I got an Instagram DM the other day that really got me thinking. This person explained that they were a data analyst by trade, and had years of experience. But, they also said that they felt that their technical skills were slightly lacking, as they had never heard of many of the terms mentioned on my page. This person mentioned that they were looking forward to expanding their skill set by learning more technical tools (SQL, Python, R, etc.)

As I thought about how to advise this person further, I realized that this person was in the perfect position to make the transition that they desired. Why? They had already mastered the data skills and data mindset that is crucial to being successful in the field of data.

I (and so many others) worry about mastering every technical tool or product that is out there. I worry about only having experience with Microsoft products (SQL Server, Excel, Power BI), and feel that I need to broaden my horizons to be a better data analyst. I constantly see data scientists questioning and debating online about whether Python or R is better in their line of work.

But, speaking with my new Instagram friend helped me realize that these worries and debates are quite silly. Tools and programming languages are constantly evolving and changing, coming and going. But you know what is here to stay? The core concepts. Every tool or language that is ever built will always fall back on these core concepts.

If you understand how to take a data set, manipulate it, and present it in a way that provides genuine insight (or at least invites more questions that you didn’t have before… because that happens!!), you are on the right path to succeed as some sort of data professional.

This base understanding of data is so powerful. You can take this understanding, and combine it with any technical tool of your choice. Then, you can group and filter data for business reporting and KPI monitoring, conduct statistical tests to answer questions about data, predict future data, or even generate AI models to use data to help guide business action. And you can do all these things with huge data sets containing millions and millions of rows!

OK I know I’m selling you and selling you on this idea, so let me cut to the chase.** If you understand data concepts and how to apply them, you can easily implement these concepts with any technical tool or product of your choice.**

But don’t worry, I’m not just here to sell you on this and then head out. I’m going to talk about 3 basic data skills that I use daily as a data analyst, from a general perspective. NO TECHNICAL TERMS OR CODE INVOLVED. If you begin to master these (and other) data concepts, it is EASY PEASY LEMON SQUEEZY to take them and apply them with any tool. I even have a serious life hack at the end of the article that will help you further flex your new data knowledge in any tool you’ve been wanting to master. Stick with me, I got you!

#1. Filtering Data

The first data concept that is crucial in the data world is filtering data. Honestly, filtering data is a super simple concept and one that we as human beings do on a daily basis. Take this example. If you are going to get McDonald’s, you should probably ask your 3 roomies if they want some (because you don’t wanna be _that _roommate). But, before you go ask your roomies if they want chicken nugs, you remember that 2 out of your 3 roomies don’t even like McDonald’s, so you only end up asking one. Basically, you “filtered out” your two roommates from your “data set” based on some “attribute”, which is whether or not they like McDonald’s.

Filtering data as a data analyst or data scientist works the exact same way. If you are conducting an analysis on female customers, you will need to use whatever tool you have at your disposal to filter out the non-female customers. If you are trying to build a model that helps recommend skincare for adults, you would want to filter out any data for non-adult patients.

Long story short, filtering data is just taking away all of the undesired data from whatever data set you have, until you are left with whatever data you need for your analysis.

#learning #programming-languages #data-analysis #data-science #machine-learning #deep learning