🛣 33-Step Data Analyst Roadmap
If I had to break into data analytics all over again, here would be the 33 steps I’d take
By the way, for fun, I asked two AI robots to discuss their thoughts on this 33-step roadmap on my podcast this week. You can watch the AI talk about it here, or listen via podcast here.
Week 1:
Learn about the 50+ different data job titles
Update your LinkedIn (headshot, cover photo, headline, about, experience, education, skill sections)
Understand the importance of a project portfolio
Week 2:
Brush up on Excel (filtering, sorting, formulas, pivot tables, VLOOKUP)
Start using LinkedIn as a tool to land you a job (networking, posting, commenting)
Build a project w/ Excel on real-world sales data
Create a project portfolio on LinkedIn
Week 3:
Study the fundamentals of data visualization (chart types, preattentive attributes, dashboards, color)
Get the basics Tableau (connecting to data, bar charts, scatter plots, pie chart, KPI dashboard layout)
Build a project w/ Tableau on real-world education data
Update your resume (make sure it's ATS friendly, add your skills multiple times, have your desired job title listed)
Week 4:
Learn SQL 101 (SELECT, AS, GROUP BY, ORDER BY, WHERE, SUM, AVG, COUNT, LIMIT, AND, OR, NOT)
Pro tip: use CSVFiddle.io so you don't even have to download anything (shoutout if you read last week's email)
Make a financial analysis project w/ SQL using real world data
Build a project portfolio using a website like Carrd or GitHub Pages
Week 5:
Tackle SQL 201 (joins, UNION, CASE WHEN, HAVING, CTE's, CAST, Window Functions)
Create & share a real-world SQL project on healthcare data & post to your new portfolio
Start sending cold messages (hiring managers, recruiters, peers, college alumni, etc)
Start applying for 20 jobs / week (know you'll get rejected from 70%+, but focus on the positives)
Week 6:
Dive deeper into Tableau (Heatmaps, Bubble Plots, Stacked Bar Charts, Treemaps, stories)
Make a detailed Tableau project w/ sports data & post to your portfolio
Keep applying for 20 jobs / week (focus on in-person or hybrid; it's 10x less competitive)
Week 7:
Learn the absolute basics of Python (variables, functions, loops, pandas, seaborn; nothing more than this or you'll be stuck here for months)
Build a simple project with Python on real-world manufacturing & share on LinkedIn
Start practicing for interviews (conduct mock interviews, make an interview matrix)
Week 8:
Study the fundaments of R & statistics (correlation, scatter plots, hypothesis testing, p-values, linear regression)
Build a simple HR project in R & share on your portfolio
Continue posting/commenting on LinkedIn,
Week 9:
Quickly study the basics of Power BI & SAS (know what they are & how to use them; nothing more than that)
Ramp up your applications to 40 / week (that's 6 / day)
Reshare all your projects on LinkedIn once again
At this point, you should be getting interviews
Week 10:
Design & build a capstone project that uses data in your preferred industry & post it
Continue to network on LinkedIn by sending invites, commenting, & posting
Keep applying to jobs (bonus points if you use the LinkedIn Hidden Job Market method)
By the end of this roadmap, you'll have:
A Stunning Project Portfolio (on LinkedIn & personal website)
9 Data Projects (using Excel, SQL, Tableau, Python, Power BI, & R)
An Updated LinkedIn Profile that works for you
An Optimized Resume that passes the ATS
If you follow each step, you will become a data analyst.
You can follow this roadmap on your own, but if you want some extra help, I’ve built a course that will walk you through the roadmap step-by-step, with coaches to help you if you get stuck.
