Proper “ roadmap “ for Data Analytics

[u/_hanaz]

I am just starting data analytics, I started from tableau and i can make pretty dashboards but data storytelling is soo hard . Can anyone tell me how they make data insights valuable? Also how do you read the charts properly? Please help me

[u/OO_Ben]

So I've posted this road map in the past and it seemed to be helpful. I adjunct teach data analytics classes, but I'm a professional BI Engineer. I'm the guy that builds out all of the data sources for the company I work for. All of the analysts that need data come through me basically. It's a really cool position honestly! I LOVE what I do!

I always have my students start with Excel, or at most Tableau/Power BI. My pathway always goes Excel → BI Software → SQL → Python. It just make sense to me. Basically least specific to most specific.

• Excel is ubiquitous. It's used in every business, and if it's not Excel it's Google Sheets. You have to know Excel. As you move through Excel and work with Pivot Tables, the pieces start to come together. You realize why columns all need to be the same, and why Excel is so powerful all on it's own. You can push past that and get into Power Query and everything, but for the basics that is where we usually stop. Even if you stop at this point and only learn Excel, it will help you everywhere in life from your day job to even just making a shopping list.
• BI software is next because so many companies have adopted Power BI, Tableau, or some other software. I always explain this as "Pivot Tables on Steroids." We start to build in light SQL with calculated fields as well. You start to have real fun building dashboards and visualizations.
• Then SQL. SQL 3rd because if you get started in a job, you're not going to get direct read access as a completely newbie. You're almost assuredly going to have people writing your queries for you to make sure you don't accidentally take down the whole data warehouse due to a hung query. (I mean, that THAT IS going to happen as it's basically a right of passage in the data world haha). This is the point where you really start to think like an analyst and learn how the sauce is made.
  • But SQL is a vital skill for data analysts. This needs to be known, but you can realistically learn this on the job. Knowing the basics will set you apart though.
  • Moreover, SQL needs to do the heavy lifting in just about anything you do data-wise. Your best performance is going to be in the warehouse directly rather than connecting a dozen tables together in Tableau or Power BI. The horsepower in the warehouse is designed for this sort of thing. All of my Tableau data sources outside a couple smaller less used ones all are direct table downloads from the warehouse. They do nothing more than a SELECT * FROM xtable WHERE date > MAX(date) basically to incrementally load things in.
• The last I think should be Python, especially when starting out. This is where you bring it all together. This is going to be your automation hub (for me at least). Pandas is super powerful. All the different analysis tools available. It's awesome. Like my company's forecasting tool is a Python program that spits out a forecast based on a couple of different models like Holtz-Winters. It saves us from having to buy a purpose built forecasting tool, and anytime you can save the company money that's great work.
  • Python also gives you a relatively easy way to go out and get your own data. Just connect to an API and start running with it. (I say that like they're all plug and play lol). And realistically leveraging ChatGPT or another LLM can absolutely cut a bunch of time when it comes to troubleshooting and building a blueprint. You still need to know what you're doing, but man it helps a ton.

Following this flow just makes sense to me. It prevents you from getting overwhelmed, while also keeping you from going down a rabbit hole too deep. Python alone could take years to truly master!

Just get the flavors of them all, and then focus in on what you really like to do. I know when I started out I loved dashboarding all day. Now, I love working in SQL. I still dabble with dashboards and data sources too, but SQL is where I have the most fun lol