December 5, 2022

20 Fun (and Unique) Data Analyst Projects for Beginners in 2023

If data is the new oil, the insights derived from it are the refined products! As more and more organizations are willing to extract insights from their data, the demand for qualified data analysts continues to grow--not only in the number of positions available, but also in the types of data analyst jobs that exist.

If you've ever thought about pursuing a career as a data analyst, there will be plenty of opportunities for you in the future if you get the necessary skills right now.

In this article, we'll share with you 20 data analyst projects for beginners that you can use to build your portfolio. Your data analytics portfolio will help you showcase your skills to potential recruiters and stand out from the crowd.

These projects will cover the most sought-after data analysis skills and the most frequently used data analysis tools. Namely:

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

1. Build an Excel Call Center Dashboard

The Microsoft Excel (or Excel) spreadsheet has been around for more than 30 years and is still a great tool for learning about data. Many organizations continue to use Excel spreadsheets for everyday tasks. As Excel is a widely-used tool for data analysis, a data analyst should have excellent Excel skills. Excel can be used to clean, store, analyze, model, visualize and generate reports on data. The functionality of Excel can be extended with add-ins for performing even more advanced analysis. The Analysis ToolPak add-in for complex statistical and engineering analysis is just one example.

In this data analyst project with Excel, you'll learn how to preprocess data in Excel and change them to your preferred data types. Furthermore, you'll learn how to create pivot tables and build charts and maps from them. In addition, you'll learn how to design the structure and background of your dashboard and insert your charts and maps into the dashboard. Finally, you'll learn how to use the filter function and slicers to make your dashboard interactive.

Here are the links to the tutorial and dataset for this project:

Excel is a powerful tool and can be used for advanced data analysis. Learn advanced pivot table techniques, forecasting, modeling, visualization, and more in our Analyzing Data with Excel skill path, and move from beginner to advanced in Excel.

2. Answering Business Questions Using SQL

Excel is a great application, but it has limitations. It can't handle very large datasets, and data-wrangling and analysis can easily turn out to be a painful exercise.

Structured Query Language (SQL) overcomes these limitations of Excel. SQL can be used to join several tables in a relational database to get a very large dataset. Performing data-wrangling and analysis using SQL is also very easy and fast.

SQL is the most in-demand data analysis skill, appearing in sixty-one percent of data analysts' job postings.

In this project, you'll learn and practice the SQL data analysis workflow by answering several business questions running SQL queries on Jupyter notebook. You'll learn how to read and use a database schema and how to query a database to join tables and return specific information from them.

The Chinook record store is partnering with a record label. Help them decide which artists to invest in by performing analysis to determine the most popular genre in the US. Then, help the record store identify opportunities outside the US by analyzing sales by country. Perform data analysis to help Chinook record store understand its sales employees’ performance and to help them decide whether to invest in albums or individual tracks.

Here are the links to the source code, instructions, and database for this project:

  • Answering business question source code
  • 10 interesting SQL projects to help you get more practice.

    Knowledge of SQL is a fundamental data analysis skill that you'll find in most data analysts' job postings. Take a deep dive into SQL programming and gain the skills you need for success as a data analyst with our SQL Fundamentals skill path.

    3. Customers and Products Analysis Using SQL

    You ran SQL queries on a Jupyter notebook in the previous project. Jupyter notebooks are very popular for completing data projects because they allow you to create and share documents containing codes, equations, texts, and visualizations in one place. Other times, you may work in a SQL Integrated Development Environment (IDE) like DB Browser for SQLite and MySQL Workbench.

    In this data analysis project, you'll learn how to run queries on your machine using the DB Browser for SQLite IDE. You'll explore the scale model car sales database. You'll write queries to analyze product performance and design a restocking strategy. You'll analyze your customers' behaviors and spending habits and design a customized marketing and communication strategy that maximizes customer lifetime value and minimizes marketing costs.

    Here are the links to the source code, instructions, and database for this project:

    You can find our other SQL projects listed here. Explore and complete these projects to take your SQL skills to the next level.

    5. Analyzing Wikipedia Pages

    So far, you've worked with Excel and database files. There are other file formats that your data can come in. Let’s assume your data is available on the internet on several web pages. You can either scrape the website to obtain your data or save the web pages in HTML format.

    In this project, you'll learn how to analyze HTML documents with data saved from Wikipedia pages. You'll learn how to clean your data by removing headers, footers, and extraneous markups. Working with the BeautifulSoup library, you'll learn how to extract your data from the HTML pages using specific tags. Lastly, you'll learn how to clean and analyze data extracted from each HTML document.

    Here are the links to the source code and instructions for this project:

    Explore our Web Scraping Football Matches from the English Premiership project to get practice.

    Web scraping is an important skill for any data analyst to have in their toolbox. We recommend our API and Web Scraping in Python course to help you get started.

    7. Cleaning NBA Stats Data with Python and Pandas

    Real-world data and data scraped off the internet aren't clean. They require preprocessing to put them in formats that statistical, machine learning, and visualization libraries can process.

    This project is a continuation of the previous project. Here, you'll work with CSV files containing data you scraped from several web pages. Working with the pandas library, you'll learn how to remove extraneous characters from your data, handle missing values, convert features to the appropriate data types, select the subsets of features you need from each DataFrame, and merge them. At the end of the project, you'll have preprocessed data ready for machine learning and statistical analysis.

    Here are the links to the source code and video tutorial for this project:

    You'll spend most of your time cleaning and preprocessing data as a data analyst. Enroll in our Data Cleaning in Python skill path and learn the skills for efficiently cleaning, transforming, and visualizing your data.

    8. Finding the Best Markets to Advertise In

    After mastering Excel and SQL, the next most important tool that a data analyst must add to their toolkit is knowledge of a programming language. Python and R are the most popular programming languages for data analysis.

    Programming languages are simply tools to get your data analysis job done. We have no preference between Python and R. You can learn both on our platform. This article presents an objective comparison between R and Python to help you decide which one you should learn.

    Statistical knowledge is listed as one of the 8 Data Analysis skills that employers want data analysts to demonstrate. So, aspiring data analysts must be able to perform statistical analysis in one or more of these programming languages.

    In this data analysis project, you'll use the Python programming language to perform probability and statistical analysis to help an e-Learning company find the best markets to advertise their programming courses.

    You'll perform descriptive statistics--estimating the mean, median, mode, variance, and frequency distribution--to better understand your data.

    You'll also perform prescriptive statistics by investigating the company’s potential customers’ locations, density, and spending power. In the process, you'll learn how to deal with outliers that disproportionately affect and skew the result of your prescriptive analysis.

    Not dealing with outliers results in misleading interpretations. If someone acts on your misleading results, the outcomes are usually dire--incurring financial losses, for example. You also hurt your reputation as a competent data analyst.

    Finally, you'll recommend the markets this e-Learning company should advertise in from the results of your statistical analysis.

    Here are the links to the source code, instructions, and data for this project:

    Broaden your knowledge of probability and statistics and find other interesting projects in our Probability and Statistics with Python skill path.

    9. Mobile App for Lottery Addiction

    Many statistical and machine learning concepts are implemented using some fundamental knowledge of probability. It is not surprising that knowledge of probability and statistics are core skills required of a data analyst.

    In this project, we'll take a deep dive into the world of probability by investigating the odds of winning the lottery. You'll help a medical institute specializing in treating gambling addiction develop the logic for its mobile app. The mobile app will help people better estimate their chances of winning the lottery. You'll use probability theory to estimate the chance of winning the jackpot with one or multiple tickets, and the chance of smaller winnings with matching numbers between 2 to 5.

    Here are the links to the source code and instructions for this project:

    Your day-to-day job as a data analyst will involve predictive analytics. Knowledge of machine learning will enable you to perform these tasks better. We recommend our Intermediate Machine Learning in Python skill path to sharpen your predictive modeling skills.

    11. Predict the NBA MVP

    Predictive analysis is part of the day-to-day tasks of a data analyst. There are as many machine learning algorithms as there are predictive analysis tasks. We'll explore more machine learning algorithms for predictive analysis tasks using the NBA stats data from project 7.

    In this project, we'll use the NBA stats data to predict the Most Valuable Players (MVP) in the NBA. We'll use ridge regression and random forest regression algorithms. First, we'll load the CSV file into a pandas DataFrame, clean, and transform the features. Next, we'll learn how to choose predictors to prevent data leakage--one of the major problems in machine learning.

    Next, we'll train our regression algorithms and choose appropriate metrics to evaluate model performance. Lastly, we'll create a backtest to validate our model performance over some time.

    Here are the links to the source code, video tutorial, and data for this project:

    12. Analyzing Movie Ratings with R

    There is an ongoing debate on which programming language is the most suitable for data science and analytics. Although Python is the most popular programming language, R is optimized for statistical analysis, scientific computing, and visualization.

    Why should you learn R? The R programming language is popular in many organizations. It also has many rigorously-tested packages for performing data analysis tasks. Earlier in the year, my team had to use an R package for part of an advanced econometrics modeling task because we couldn't find a good Python equivalent.

    Learning R is worth it.

    In this project, you'll learn how to scrape and extract data from a webpage with the rvest package. You'ill also learn how data preprocessing is done in R. You'll parse data in the appropriate data types, remove extraneous characters, and handle missing values. Finally, you'll load your data into a dataframe and visualize their distribution using the ggplot package.

    Here are the source code, instructions, and web page to be scrapped for this project:

    Whether R or Python is your preferred language, we’ve got you covered. Perfect your web scraping with R skills by enrolling in our APIs and Web Scraping with R skill path.

    13. Winning Jeopardy with R

    Your work as a data analyst may involve investigating the results of experiments. A company recently changed its user interface and noticed people spend more time on its website. Your task may be to investigate whether this is a result of changes made to the user interface. To perform your investigation, you draw null and alternative hypotheses. Then you test whether the observation from the data is statistically significant or due to chance.

    In this project, you'll learn how to formulate hypotheses and test them for statistical significance. You'll be working with data from the popular TV show “Jeopardy!” You'll test whether a participant appearing on the show should focus on a particular subject area, certain types of questions, and questions with a certain monetary value attached.

    Here are the source code, instructions, and data for this project:

    Learn more about hypothesis testing and find other cool statistics and probability projects in our Probability and Statistics with R skill path.

    14. Predicting Condominium Sale Prices with R

    Data analysts often find themselves working on predictive analysis tasks. The R programming language is also great for predictive analytics. It has many well-developed packages for machine learning.

    In this data analysis project, you'll learn how to use R machine learning packages for making predictions. You'll be working with New York City property sales data. You'll preprocess and explore the data to get a deeper understanding of it. Next, you'll perform linear regression modeling to predict the price of condominiums in each borough in New York City and learn how to interpret your model’s summary statistics.

    Here are the links to the source code, instructions, and data for this project:

    Take our Linear Regression Modeling in R and Machine Learning Fundamentals in R courses to learn more about predictive modeling with machine learning in R.

    15. Analyzing Forest Fire Data with R

    Data visualization is a very important data analysis skill. We can easily identify patterns and trends in data when they are presented visually. Data visualization is also a very good way to communicate the results of your analysis.

    The R programming language is a very powerful tool for data visualization. It has lots of data visualization packages, and with a few lines of code, we can create any type of visualization.

    In this project, you'll use the ggplot package to perform exploratory data analysis with the forest fire dataset. You'll create univariate and bivariate plots to better understand the data. You'll learn how to use visualization plots to identify outliers. You'll create graphical plots to answer questions like what time of the month most fires occur and what factors are responsible for severe forest fires.

    Here are the links to the source, instructions, and data for this project:

    A chart is worth ten thousand words. Get the crucial data visualization skills you need to succeed as a data analyst with our Data Visualization with R skill path.

    16. Customer Analysis Dashboard with Tableau

    Data analysts have to share their findings with the stakeholders of their projects. Data visualization tools help data analysts effectively communicate the results of their analyses.

    Although we can visualize data with Excel, R, and Python, business intelligence (BI) tools like Tableau and Power BI have their advantages. They are scalable, fast, and highly integrated. These BI tools can be easily integrated with Excel, databases, cloud storage, and other document formats. They can also be used to create highly interactive dashboards hosted on their servers. With a single click, you can access these dashboards.

    In this tutorial, you'll create visualizations with Tableau using customers’ data. You'll learn how to: format columns in Tableau; create maps, bar graphs, histograms, scatter plots, donut charts, and butterfly charts; and build an interactive dashboard.

    Here are the links to the video tutorial, dashboard, and data for this free data analyst project with Tableau:

    17. Airbnb Dashboard with Tableau 1

    Practice makes perfect. We'll perform more descriptive analysis with Tableau with Airbnb data from the New York City boroughs. This project is two-fold. In this section of the project, you'll transform columns to the appropriate data types and take a deep dive into visualizations for geographical features.

    At the end of the project, you’ll have created a map of the average rental prices of Airbnb apartments, based on their zip codes, and horizontal bar graphs of regions with the highest ratings and bed availability.

    Here are the links to the video tutorial, dashboard, and data for this free data analyst project with Tableau:

    18. Airbnb Dashboard with Tableau 2

    In this section of the project, we'll make a times series chart to analyze average rental price changes. You'll learn chart-formatting techniques that will enable you to create visualizations that communicate your results accurately. Next, you'll learn how to design a dashboard with all the charts that you've created and how to use filters to make your dashboard interactive.

    Here are the links to the video tutorial, dashboard, and data for this free data analyst project with Tableau:

    The ability to communicate your results well is a crucial data analysis skill. Gain the skills you need to identify patterns and trends in your data and communicate the results of your analysis through visualization in our Data Visualization with Tableau skill path.

    19. Power BI Report and Dashboard for Domino's Pizza

    Power BI is a business intelligence tool developed by Microsoft. It is a popular data visualization tool used by data analysts to communicate their insights from data. Microsoft products are used in most organizations. One of the advantages of using Power BI is the easy integration with other Microsoft office products and services.

    In this project, you'll create a Power BI report and dashboard with Domino's Pizza sales data. You'll learn how to import data into Power BI, transform your columns to the appropriate data types, and delete unwanted columns. Next, you'll learn how to manage relationships and use Power BI’s Data Analysis Expression (DAX) to perform calculations. Then, you'll learn how to create a dashboard and generate reports in Power BI.

    Here is the link to the tutorial and data for this free data analyst project with Power BI:

    20. Zomato Sales Dashboard with Power BI

    Here's another Power BI project to strengthen your skills. In this project, you'll act as a data analyst for a client who wants to set up a restaurant business in Bangalore city. You'll create an interactive dashboard that will enable the client to make an informed decision that will maximize profitability. Your dashboard will help the client choose the type of restaurant, the preferred location, and the cuisine. The dashboard will tell the client the average profitability and customer spending, and help them identify the potential competition.

    Here are the links to the video tutorial and data for this free data analyst project:

    Strengthen your data analysis and visualization skills in Power BI by enrolling in our Analyzing Data with Microsoft Power BI skill path. This course was developed in collaboration with Microsoft, and it contains all you need to ace the PL-300 Microsoft Power BI Analyst certification exam.

    Takeaways

    In this article, we've discussed 20 interesting data analyst projects that cover both the skills and tools data analysts should have. These projects are must-haves for your data analyst portfolio because they demonstrate to recruiters that you possess the skills required to succeed as a data analyst.

    The data analytics career is expanding and there are different kinds of analyst roles. If you're new to data analysis and haven’t learned the basics yet, we recommend our Analyzing Data with Excel, SQL skills, Python Basics for Data Analysis, and Data Visualization with Tableau skill paths. You'll learn the most in-demand skills to land your first job as an analyst.

    Learn practical Excel, SQL, and Power BI skills and become a business analyst with our Business Analyst career path. Knowledge of a programming language will enable you to take your data analysis skills up a notch. We recommend that aspiring data analysts sign up for our Data Analyst in Python and Data Analyst in R career paths.

    As you make progress in your career as an analyst, you'll work in different data analytics roles and use different tools. In this article, we've discussed data analysis projects that cut across the skill spectrum required of data analysts. As they say, “luck is what happens when preparation meets opportunity.” Complete these portfolio projects and enroll in our career and skill paths, and maximize your chances of getting any data analytics role.

Aghogho Monorien

About the author

Aghogho Monorien

Aghogho is an engineer and aspiring Quant working on the applications of artificial intelligence in finance.

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