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5 Data Science Skills That Pay & 5 That Don’t

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

5 Data Science Skills That Pay & 5 That Don't


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. 


5 Data Science Skills that Pay


1. Mathematical concept


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.


2. Programming, Packages, and Software


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:

  • Python
  • R
  • C#
  • TensorFlow
  • Apache Spark
  • Scikit-Learn


3. Machine Learning and Deep Learning


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. 


4. Forever learning


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?’ 


5. Hyperscaler approach


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. 

A lot of companies are reviewing the architecture and workflow of hyperscalers which are known for offering next-level performance without further complexity. It refers to my point above - always knowing what’s going to happen next. These companies have a good eye for what’s going to happen next - they are constantly innovating and developing their infrastructure to meet the demands of the future.

Individuals and companies that are on the ball and are constantly researching or learning more about the approaches that Hyperscalers take to be successful - will be able to apply that to their careers and be financially compensated for it.


5 Data Science Skills That Don’t Pay. 


All the skills below are not that it doesn’t pay, but having a lack of them can determine the security of your job and your overall salary.


1. Curiosity


The reason that I put this into a skill that doesn’t pay is that it is a skill that you cannot buy - it needs to be within you to want to be curious about the industry you are in. This reflects my points about ‘Forever Learning’ and ‘Hyperscaler approach’. Without curiosity, every single Hyperscaler would not be in the position they are in now. Some of the best Data Scientists, machine learning Engineers, etc would not be where they are if they were not curious. 

Like I said before, you can be content with your current position and believe that this is your end goal. However, if you’re looking to better your career and be financially compensated for it - you can’t stay in the same position. To get yourself to a different position, you need to take a risk, and risks are taken due to curiosity. 


2. Lack of knowledge


This is instantly a reason why you won't get paid. As mentioned prior, with the demand for tech experts - many are taking the fast route to land themselves a job. However, the fast route isn’t always the best.

You may miss out on learning a lot of the essentials which will be helpful to your first job. If you prove to have a lack of knowledge, depending on your company - some may not have the resources to help you build your skills. Some may require people who already have the skills and can come in to solve their problems. A lack of knowledge for a company is an additional problem.

You don’t have to be an expert, but you need to have a good foundation that allows employees to see your potential. Slow and steady wins the race..


3. Communication is key


I know you must be tired of hearing this - but it is true. Again, this skill does pay but as a matter of fact that it is a vital skill in order for you to keep your job. If there is a lack of communication, it can make your work life 1000x harder. 

When there’s an issue or lack of understanding - address it! If you don’t ask, you don’t get - so you’re better off asking and getting an answer that will at least guide you to the right direction. If that be with a challenge in a project or where you are going wrong and how you can better yourself and your career


4. Problem solver and Critical thinker


If you are given a task and you are at a blocker without having even tried on how to resolve the issue - this is a big detriment to your data science career

Being able to critically think on how you can solve a problem will allow seniors to see how your train of thought works. They will be able to see what’s your first point of call and how you took it from there to come to a solution. 

You will be able to take the problem, come to an objective or hypothesis to then further analyze and determine if their findings are helpful to the challenge being faced and are one step closer to a desired course of action. 


5. Business Acumen


Having business acumen is imperative for you to successfully grow in every job. If you don’t have business acumen, you will find yourself at one level at your job, probably confused about what you can do next and very content as your potential is limited. 

For an organization to grow, Data Scientists need to dissect problems and figure out the potential challenges and how these can be resolved. Not only will you be able to explore new business opportunities for the organization, but you are also building your skill set to show your ability




It’s all good and well when blogs, YouTube videos tell you the skills you need to become a Data Scientist. But you also need to be given the real on how you secure your job whilst you get it. The 5 skills that pay you are highly beneficial and will guide people into the right direction on how to excel in their career

However, the 5 skills that don’t pay are skills that people need to be more aware of. 

Soft skills are just as important as hard skills. Soft skills are not primarily used to determine your salary, your hard skills are. You will get paid more if you have AWS skills then if you have business acumen. Soft skills boost your value - they build opportunity, improve your performance and enhance your career

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.