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The 4 Tech Skills That Every Data Analyst Must Learn To Boost Their Careers – Plus Where To Learn Each One Online

Today, I’m sharing the specific 4 tech skills that every data analyst must learn to earn more in their careers, plus the online resources to learn each one.

When I started working with data 15 years ago, I learned by trial and error. It was grueling. And I made a lot of mistakes along the way. I wasted a lot of time because I didn’t really know where to start.

This is probably the #1 question I get from people all the time. Where do I start? What should I get started with first?

Unfortunately, there’s SO MUCH information out there it can be hard to know the exact right steps.

Start learning SQL, Tableau, Excel, and Python from books and YouTube

Here’s what most career-changers that want to get into data do:

  1. Search online to start their journey
  2. Find a certificate course or Udemy course
  3. Make it halfway through the course (sometimes less)
  4. Get bored or frustrated
  5. Quit

Fortunately, you can skip all the stress by learning from books and YouTube videos. I recommend this method because it works for me and is pretty tried and true.

There are absolutely some great online courses out there (Google comes to mind) and I will be launching my own courses next year (stay tuned!)

But I’m talking about the endless list of $15 Udemy courses that we purchase but never complete. Those are a waste of time!

Here are the exact resources I recommend:

Step 1: Learn SQL to work with data

Working with databases using SQL is part of the job description for every data analyst.

  1. Querying databases using SQL syntax
  2. Filtering, joining and creating metrics using SQL
  3. Understanding how data works in these underlying systems

The great thing about SQL is that once you learn it for one platform (or RDBMS, which stands for relational database management system) you know it for pretty much every other platform. 

You can learn SQL on any of these platforms:

  1. PostgreSQL
  2. MySQL
  3. MS SQL
  4. Oracle

PostgreSQL and MySQL are best for beginners because they are 100% free and open source and extremely popular for small businesses and even large businesses. 

MS SQL and Oracle are used for huge enterprise-level databases and are a little harder to use when you are just getting started. If you’re not sure, then start with PostgreSQL.

Learn more about SQL:

Step 2: Learn Tableau to visualize data

Data visualization is the best way to help people see and understand data. And Tableau is the data viz tool I recommend.

Here’s why:

People are visual. You need to show them the data in a way that makes sense in their brain. The best way to do that is with a visualization tool. 

here are other tools out there besides Tableau for data visualization. But I recommend Tableau:

  1. Tableau has a free version called Tableau Public
  2. Tableau has an AWESOME online community
  3. There are TONS of jobs and freelancing gigs available with Tableau
  4. It’s pretty easy to get the hang of Tableau, but it takes a LONG time to master the tool.

Don’t put yourself in a bind by looking for the “perfect” data visualization tool. If you learn Tableau first, you can learn the others. More importantly, you can put your Tableau skills to work immediately and start earning more in your job or freelancing.

Get started here:

Step 3: Learn Excel

Tons of people use Excel every single day. It’s stood the test of time even though other products have come and gone. 

Excel is used to organize, filter, sort, and share data as part of a data analyst’s job. 

  1. Quickly share datasets with coworkers 
  2. Put together a quick report to share ideas
  3. Extract data from source systems
  4. Create financial models and perform calculations without having coding experience

Top Excel resources for beginners:

Step 4: Learn Python

Python is a slightly more advanced skill, but that makes it a MUCH more valuable skill.

Python is used in data prep, blending, exploration, analysis, automation, and more!

  1. Data prep: pull data from sources and clean it up
  2. Data blending: combine data from different sources
  3. Exploration: answer questions based on the data
  4. Analysis: analyze data using powerful tools like Pandas
  5. Automation: build complex data pipelines to get the job done

You get the picture: Python skills are super valuable for data analysts. You can even build full-blown web applications with Python. Really, the sky is the limit.

Best resources for Python data analytics:

That’s it! 

Master these 4 tech skills to stand out from the crowd.

  1. SQL
  2. Tableau
  3. Excel
  4. Python

See you again next week!

Interested in starting with data?

Whenever you’re ready, there are 2 ways I can help you:

  1. How To Create An Awesome Online Data Analytics Portfolio
  2. Need specific advice? Book a 1:1 session with me

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