5 Data Analytics Projects for Beginners

If you’re getting ready to launch a new career as a data analyst, you can build a portfolio that demonstrates your skills in a more interactive way than a resume or a CV. 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 building confidence that you’re the right person for the job, even without previous work experience.

Explore five types of projects you should include in your data analytics portfolio, especially if you’re just starting out. You’ll see some examples of how these projects are presented 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 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 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, explore  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: Edward Crowder and Jay Lanisquot explored data mining the darknet to assess cybercrime in Canada for an Honours Bachelor's Capstone Project. The duo used a multi-stage processing pipeline in their research, including data crawling, web scraping, and parsing of information from web forums, chatrooms, and emails. 

2. Data cleaning

A significant part of your role as a data analyst is cleaning data to make it ready to analyse. Data cleaning (also called data scrubbing) is the process of evaluating and removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data 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:

• Public Health Agency of Canada (PHAC)

• Government of Canada's Open Government Portal

• World Bank

• Data.world

• /r/datasets

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 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.

10 free public data sets for EDA

An EDA project is an excellent time to take advantage of the wealth of public data sets available online. Explore 10 fun and free data sets to get you started in your explorations.

1. Government of Canada Historical Climate Data: Dig into Canada’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 data sets made available by NASA.

4. Statistics Canada: Learn more about the people and economy of Canada with the latest census data from 2021.

5. Royal Canadian Mounted Police (RCMP): Explore crime data collected by Canadian law enforcement.

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

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

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. Toronto Open Data: Discover New York City through its many publicly available data sets on topics like the Toronto beach observations to city vehicle availability.

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

• Amazon (product reviews)

• Rotten Tomatoes (movie reviews)

• Facebook

• Twitter

• News sites

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, they also have the power to make your portfolio look beautiful.

Five free data visualisation tools

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

1. Tableau Public: Tableau ranks among the most popular visualisation tools. Use the free version to transform spreadsheets or files into interactive visualisations (examples from April 2021).

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 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

You can populate your portfolio with mini projects highlighting individual skills. However, 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.

Three data analysis projects you can complete today

The amount of data available is staggering, and you can do a lot with it. 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, and you don’t have to download or own any special software.

• Data Analysis in Python: Using Pandas DataFrames

• Introduction to Data Analysis using Microsoft Excel

• Overview of Data Visualization

Next steps: Get started in data analysis

Building a portfolio with data analytics projects is a great way to demonstrate your skills to potential employers when you don’t necessarily have a lot of professional experience. Another great way to build some portfolio-ready projects is through a project-based online course. 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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