5 Data Analytics Projects for Beginners

Written by Coursera Staff • Updated on

Build a job-ready portfolio with these five beginner-friendly data analysis projects.

[Featured image] Woman at computer working on data analysis project

If you’re getting ready to launch a new career as a data analyst, chances are you’ve encountered a common dilemma. Job listings ask for experience, but how do you get experience if you’re looking for your first data analyst job?

This is where your portfolio comes in. The projects you include in your portfolio demonstrate your skills to hiring managers and interviewers. Even if it’s not from a previous data analytics job, this proof of experience in data analysis can help differentiate you from the competition. Populating your portfolio with suitable projects can go a long way towards building confidence that you’re the right person for the job, even without previous work experience.

This article discusses five projects you could include in your data analytics portfolio, especially if you’re just starting. You’ll see some examples of how these projects appear in real portfolios and find a list of public data sets you can use to start completing projects. 

Tip: When you're just starting, think in terms of "mini projects". A portfolio project can feature a partial analysis end-to-end. Instead, complete smaller projects based on individual data analytics skills or steps in the data analysis process.


Data analysis project ideas

As an aspiring data analyst, you’ll want to demonstrate a few key skills in your portfolio. These data analytics project ideas reflect the tasks often fundamental to many data analysis roles. 

1. Web scraping

You’ll find no shortage of excellent (and free) public data sets on the internet, and you might want to show prospective employers that you can find and scrape your own data as well. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not someone has already compiled them.

If you know some Python, you can use tools like Beautiful Soup or Scrapy to crawl the web for interesting data. If you don’t know how to code, don’t worry. You’ll also find several tools that automate the process (many offer a free trial), like Octoparse or ParseHub.

If you’re unsure where to start, here are some websites with interesting data options to inspire your project:

  • Reddit

  • Wikipedia

  • Job portals

Tip: Remember to respect and abide by each website's terms of service when you're scraping data from the internet. Limit your scraping activities so as not to overwhelm a company's servers, and always cite your sources when you present your data findings in your portfolio.


2. Data cleaning

A significant part of the data analyst’s role is cleaning data to prepare it for analysis. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and ensuring data formatting is consistent. 

As you look for a data set to practise cleaning, look for one that includes multiple files gathered from multiple sources without much curation. Some sites where you can find “dirty” data sets to work with include:

  • CDC Wonder

  • Taxi Trajectory

  • World Bank

  • Data.world

  • /r/datasets

Example data cleaning project: This Medium article outlines how data analyst Raahim Khan cleaned a set of daily-updated statistics on trending YouTube videos.

3. Exploratory data analysis (EDA)

Data analysis is all about answering questions with data. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. This could be done separately from or in conjunction with data cleaning. Either way, you’ll want to accomplish the following during these early investigations.

  1. Ask many questions about the data.

  2. Discover the underlying structure of the data.

  3. Look for trends, patterns, and anomalies in the data.

  4. Test hypotheses and validate assumptions about the data.

  5. Think about what problems you could solve with the data.

Example exploratory data analysis project: This data analyst took an existing data set on American universities in 2013 from Kaggle and used it to explore what makes students prefer one university over another.

8 free public data sets for exploratory data analysis

An EDA project is an excellent opportunity to take advantage of the wealth of public data sets available online. Here are eight free data sets to start your explorations.

1. National Centres for Environmental Information: Dig into the world’s largest provider of weather and climate data.

2. World Happiness Report 2021: What makes the world’s happiest countries so happy? 

3. Open Government Data: Explore nearly 600,000 resources aggregated in the Open Government Data portal for India.

4. World Health Organisation COVID-19 Dashboard: Track the latest coronavirus numbers by country or WHO region.

5. Latest Netflix Data: This Kaggle data set (updated in April 2021) includes movie data broken down into 26 attributes.

6. Google Books Ngram: Download the raw data from the Google Books Ngram to explore phrase trends in books published from 1960 to 2015.

7. NASA: If you’re interested in space and earth science, see what you can find among the tens of thousands of public data sets made available by the US space agency NASA.

8. Yelp Open Dataset: See what you can find whilst exploring this collection of Yelp user reviews, check-ins, and business attributes.


4. Sentiment analysis

Sentiment analysis, typically performed on textual data, is a technique in natural language processing (NLP) for determining whether data is neutral, positive, or negative. You also may use it to detect a particular emotion based on a list of words and their corresponding emotions (known as a lexicon). 

This type of analysis works well with public review sites and social media platforms, where people are likely to offer public opinions on various subjects.

To get started exploring what people feel about a certain topic, you can start with sites like:

  • Amazon (product reviews)

  • Facebook

  • Twitter

  • Reddit

  • News sites

Example sentiment analysis project: This blog post on Towards Data Science explores the use of linguistic markers in Tweets to help diagnose depression.

5. Data visualisation

Humans are visual creatures. This makes data visualisation a powerful tool for transforming data into compelling stories to encourage action. Great visualisations are not only fun to create, they also have the power to make your portfolio look beautiful.

Example data visualisation project: Data analyst Hannah Yan Han visualises the skill level required for 60 sports to determine which is toughest.

Five free data visualisation tools

You don’t need to pay for advanced visualisation software to start creating stellar visuals, either. These are just a few of the free visualisation tools you can use to start telling a story with data:

1. Tableau Public: Tableau ranks among the most popular visualisation tools. Use the free version to transform spreadsheets or files into interactive visualisations (here are some examples from 2022). 

2. Google Charts: This gallery of interactive charts and data visualisation tools makes it easy to embed visualisations within your portfolio using HTML and JavaScript code. A robust Guides section walks you through the creation process.

3. Datawrapper: Copy and paste your data from a spreadsheet or upload a CSV file to generate charts, maps, or tables—no coding required. The free version allows you to create unlimited visualisations to export as PNG files.

4. D3 (Data-Driven Documents): With some technical know-how, you can do a ton with this JavaScript library.

5. RAW Graphs: This open source web app makes it easy to turn spreadsheets or CSV files into a range of chart types that might otherwise be difficult to produce. The app even provides sample data sets for you to experiment with.


Bonus: End-to-end project

There’s nothing wrong with populating your portfolio with mini-projects highlighting individual skills. But if you’ve scraped the web for your own data, you might also consider using that same data to complete an end-to-end project. To do this, take the data you scraped and apply the main steps of data analysis to it—clean, analyse, and interpret. 

This can show a potential employer that you have the essential skills of a data analyst and know how they fit together.

Three data analysis projects you can complete today

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 need a little direction for your next project, consider one of these data analysis Guided Projects on Coursera that you can complete in under two hours. Each includes split-screen video instruction; you don’t have to download or own any special software.

1. Exploratory Data Analysis with Python and Pandas: Apply EDA techniques to any table of data using Python.

2. Twitter Sentiment Analysis Tutorial: Clean thousands of tweets and use them to predict whether a customer is happy or not.

3. COVID19 Data Visualisation Using Python: Visualise the global spread of COVID-19 using Python, Plotly, and a real data set.

Next steps: Get started in data analysis

Another great way to build some portfolio-ready projects is through a project-based online course. You can complete hands-on projects and a case study to share with potential employers by completing the Google Data Analytics Professional Certificate on Coursera. Along the way, you have opportunities to learn and practise skills, including data collection, data cleansing, and data visualisation.

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