Things every Data Scientist should know besides coding

Welcome back! Data Science is a very popular field that’s going to continue to grow, i’ve even made a lot of articles talking about how to become a data scientist, a lot of those articles talk about the importance of programming, so let’s talk about the other important aspects of Data Science outside of programming.

Data Visualization Tools

The first thing that’s extremely important that every Data Scientist should know is an assortment of data visualization tools. The tools can range from Tableau, PowerBI or even some Excel pivot charts, you will need to have a bit of experience using data visualization tools. One of the main things I would do at any position I had was quickly developing charts, you could do this within Python and R, but it’s very important to know tons of different software especially data visualization tools.


Another very popular thing every data scientist should know is the AWS suite of products, specifically with Amazon S3. From what i’ve seen, almost every single data science position from tons of different companies (including Amazon, Apple and Microsoft) required some experience with this. AWS is essentially Amazon’s offering for cloud architecture, there are tons of products that Amazon offers with this, so try your best at understanding as much as you can with this.

Developing Reports (Writing Skills)

This is almost a gurantee for any job you take, there have been weeks of time i’ve spent just developing reports. If you couldn’t tell, writing isn’t my best quality, so developing your written skills is another massive thing every data scientist should know. As most of you know, during our college years we had to develop reports all of the time, in your job you essentially write code, but writing reports for your job always seems to be the hardest thing to do (especially for me). Improving your written skills won’t guarantee you a job as a data scientist, but it will definitely make you stand out if your reports / documentations of your projects are written extremely well.

Presentation Skills

Going along with written skills, presentation skills are another massive attribute to data science. You have to remember that a majority of the time you’ll be presenting your findings to another group of people, this is very common stuff, a massive pitfall to this is you may present to people who don’t understand the technical aspect behind your findings, why is that important to remember? Easy, if you talk about machine learning this & python that, no one is going to understand what you’re talking about, so you have to be able to balance your public speaking skills with your ability to speak about your project from a non-technical stand point.

Product Ownership

One of the final things i’ve found to be extremely important in any job i’ve had is product ownership, this essentially covers everything from security of the product, version control and deployment of the product. This is one of the most important things about being any type of engineer, you must be able to develop the product, but you have to be able to maintain the product throughout the duration of your involvement. To be honest, a lot of this can be maintained by learning Git, so understanding that will greatly help you out with this, but being able to deploy the product is another important aspect to data science. Whether the end result of the product is a report, charts or a full fledged piece of software, you have to be able to maintain the product and troubleshoot any issues that may arise in the future.

There you have it! Those are some of the most important things I would recommend learning outside of programming, especially if you want to become a data scientist!

As Always

if you have any suggestions, thoughts or just want to connect, feel free to contact / follow me on Twitter! Also, below is a link to some of my favorite resources for learning programming, Python, R, Data Science, etc.

Data Scientist / Engineer

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