for real-world data projects.

5. Predicting Heart Disease — We're tasked with using a dataset from the World Health Organization to accurately predict a patient’s risk of developing heart disease based on their medical data.

6. Analyzing Accuracy in Data Presentation — In this project, we'll step into the role of data journalists to analyze movie ratings data and determine if there’s evidence of bias in Fandango’s rating system.

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More Projects to Help Build Your Portfolio:

7. Finding Heavy Traffic Indicators on I-94 — Explore how using the pandas plotting functionality along with the Jupyter Notebook interface allows us to analyze data quickly using visualizations to determine indicators of heavy traffic.

8. Storytelling Data Visualization on Exchange Rates — You'll assume the role of a data analyst tasked with creating an explanatory data visualization about Euro exchange rates to inform and engage an audience.

9. Clean and Analyze Employee Exit Surveys — Work with exit surveys from employees of the Department of Education in Queensland, Australia. Play the role of a data analyst to analyze employee exit surveys and uncover insights about why employees resign.

10. Star Wars Survey — In this data cleaning project, you’ll work with Jupyter Notebook to analyze data on the Star Wars movies to answer the hotly contested question, "Who shot first?"

11. Analyzing NYC High School Data — For this project, you’ll assume the role of a data scientist analyzing relationships between SAT scores and demographic factors in NYC public schools to determine if the SAT is a fair test.

12. Predicting the Weather Using Machine Learning — For this project, you’ll step into the role of a data scientist to predict tomorrow’s weather using historical data and machine learning, developing skills in data preparation, time series analysis, and model evaluation.

13. Credit Card Customer Segmentation — For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.

Python Projects for AI Enthusiasts:

14. Building an AI Chatbot with Streamlit — Build a simple website with an AI chatbot user interface similar to the OpenAI Playground in this intermediate-level project using Streamlit.

15. Developing a Dynamic AI Chatbot — Create your very own AI-powered chatbot that can take on different personalities, keep track of conversation history, and provide coherent responses in this intermediate-level project.

16. Building a Food Ordering App — Create a functional application using Python dictionaries, loops, and functions to create an interactive system for viewing menus, modifying carts, and placing orders.

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Fun Python Projects for Building Data Skills:

17. Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.

18. Find out How Much Money You’ve Spent on Amazon — Dig into your own spending habits with this beginner-level tutorial!

19. Analyze Your Personal Netflix Data — Another beginner-to-intermediate tutorial that gets you working with your own personal dataset.

20. Analyze Your Personal Facebook Data with Python — Are you spending too much time posting on Facebook? The numbers don’t lie, and you can find them in this beginner-to-intermediate Python project.

21. Analyze Survey Data — This walk-through will show you how to set up Python and how to filter survey data from any dataset (or just use the sample data linked in the article).

22. All of Dataquest’s Guided Projects — These guided data science projects walk you through building real-world data projects of increasing complexity, with suggestions for how to expand each project.

23. Analyze Everything — Grab a free dataset that interests you, and start poking around! If you get stuck or aren’t sure where to start, our introduction to Python lessons are here to help, and you can try them for free!

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Cool Python Projects for Game Devs:

24. Rock, Paper, Scissors — Learn Python with a simple-but-fun game that everybody knows.

25. Build a Text Adventure Game — This is a classic Python beginner project (it also pops up in this book) that’ll teach you many basic game setup concepts that are useful for more advanced games.

26. Guessing Game — This is another beginner-level project that’ll help you learn and practice the basics.

27. Mad Libs — Use Python code to make interactive Python Mad Libs!

28. Hangman — Another childhood classic that you can make to stretch your Python skills.

29. Snake — This is a bit more complex, but it’s a classic (and surprisingly fun) game to make and play.

Simple Python Projects for Web Devs:

30. URL shortener — This free video course will show you how to build your own URL shortener like Bit.ly using Python and Django.

31. Build a Simple Web Page with Django — This is a very in-depth, from-scratch tutorial for building a website with Python and Django, complete with cartoon illustrations!

Easy Python Projects for Aspiring Developers:

32. Password generator — Build a secure password generator in Python.

33. Use Tweepy to create a Twitter bot — This Python project idea is a bit more advanced, as you’ll need to use the Twitter API, but it’s definitely fun!

34. Build an Address Book — This could start with a simple Python dictionary or become as advanced as something like this!

35. Create a Crypto App with Python — This free video course walks you through using some APIs and Python to build apps with cryptocurrency data.

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Additional Python Project Ideas

Still haven’t found a project idea that appeals to you? Here are many more, separated by experience level.

These aren’t tutorials; they’re just Python project ideas that you’ll have to dig into and research on your own, but that’s part of the fun! And it’s also part of the natural process of learning to code and working as a programmer.

The pros use Google and AI tools for answers all the time — so don’t be afraid to dive in and get your hands dirty!

Beginner Python Project Ideas

36. Create a text encryption generator. This would take text as input, replaces each letter with another letter, and outputs the “encoded” message.

37. Build a countdown calculator. Write some code that can take two dates as input, and then calculate the amount of time between them. This will be a great way to familiarize yourself with Python’s datetime module.

38. Write a sorting method. Given a list, can you write some code that sorts it alphabetically, or numerically? Yes, Python has this functionality built-in, but see if you can do it without using the sort() function!

39. Build an interactive quiz application. Which Avenger are you? Build a personality or recommendation quiz that asks users some questions, stores their answers, and then performs some kind of calculation to give the user a personalized result based on their answers

40. Tic-Tac-Toe by Text. Build a Tic-Tac-Toe game that’s playable like a text adventure. Can you make it print a text-based representation of the board after each move?

41. Make a temperature/measurement converter. Write a script that can convert Fahrenheit (℉) to Celcius (℃) and back, or inches to centimeters and back, etc. How far can you take it?

42. Build a counter app. Take your first steps into the world of UI by building a very simple app that counts up by one each time a user clicks a button.

43. Build a number-guessing game. Think of this as a bit like a text adventure, but with numbers. How far can you take it?

44. Build an alarm clock. This is borderline beginner/intermediate, but it’s worth trying to build an alarm clock for yourself. Can you create different alarms? A snooze function?

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Intermediate Python Project Ideas

45. Build an upgraded text encryption generator. Starting with the project mentioned in the beginner section, see what you can do to make it more sophisticated. Can you make it generate different kinds of codes? Can you create a “decoder” app that reads encoded messages if the user inputs a secret key? Can you create a more sophisticated code that goes beyond simple letter-replacement?

46. Make your Tic-Tac-Toe game clickable. Building off the beginner project, now make a version of Tic-Tac-Toe that has an actual UI  you’ll use by clicking on open squares. Challenge: can you write a simple “AI” opponent for a human player to play against?

47. Scrape some data to analyze. This could really be anything, from any website you like. The web is full of interesting data. If you learn a little about web-scraping, you can collect some really unique datasets.

48. Build a clock website. How close can you get it to real-time? Can you implement different time zone selectors, and add in the “countdown calculator” functionality to calculate lengths of time?

49. Automate some of your job. This will vary, but many jobs have some kind of repetitive process that you can automate! This intermediate project could even lead to a promotion.

50. Automate your personal habits. Do you want to remember to stand up once every hour during work? How about writing some code that generates unique workout plans based on your goals and preferences? There are a variety of simple apps you can build to automate or enhance different aspects of your life.

51. Create a simple web browser. Build a simple UI that accepts  URLs and loads webpages. PyWt will be helpful here! Can you add a “back” button, bookmarks, and other cool features?

52. Write a notes app. Create an app that helps people write and store notes. Can you think of some interesting and unique features to add?

53. Build a typing tester. This should show the user some text, and then challenge them to type it quickly and accurately. Meanwhile, you time them and score them on accuracy.

54. Create a “site updated” notification system. Ever get annoyed when you have to refresh a website to see if an out-of-stock product has been relisted? Or to see if any news has been posted? Write a Python script that automatically checks a given URL for updates and informs you when it identifies one. Be careful not to overload the servers of whatever site you’re checking, though. Keep the time interval reasonable between each check.

55. Recreate your favorite board game in Python. There are tons of options here, from something simple like Checkers all the way up to Risk. Or even more modern and advanced games like Ticket to Ride or Settlers of Catan. How close can you get to the real thing?

56. Build a Wikipedia explorer. Build an app that displays a random Wikipedia page. The challenge here is in the details: can you add user-selected categories? Can you try a different “rabbit hole” version of the app, wherein each article is randomly selected from the articles linked in the previous article? This might seem simple, but it can actually require some serious web-scraping skills.

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Advanced Python Project Ideas

57. Build a stock market prediction app. For this one, you’ll need a source of stock market data and some machine learning and data analytics skills. Fortunately, many people have tried this, so there’s plenty of source code out there to work from.

58. Build a chatbot. The challenge here isn’t so much making the chatbot as it is making it good. Can you, for example, implement some natural language processing techniques to make it sound more natural and spontaneous?

59. Program a robot. This requires some hardware (which isn’t usually free), but there are many affordable options out there — and many learning resources, too. Definitely look into Raspberry Pi if you’re not already thinking along those lines.

60. Build an image recognition app. Starting with handwriting recognition is a good idea — Dataquest has a guided data science project to help with that! Once you’ve learned it, you can take it to the next level.

61. Create a sentiment analysis tool for social media. Collect data from various social media platforms, preprocess it, and then train a deep learning model to analyze the sentiment of each post (positive, negative, neutral).

62. Make a price prediction model. Select an industry or product that interests you, and build a machine learning model that predicts price changes.

63. Create an interactive map. This will require a mix of data skills and UI creation skills. Your map can display whatever you’d like — bird migrations, traffic data, crime reports — but it should be interactive in some way. How far can you take it?

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Next Steps

Each of the examples in the previous sections built on the idea of choosing a great Python project for a beginner and then enhancing it as your Python skills progress. Next, you can advance to the following:

• Think about what interests you, and choose a project that overlaps with your interests.

• Think about your Python learning goals, and make sure your project moves you closer to achieving those goals.

• Start small. Once you’ve built a small project, you can either expand it or build another one.

Now you’re ready to get started. If you haven’t learned the basics of Python yet, I recommend diving in with Dataquest’s Introduction to Python Programming course.

If you already know the basics, there’s no reason to hesitate! Now is the time to get in there and find your perfect Python project.