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  • Writer's pictureKelly Adams

How to Stand Out as a Data Analyst: The Power of T-Shaped Learning


Shelves with paperback books on them

As a data analyst there's a lot to learn. Whether it's your specific field you're in like healthcare or finance. Or the various tools you can use like SQL, Tableau, Power BI, etc. Sometimes within those tools and skills there's varieties to learn like for SQL there's different languages (e.g. PostgreSQL or MySQL). It can be overwhelming deciding what to do next. Enter the -shaped learning method.


What is T-Shaped Learning?

In this article from College Info Geek, "The T-Shaped Person: Building Deep Expertise AND a Wide Knowledge Base" it talks about a T-shaped person. It's a person who has "deep knowledge/skills in one area and a broad base of general supporting knowledge/skills", shown in the image below.


T-shaped people are a mix between someone with a large set of general knowledge but no specialization (a "dash" ) and a person who is a specialist with no general knowledge (an "I").



Why Not Specialize?

In his book Range, David Epstein, dives into the benefits of having range, which he defines as breadth, diverse experience, and interdisciplinary thinking in your life. This is crucial because modern work demands knowledge transfer or the "ability to apply knowledge to new situations and different domains". Individuals with a broad sense of knowledge are generally better problem solvers.

Professionals who maintain a breadth of knowledge across various fields and pursue diverse hobbies and interests are useful; they can easily move among different teams, cross organizational and disciplinary boundaries, and create new collaborations.

How Does it Apply to Being a Data Analyst?


There's two ways to think about this.

Career

First, consider the broad context of your career. Your specialty is data analysis. You spend time analyzing data to give business insights on how to make data drive decisions. The goal is to improve and become better at that. But you need to develop knowledge in a broader sense. Learn about how a business works, study the field you're in, or how to write better. Anything else you learn can only benefit you.

Data Analyst

Next, let's think about this as a data analyst. What you want to do is gain a broad range of knowledge in various data analyst skills like Excel, SQL, Tableau, Power Bi, R and Python. Be able to understand these tools and use them. But you want to have an depth of expertise in another tool like SQL. Get really good at one tool or skill.


How Do You Do This?

First take time to figure out what you want to be known for. When you are starting out it's best to start out as a generalist. Learn a handful of skills. You don't need to become an expert in these skills, but you should have a solid understanding of them. For instance in Excel the minimum for most analyst jobs require you to have knowledge in Pivot tables and VLOOKUPs. Reach the "minimum" for each of these skills.

This will let you test out and experiment with common tools analysts use. After this you should have an idea of what tool you like the most. Some people love visualizing data in Tableau others prefer writing complex SQL queries. It may take time to figure out what your specialty is.

Why Do This?

The main reason is it makes you more marketable for companies. You have the basic knowledge in common data analyst tools but you also have a specialty. But it also helps keeps you interested in the field. Being a data analyst isn't stagnant. There's always new tools to learn and ideas to explore.

Conclusion

Becoming a data analyst can seem overwhelming at first, with all the tools and skills to learn. But by using the T-shaped learning method, you can be strategic about your learning. By gaining a broad understanding of multiple skills and diving deep into one or two specific areas, you'll make yourself more appealing to potential employers. Remember, the best part about being a data analyst is continuously learning. Embrace any the to learn, adapt, and grow.

Thanks to Matt Mike and his LinkedIn post which helped inspire this post.

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