5 Data Science Skills That Pay & 5 That Don't | 7wData

When you’re starting a new career, especially one part of the tech world - you will find yourself trying your best to have every skill in the book. With a growing field, you don’t want to be left behind - every skill is imperative at this point. 

This article will go over the top 5 data science skills that pay you and 5 that don’t. 

I am going to start with the concept of Math. As the demand for Data Scientists continues to increase, we see an increase in Bootcamps, Courses, etc. I did a BootCamp course in data science and when I landed my first commercial data science role - I was missing one thing. A good and deep understanding of the concept of Math and its vital role in the movement of data science. 

A lot of BootCamp and online courses are short courses that fast-track you to land a job instead of going through the traditional route of University. However, this fast-track route results in you missing out on diving into important elements of data science such as statistical probability. If you have a better grasp and are able to apply math to your data science projects, you will ensure that it is done correctly and the results match the expectation. 

This skill pays - you will be less likely to be dependent on your seniors and will be financially compensated as you possess a skill that proves independence and a core understanding of what’s going on.

As a Data Scientist, you are tested on your programming skills - that is what brings projects to life. You will have the ability to transform raw data into something that has valuable insight. As Data scientists in this day and age, many people use the programming language Python and/or R.

However, as a Data Scientist, you will come to learn that there is more than one way of doing something, more than one way of solving your problem, etc. Therefore, you should not limit yourself to what tools you use to help come to your solutions to gain valuable insights. There’s a variety of programming languages, packages, and software that you can use. Here are a few popular ones:

You could go on as a Data Scientist that wants to take in raw data and figure out how to create valuable insights which can then be easily interpreted through visualizations and reports. However, if you’re looking to excel in your career and let that be reflected in your pay - you need to know and learn more about machine learning and Deep Learning. 

A lot of companies in technology have started to ask ‘how can this be done without me manually doing it?’. This is where ML, AI, and DL come into the picture - the next wave of technology and its uses. If you want to see how your data science skills are paying off and see yourself going to the next level -  this is where the bag is. Along with the points above and the points below - you will be able to push your data science skills. 

It’s part of the industry - you will always have to keep learning. Your value as a Data Scientist comes from what knowledge you have that can be applied to a company’s value. In order to do this, you as a Data Scientist need to be on your game and know the next moves in the industry.

Although many concepts are traditional and will always be used to solve problems, as the field of AI, ML and DS grow - new companies are coming out of the woodwork to provide better and simpler solutions. 

For example, SQL - a lot of people started with Excel and have now discovered the capabilities of SQL and made the transition. This is how you become part of a movement by always being on game and continuously asking yourself ‘how can I make this easier?’ 

Hyperscalers include companies such as Google, Facebook, and Amazon. These companies are making the effort to dominate the tech industry through cloud services and more - but they are also using their ability to expand their business into different sectors.