5 Data Analytics Projects for Beginners

If you’re getting ready to launch a new career as a data analyst, chances are you’ve encountered an age-old dilemma. Job listings often require experience, but how do you get said experience if you’re seeking your first data analyst job? This is where your portfolio comes in.

The projects you include in your portfolio demonstrate your skills and experience—even if it’s not from a previous data analytics job—to hiring managers and interviewers. Populating your portfolio with the right projects can go a long way toward communicating that you’re the right person for the job, even without previous work experience.

Explore five essential data analytics portfolio projects, particularly if you’re new to the field.  See some examples of how people present these projects in real portfolios, and find a list of public data sets you can use to start completing projects.

Tip: When you’re just starting out, think in terms of “mini projects.” A portfolio project doesn’t need to feature a complete 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 analyst roles. 

1. Web scraping

While you’ll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you’re able to 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 they’ve already been compiled.

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, below are some websites with interesting data options to inspire your project:

• Reddit

• Wikipedia

• Job portals

Tip: Anytime you’re scraping data from the internet, remember to respect and abide by each website’s terms of service. 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.

Example web scraping project: Hosting and cloud partner IONOS provides an example using BeautifulSoup to search through the available online listings of used cars—specifically the Volkswagen Beetle. 

2. Data cleaning

A significant part of your role as a data analyst is cleaning data to make it ready for analysing. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and ensuring consistent data formatting.  

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:

• London Air

• Data.gov.uk

• World Bank

• Data.world

• /r/datasets

Example data cleaning project: Consider this series of straightforward data cleaning projects, as outlined by York University. They explore different data types and formats, and methods to clean and reorganise data for analysis and visualisation.

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 in conjunction with, or in conjunction with, data cleaning. Either way, you’ll want to accomplish the following during these early investigations.

1. Ask lots of 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 potentially solve with the data.

Example exploratory data analysis project: This dataset provides spatial boundaries for UK Retail Centres, along with information on supply vulnerability, online exposure, a clone town measure (which assesses retail centre diversity), E-resilience, and hierarchical classification. 

10 free public datasets for EDA

An EDA project is an excellent time to take advantage of the wealth of public datasets available online. Discover 10 fun and free globally available datasets to kickstart your explorations

1. National Centers 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. NASA: If you’re interested in space and earth science, see what you can find among the tens of thousands of public datasets made available by NASA.

4. UK Census: Learn more about the people and economy of the UK with the latest census data from 2024.

5. FBI Crime Data Explorer (CDE): Explore crime data collected by more than 18,000 law enforcement agencies in the US.

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

7. Latest Netflix Reviews Data: This Kaggle dataset (updated daily) includes information about the reviews given by Netflix users on Google Play Store.

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

9. MPS Business Crime Dashboard Data: This Metropolitan Police Services dataset is updated monthly at the beginning of every month.

10. Yelp Open Dataset: See what you can find while 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. It may also be used 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 start exploring people’s opinions on a certain topic, consider beginning with sites like:

• Amazon (product reviews)

• Rotten Tomatoes (movie reviews)

• Facebook

• Twitter/X

• UK news sites

Example sentiment analysis project: This study via ResearchGate, entitled Detecting the magnitude of depression in Twitter/X users, uses sentiment analysis.  The main aim is to understand depression levels in users and correlate the scores to the main data. 

5. Data visualisation

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

Example data visualisation project: Tableau outlines the 10 Of The Best Data Visualization Examples From History & Today, including The Napoleon March Map, to Disney Film Dialogue (broken down by gender). 

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 (examples celebrating the 2024 Vizzie Award Winners).

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 a bit of technical know-how, you can do a lot 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 not only have the essential skills of a data analyst but that you know how they fit together.

Two data analysis projects you can complete today

There’s a lot of data out there and a lot you can do with it. If you need a little direction for your next project, consider one of the below data analysis Guided Projects on Coursera that you can complete in under two hours. Each includes split-screen video instruction, plus 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.

Get started in data analysis with Coursera

Now that you’re up to speed regarding five accessible data analytics projects for beginners—including web scraping, data cleaning, EDA, sentiment analysis, and data visualisation—another great way to build some portfolio-ready projects is through a project-based online course. Consider the Google data analysis course suggested below.

By completing the Google Data Analytics Professional Certificate on Coursera, you can complete hands-on projects and a case study to share with potential employers.

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