10 Project Ideas for Beginner Data Analysts
There's always this Catch-22 when talking about switching careers. How you can't get hired because you don't have experience but you can't get experienced if you don't get hired. It Especially for those who are transitioning from another career into the data field. But there's a few ways to get experience without a professional role. In Albert Bellamy's LinkedIn Post he gives some examples: volunteer for a nonprofit, do contracts on Upwork, or creating content.
Another popular method is to create personal portfolio projects. This is one of the steps in Alex The Analyst's video, How To Become A Data Analyst In 2022. Personal projects are one of the best ways to land (entry level) jobs in data. Luke Barousse's video PROJECTs that Landed Data Jobs for My Subscribers goes into a few projects. it lets you demonstrate your relevant skills (SQL, Tableau, Python) without needing professional experience. And it gives you something to talk about in your interviews.
It's even better if the project is based on something you're passionate about like a hobby or a cause. Plenty of people have analyzed the Titanic dataset in Kaggle, but how many have analyzed the network of The Witcher (more of a data science project but still relevant)? Not many. The more specific and unique, the better.
You can either pick your topic first and then decide on what tools you want to use (SQL, Tableau, Excel) or you can decide to focus on a particular tool (SQL or Tableau). Some of these may be specific for certain tools, others you can use whatever tools you see fit.
Below are ten portfolio project ideas with examples (and links sources, if applicable):
#1 Analyze your weightlifting data
Keep track of reps, sets and weight, analyze your maximum weight and PRs.
Example: Nick Green's analysis of his own weightlifting data to see why he improved on certain lifts.
#2 Use a given dataset from a website to practice your skills
Use a dataset from places like Kaggle or Maven Analytics and participate in their challenges to practice your own data analysis skills. Usually these have specific goals for each challenge and dataset.
#3 Analyze your posts on social media
Using metrics: likes, comments, views, etc., see what posts do well or not, see if there's a pattern.
#4 Webscrapping websites/job postings to see what skills are most requested
Create a dashboard for these top skills, education, and tools.
#5 Analyze sports performance
Track to see competitions won or lost, goals made or hits you strike out on.
#6 Analyze public data sets for exploratory data analysis
These are public and free, there's also people out there who've done this so you can check out their work for inspiration, a few of the datasets are: World Happiness Report, US Census, and Netflix Data (source).
#7 Track the time you've spent learning a specific skill
Pick any skill you're interested in learning like guitar, then record the amount of time you've spent learning that skill or learning in general, then you can create a dashboard or graphic with that information (it's a highly visual project and showcases your design work).
Example: My 2021 Wrapped Project, which is based on the Spotify Wrapped for 2021.
#8 Try to predict/analyze a customer's behavior on a site
Using a public dataset, webscrapping (if allowed), or from a competition, analyze a customer's data to identify trends in their spending. This is a practical problems, many businesses hire data analysts to analyze this type of data to help attract or retain customers.
Example: James Le's Instacart Market Basket Analysis where he tries to predict which products will be in a user's next order.
#9 Analyze Your Fitness Data from a Fitness App
Analyze your fitness data from an app like Fitbit or Apple, look at your steps, floors climbed, mileage, activity, calories. Try to see the trends in your data (what days/times you exercise more or less).
Example: Jessie-Raye Bauer who explored her Fitbit Data using Fitbit's API.
#10 Analyze a type of media and try to distinguish trends
Types of media could be: magazines, books, movies, TV shows. You could analyze for movies/tv shows the budget, actors, directors, genre. See what correlates and identify trends. Or analyze a specific series and identify how the characters interact with each other, how many times a character appears in the show, and more.
If you want more data analytics/science resources check out these creators: