data science

Data Science & Analytical Tools

The other day I was asked questions regarding my technical skills. While I was answering, I had not realized the accumulation of knowledge I have gained these past 2-3 years in regards to learning new tools for data science & analytics. I decided to lay them out on a list and determine my skill level (1 - 10 with 10 being “highly skilled”) with each tool while also determining how often I currently use them for projects/fun.

The order will show my level of usage when compared to other tools — the first being the most used on a high frequency. There are times where I will use one more than the other, but for the most part these are all active tools I frequently used.

  1. Python (Anaconda) — 10

  2. Jupyter Notebook (+ JupyterLab) — 10

  3. Microsoft Power BI — 10

  4. SQL (MSSQL) — 9

  5. Excel/CSV — 9

  6. Alteryx Designer — 6

  7. Tableau Desktop + Tableau Prep — 7

  8. Visual Studio Code — 10

  9. R (RStudio) — 5

  10. Microsoft Flow — 8

Dataviz Color Palettes

canvacolors

Whenever I am creating an analytical visual/dashboard, I start/finish the visual with absolutely no colors. This was something I picked up during one of Cole Nussbaumer’s podcasts—I highly recommend for anyone looking to learn/improve your data visuals skills. Now after finishing the visual, you can then look at the entirety of the visual to determine what values/items need to stand out. But first consider, can you use shading to help it stand out? If colors are appropriate, do they relate to what is being presented?

I came across this website by canva.com and have used it often when I needed eye-appealing color combinations. Bookmark it, I think it could be very handy as you build out your visuals.