How To Become A Data Analyst (If I Had to Start Over)

I’ve been working full-time as a Data Analyst for the last 6 months. Landing this position took 3+ years, 200+ job applications, and a whole lot of stress.

If I could start over, I think I could cut that time and effort in half. Here’s how I’d do it:

What I’d Learn First:

  • SQL: Data lives in a database. Learn to speak database. (Creating and querying a database, manipulating data, schema design, joining tables, aggregations.)

  • Python: Whether you need a quick and dirty exploration or a fine-tuned data visualization, Python has your back at every step of the analysis. (Fundamentals, data structures, data exploration, data cleaning, analytics/visualization libraries: pandas, NumPy, sci-kit learn, Matplotlib.)

  • Statistics: A strong probability and statistics foundation is a very important part of any data analyst's arsenal. Statistical knowledge will help guide your analysis, help you understand the data that you’re working with, and help you make sure your analysis is valid.

  • Tableau, PowerBI, Mode (Or another popular BI tool): Creating informative dashboards and reports from data living inside a SQL database is a necessary part of almost every data analyst position.

Where/How To Learn:

  • SQL: Mode.com SQL Tutorials — I would complete all of the following courses and complete all the exercises included. (Basic SQL, Intermediate SQL, Advanced SQL, SQL Analytics Training)
    - Bonus: Database Management Essentials (Coursera/University of Colorado)

  • Python: Data Analysis with Python (Coursera/IBM) + Intro/Intermediate Python (DataCamp) + Matplotlib/Seaborn Visualizations (DataCamp)

  • Statistics: Introduction to Statistics (Coursera/Stanford) + Concepts: Hypothesis testing, types of statistical tests, type 1 and 2 errors, confusion matrix, level of significance, p-value, confidence intervals, data distributions — normal/binomial, central limit theorem, regression, sampling, descriptive statistics.

  • Projects: After completing the courses above and learning the basics, you should continue learning by building a project that you think is cool. Put your new knowledge to the test and apply it to a real-world project. (Conduct your own survey, gather sample data, put it in a SQL database, analyze the data in a Python notebook, and present your findings in a Mode or Tableau dashboard!)

Should You Go To School for Data Analysis:

  • College: No. You absolutely do not need a 4 year CS degree to become a data analyst. You can learn these skills in less time while spending less money on education. (I learned these skills and landed a job before I would have even completed a college degree.)

  • Bootcamp: I graduated from the Data Science program at Lambda School (Now: Bloom Institute of Technology) and I directly credit them as one of the largest factors for landing a great job in tech. (And I didn’t pay a single penny until I landed my dream job. Find out more here.) You still don’t NEED to go to a coding boot camp to become a data analyst, but you better be prepared for lots of self-study if not.

Build A Network:

  • Network with other students if you’re in college or bootcamp. The journey from noob to data master is not an easy one so try and make some friends along the way.

  • Seek out high-performance students and join or create a mastermind group. Keep each other accountable and share cool opportunities, events, and job postings. (Shoutout to Lambda Brain Trust)

  • Document and share your entire journey of learning to code/becoming a data analyst on LinkedIn and other social media platforms. (Pro Tip)

  • Create a personal website to tell your story and show your work/personal projects in a visually appealing way.

Applying To Data Positions:

  • LinkedIn is magic. Fill out every section of your profile and start connecting with people in your target industry. LinkedIn also gets better at finding positions that match your skills/interests over time so keep browsing those job postings!

  • Start applying to jobs on Day 1. This will help you learn the job posting lingo and give you real-time feedback on if your resume is strong enough.

  • Take note of common tools and methods mentioned in job postings that you’re interested in. Learn new skills based on what you see in cool job postings.

  • Track your job applications! Title, job description, company, date applied, and any other information should be written down. (I use Notion for this.)

Getting Interviews:

  • Iterate on your resume every 20 applications.

  • Reach out to hiring managers/team members for every single job you apply to. (LinkedIn Premium allows you to see who posted the job — this has gotten me multiple conversations with hiring managers before they even saw my application. You can also just search the company on LinkedIn and look through their employees.)

  • Use resume checkers to make sure the ATS (applicant tracking system) isn’t automatically rejecting you. (I use jobscan.co for this.)

  • Use power statements and include quantitative results in your resume.

Passing Interviews:

  • Practice SQL challenges with SQL Zoo.

  • Practice live coding interviews with Pramp.

  • Practice data analytics projects with Kaggle.

  • Look for leaked questions on Glassdoor.

  • Explain your thought process every step of the problem.

  • Practice solving problems on a whiteboard or piece of paper.

  • Learn about the company you’re interviewing with. What metrics drive their product direction/strategy?