How to Build a Data Analytics Portfolio
With a real example of a Product Funnel analysis I did!
Got BDE?⚡️
Hi BDEs!⚡️
Today I’m finally tackling one of the MOST ASKED questions I get: Can you show an example of a data analytics portfolio?! I actually had to do quite a bit of work in GitHub to prep for this because I haven’t touched my personal coding projects and portfolio in a WHILE, but I did it for y’all ❤️
Social Highlights⚡️
How to Build a Data Analytics Portfolio
*sigh* Everyone says you NEED a data analytics portfolio, but no one actually tells you how to build one. It’s almost become a cop out that people say without actually giving you any real, actionable info! Nowadays in today’s ✨HORRIBLE✨ job market, so many people have portfolios because they’ve heard the rhetoric and want to stand out, which means that you need a really good one to stand out.
So let’s talk about it…
Where should you build a data analytics portfolio?
A lot of people will tell you to build these fancy websites and webpages. There’s NOTHING wrong with that if you’re feeling fancy and have a creative side to you. But by no means do you need to do anything that hands on. Any data person or hiring manager will be fluent and comfy in GitHub, so don’t overthink it by trying to impress them with fluffy and fancy platforms. Keep it simple and focus on the content over everything else. Here’s my GitHub🔗 which acts as my portfolio:
DISCLAIMER: My GitHub has not gotten a lot of love lately, so please don’t look at the whole thing as an example. However, I did create a repository, Product Funnel SaaS FinTech🔗, that is formatted to be a good example of a portfolio project for you to view ❤️ So don’t go look at my old projects from college and my first job and roast me 😉
What types of projects should you include?
I highly recommend including pillars that show off your different skills and tools:
An EDA: Shows your curiosity and ability to find insights
A Dashboard: Shows your data viz and business acumen skills
Funnel Analysis: Shows you can think about the bigger picture and translate data into business questions
A full stack project: Shows you can do a variety of data skills from beginning to end of a project
By no means does this mean you need exactly 4 projects— you could tackle all of these pillars in 1 project if you wanted. But based on my experience in data analytics, these types of projects will show off the right skills you need to get and pass interviews. The example I’m showing you today is a FUNNEL ANALYSIS— my fave 😉
How to structure your GitHub repository:
A repository is a fancy folder where you can store all of your project files. You want a repository for each project, and then you can put in all relevant files such as PDFs, excel/csv files, Python & SQL code, etc. so it’s easy to keep things organized. This will make it really easy for people such as hiring managers and recruiters to see your projects and what type of skills you have.
ReadMe Page:
The ReadMe page is the MOST IMPORTANT part of your repository because it’s featured on the front of the page, and sometimes it’s the only thing people look at. Very often people don’t have the time or interest to dig through all your code— they just wanna see a quick overview of what you did and how. This is where the ReadMe comes in. It’s your best superpower to have in your portfolio, so don’t skip this or underestimate the value. It MIGHT just be what gets you the job!
Here’s what you want to include:
An Executive Summary: Assume the reader won’t read everything and list everything you want them to know here! What did you accomplish and how? Tie in the skills and business impact— what do you recommend?
Business Problem: Set up the problem and what you want to accomplish. If you’re doing a fake project, make one up! Showing that you’re thinking about the business and problem will really show that you can connect the data to the real world. Bonus points if you include graphics or pictures to help set up the issue!
Methodology: Explain high level what you did. Did you write a query? Build a dashboard? Perform statistical tests?
Skills: This is where you want to include all the shiny buzzwords that recruiters will be looking for— SQL, Python, etc! What skills did you use to accomplish this? Bonus points if you go into more detail by providing specific examples and libraries like CTEs or pandas.
Results & Business Recommendation: Summarize what you found out, learned, or accomplished, and use visualizations when possible. Always use as many quantifiable numbers and show the business impact when you can. And think beyond dollar signs $$$— sometimes the business impact can be time or resources saved, so be sure to think through those possibilities too. And of course, providing business recommendations is what will really make you stand out. What action would you take based on what you learned? What stakeholder would be most interesting to talk to? Show you have business acumen!
Next Steps: List the next steps or things you would look into if you had more time. You always want to go the extra effort to think about the bigger picture and deeper questions. Plus, this may just convince the hiring manager to bring you on to do the next steps 😉
Building a data portfolio doesn’t have to be scary. Check out my Product Funnel SaaS FinTech🔗
Check out Big SQL Energy 🔗— my intermediate SQL course packed with interview questions, portfolio project building, and experience on a realistic data model with a modern tech stack! You’ll also get access to the exclusive Big Data Energy Discord community to network, ask questions, and share resources.
Big SQL Energy
I finally launched my very own SQL course called Big SQL Energy (I mean DUH, what else would I call it?!) which is designed to prepare you for Data Analytics interviews whether you’re trying to get your first data job or want to level up to the senior level.
In this course, we’ll work together on some intermediate SQL, practice common SQL interview questions, and build a solid portfolio project (which is an exact replica of the one that has landed me my last 2 6-figure data analyst jobs in tech).
This isn’t a typical boring course where you pull random data and have no idea what you’re doing, leaving you ill-equipped for your job search. This course is based on real business problems I’ve solved on the job using a proprietary dataset (yes I typed every. single. cell with my bare hands) based on a typical SaaS tech company data model. No Titanic or Iris datasets will be found in my course.
We’ll also be using Hex Data Science notebooks and a Snowflake data warehouse, which let’s be honest, will look ✨ sexy ✨ on your resume.
So if you wanna learn:
Joins
CTEs
Subqueries
Window Functions
Set Operations (like union)
Self Joins
Case
Optimization
Temp Tables & Views
Who should take Big SQL Energy?
Current Data Analysts, Business Analysts, Data Scientists who want to level up their technical skills for a more technical role
Career transitioners who want to transition into a data role and have some basic data knowledge
Managers and leadership who want to understand more about what their team does and get hands-on experience
Students who don’t feel like their schooling has prepared them well for technical interviews or portfolio-building.
If you have any questions, EMAIL courses@bdeanalytics.com or message me on social media. Happy to help you make the most-informed choice for you.
Well, I’ll c u next tuesday!
-xoxo,
jess💕
WHO AM I?⚡️
I'm Jess Ramos, a content creator, instructor, and leader in the data world. I'm super passionate about data and especially SQL! My 9-5 is being a Senior Data Analyst @ Crunchbase, a top tech startup that specializes in private company data. I'm also an Instructor with LinkedIn Learning and the founder of Big Data Energy Analytics. My 5-9 (aka outside of my regular job!) is creating amazing content for YOU on LinkedIn, Instagram, TikTok-- and this newsletter!🤩
Maybe you covered this elsewhere,but if Big SQL Energy is for intermediate SQL, where do you recommend beginners start before taking your course? I'm a new sub and working on a pivot into data analytics from clinical data management.