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The Six Figure Data Analytics Tech Stack: The Tools You Need To Succeed In Data (And How To Learn Each One)

Today, I’m sharing what I call “The Six Figure Data Analytics Tech Stack”.

This is one of the most common questions that I get in my 5-minute subscriber survey (ask your question here)

  • “Do companies hire based on one skill or a tech stack?”
  • “Is one tool good enough (like SQL) or do you need multiple tools?”
  • “Job listings look more like a wish list with everything that’s required!”
  • “How do I tighten up my skills to land the job I want?”

Earning six figures in the data industry might not be your goal.

But if it is, here’s the tech skill stack I recommend. There are different opinions on what to learn. So I’m sharing the 4 skills that I think are the best if you want to earn a high salary and work from anywhere.

And these are based on my 15+ years of industry experience, working in large and small companies, freelancing, and consulting.

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Don’t get overwhelmed. Each tool has a role, and they all play well together.

You just need to learn how to be a good team leader.

Let’s dive in:

SKILL #1: Excel – The Swiss Army Knife of Data Tools

Excel is the bread and butter of data tools, a must-know.

This is because both data professionals (like you) and business people (your clients) use the tool. So Excel provides a common place to share data and quick analysis. Over the years I’ve created countless dashboards, exports, reports, and visualizations with Excel to share with my business partners.

Don’t get lost in the sea of buttons and menus in Excel when you are just getting started.

Instead, stick to the key features at first. Then, when you’ve mastered the basics, you can move on to more advanced techniques. And before you know it, you’ll be slicing and dicing data like a pro.

When I was just starting as a data analyst, Excel was pretty much the only tool I really knew. I had SOME experience with databases (Microsoft Access 2003 probably) but, really, Excel was it. I was able to get my foot in the door for my first analyst role and work my way up from there.

So it’s definitely the right place to get started!

Excel learning resources for you

Rookie mistakes to avoid when learning Excel

  1. Using Excel like a Word document. It’s for numbers! Keep things short and sweet.
  2. Watch out for cell references! Formulas are powerful but can be tricky to master.
  3. Not using shortcuts. Save time and make your life easier with keyboard commands
  4. Leaving things messy. This was a piece of advice from my favorite boss ever. She shared the importance of keeping things clean by adding some light formatting, some filters, and putting myself in the shoes of my audience before I sent out a spreadsheet. I think about that advice often!

SKILL #2: SQL – Talk to Your Database Like a Boss

SQL is the language of data.

It allows you to ask any question about your data and get the answer in the exact way that you need. And it’s possible to learn everything you need to get started in 30 days (or less). The cool thing about SQL is that once you learn it for one database system, you know it for pretty much every other one.

It’s a skill that will pay off for the rest of your data career.

That’s why I think it’s so important to learn as early (and as fast) as humanly possible. There are some syntax things to learn, but the sooner you start, the better off you’ll be. And before too long, you’ll be writing more advanced queries and answering questions your business partners never even thought to ask.

Learning SQL was the first major advancement in my data career.

Before I really knew how to use SQL, I felt really lost and out of place. I knew that there were answers in the data, but had no clue on how to get them out of there! I remember being VERY confused about JOINs, especially. But, thankfully, I had colleagues and mentors to help me along.

How to learn SQL

Rookie mistakes to avoid when learning SQL

  1. Skipping the basics
  2. Not practicing enough
  3. Failing to use real-world data
  4. Not asking for help

SKILL #3: Tableau – Tell a Compelling Story with Your Data

Tableau lets you turn raw data into stunning charts, graphs, and dashboards.

Tableau was another GAMECHANGER for my career. I really can’t say enough about the tool and it was what finally pushed me up past the six-figure mark. I was able to take raw data and transform it into a TOOL that my clients could actually use to solve problems.

Tableau is simple to get started with. But it takes practice to get really good.

Unfortunately, a lot of people will stop here and say “What about Power BI?”. Listen, if you want to use Power BI, go ahead. There are millions of people using it and I know it has the Microsoft seal of approval.

But Tableau allows me to think and create data.

I can’t count the number of times I’ve transformed a random, messy Excel or CSV file into a final product using Tableau. It’s probably in the thousands of times over the past 10 years I’ve been using the tool. For one period in my career, I was using it all day, every day, for probably 3 years straight.

And it helped me create a name for myself in the company I was working for at the time.

How to learn Tableau

Rookie mistakes to avoid with Tableau

  1. 5 Mistakes to Avoid as a Tableau Beginner
  2. Using way too many colors, fonts, and other formatting features
  3. Choosing the wrong chart type. You’ve got to pick the right one for your data.
  4. Not labeling things properly. Keep your audience in mind!

SKILL #4: Python – The Magic Wand of Data

Python is incredibly powerful when it comes to working with data.

By adding it to your toolbox, you’ll be able to work with HUGE amounts of data (when needed), automate routine tasks, and even use machine learning and AI models to make predictions based on your data.

It can do a lot, but don’t try to learn it all at once.

Start small and build up your skills over time. Reminder: slow and steady wins the race! And when you finally start getting the hang of it, Python really will feel like magic.

Python is the most recent tech skill that I’ve added to my toolbox.

I did some basic Python programming years ago but took an Advanced Python for Data Science course 2 years ago. It was probably the most difficult course I’d ever taken, but I learned a TON! And now I’m able to do anything I want to do with data and get things automated.

Here’s a small sample:

  1. Created a fully automated reporting system with Python, SQL, and Word (yes, WORD was the preferred “reporting tool” for my client)
  2. Automated the creation of an 80-page PowerPoint deck based on 35 different Excel worksheets (a monthly report)
  3. Prototyped an entire metric creation framework by pulling in data from a bunch of different sources into one package

The opportunities are endless when you know Python!

How to learn Python

Rookie mistakes to avoid

  1. Trying to learn everything at once. Focus on just ONE data analytics topic with Python first.
  2. Ignoring the debugging features of your Python IDE (this one took me forever to grasp)
  3. Copying code without understanding what it’s doing — a waste of your time!
  4. Not knowing how Python libraries work (and which ones to use)

Okay, that is what I call “The Six-Figure Data Analytics Tech Skill Stack”.

Am I guaranteeing that you’ll make $100,000 tomorrow if you follow each of the links above?

Of course not.

But I DO know that these are the skills that are in demand now and will be in demand for the near future. And they take time to learn and then apply to real-world business scenarios. These technologies have been around for decades and millions of business leaders will be using them for decades to come.

You have to take the first step.

If you put off learning them, you’ll never get to where you want to be in your data career. It’s all about taking action, not overthinking, and just taking the very next step on your data analytics career journey.

Here are the 4 skills:

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

Whenever you’re ready, here are 3 ways I can help you:

  1. View all past issues of my newsletter here.
  2. If you’re ready to build your online data analytics portfolio, my 14-day Data Analytics Portfolio Playbook is for you.
  3. If you want actionable data analytics advice on your specific situation, book a 1:1 coaching session with me today.

1 thought on “The Six Figure Data Analytics Tech Stack: The Tools You Need To Succeed In Data (And How To Learn Each One)”

  1. Pingback: Creating an Interactive Help Desk Ticket Analysis Dashboard from Scratch with Python Using Plotly (Code Included) - New Prediction

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