10 Most Desirable Data Science Skills that You must develop to crack high paying Data Science jobs

In the recent times, Data Scientists have been in high demand. According to Glassdoor, Data Science is the number one job in The United States, which invariably makes it one of the highest paid jobs as well. However, the money doesn’t come easy.

Data Science can be a demanding profession. There are several highly specific hard and soft skills that you must acquire to gain excellence in your domain. These skills require a lot of efforts, experience, and a natural inclination.

Most times, the career advice you receive regarding the data science skillsets can be a little underwhelming. Of course, there are certain skill standards that you must meet to become a Data Scientist, but to achieve true proficiency at your job(and make loads of money from it,) you need to approach your skillset comprehensively.

In this article, we have compiled a comprehensive list of 20 most desirable data science skills that you must develop to crack high paying Data Science jobs.



Programming is the most fundamental of all the skills that a Data Scientist must have. A data scientist should know a variety of programming languages to extract and dissect data. However, the standard programming languages that should definitely be in your arsenal are:

  • Python – Python is, perhaps, the most famous programming language of them all. It is used by several large companies as the main language for their data science operations. It is also, arguably, the easiest to learn, despite being sharp, fast, and highly efficient.
  • Structured Query Language(SQL) – SQL is a highly specialized language used to manage data in relational database management systems. You can carry out some of SQL’s functions from other languages as well(like R and Python,) but writing your own SQL code will make your process more efficient, and give you a scope of playing around with the data.
  • R – R is amongst the most widely used languages. Social media platforms, financial companies, and media outlets have been using R to visualize data and do predictive analysis and modeling.

The knowledge of basic programming language has the following benefits:

  • It helps you analyze large sets of data – Most times you’ll be dealing with large sets of data, which can become overwhelming if you do not know the basics of programming.
  • It helps you create tools to better your data science operations – If you know how to code a program, you can create tools that will help you streamline your data science processes.


An understanding of probability, Hypothesis Testing, and Inferential Statistics is a must for any data scientist. As a Data Scientist, your fundamental job will be to incorporate statistical output in a business-friendly context. This means that you should have the ability to understand business statistics intuitively. You will also gain a competitive edge, if you can get exposure on at least some statistical techniques, such as Logistic Regression, Linear Regression, Clustering, Time Series Forecasting, etc.


Analytical thinking is at the heart of data science. Data science primarily is about analyzing the data produced by complex systems. This makes quantitative analytical skills an absolute imperative if you want a job in data analytics. Since you will be conducting a lot of experiments based on hypotheses, your quantitative analytical skills will help you modulate the experiments and mitigate the risk.

Applied Mathematics

Every recruiter expects Data Scientists to know at least the basics of multivariable calculus or linear algebra. This is because most recruiters need Data scientists to build their implementations in-house. The knowledge of applied mathematics is crucial for businesses where small tweaks in predictive performance and algorithm optimization can churn out positive results.


It is a primary job of Data Scientists to depict the complex data analysis and reports in visual graphs, slides, etc. Business people heavily rely on Data Scientists for crucial decision. However, these two departments speak two different languages. Data Scientists understand complex data; business people understand numbers and graphs. Therefore, visual communication is crucial to creating that smooth flow of communication between the two parties.

Business Knowledge

As a Data Scientist, you are expected to help in making crucial business decisions. The analysis and reports you extract out of complex data will help in that decision making. In that case, how do you expect your reports to work for your business, if you have zero business acumen yourself? Most Data Scientists make the mistake of learning all the niche, technical skills, and forget to get a taste of the real, business world as well. Your data analysis will amount to nought if it is not applicable in the business world. Therefore, a business acumen and knowledge is a must.

Product Knowledge

The Data you work on is extracted from a system. Needless to say, understanding of that system is indispensable. You should have a clear knowledge about the system; in terms of its features and functions. Sometimes data scientists have to carry out specific experiments. These experiments can only happen over the foundation of certain hypotheses. A data scientist can only create hypotheses if he has a good knowledge of the product.


Machine Learning

As a data scientist, you may get an opportunity to work on a product that, in itself, is largely data-driven. In that case, a robust knowledge of the techniques of machine learning will prove extremely handy. However, you don’t have to become an expert in the machine learning field. A bird’s view of the various machine learning techniques and buzz words is enough when the need arises.

Data Munging

A lot of times, the data you get will be imperfect. And you have to learn to work with these data imperfections and inconsistencies. This mostly happens when you are working for a small organization that has just begun extracting data. In that case, a slight munging of data is an important skill to have to maintain smoothness in the workflow.

Software Engineering

There is a possibility that you may develop data driven products. If not that, there’s always the management of data logging, both of which, require a background in Software Engineering. This is not a must-have skill, but can prove extremely helpful if you have it.


Problem Solving

Problem-solving is, perhaps, the most important soft skill that is required in virtually every profession. In data science, problem-solving is as important as any of the hard skills mentioned earlier. Since your job, per se, is to help businesses make informed, data-driven decisions, you need a problem-solving mindset. It cannot work any other way than that. In data science, if you don’t set out to solve a problem, you will never reach anywhere.


As a data scientist, you have to constantly explain complex data reports and studies to layman, non-technical business people. They are ones who will eventually go ahead and take business decisions based on your reports. To explain complex ideas to people, you need the ability to communicate efficiently. Your articulation and explanations can make a big difference in your role as a data scientist.

Community Skills

Community skills are pretty handy in every walk of life. In data science, community skills work in two ways:

  • Let’s say you don’t have a natural tilt towards business. You don’t understand how business decisions are made. But if you have strong community skills and a professional nature, you can ask for help from your colleagues who can help you overcome your weakness through their expertise and skillsets.
  • Joining communities of data scientists will help you share your ideas with the community(and get new ones in return,) and ask for help lest you’re stuck at a problem.


The best statistical models should always come together to tell a story. In fancy words, a good data scientist tells stories with numbers. The story helps the layman make sense of what the scientist is trying to say. It makes the study accessible and gives it a coherent structure. Just because you’re a data scientist doesn’t mean you blandly talk about data to laymen. You have to keep your audience interested. At least your employers. The ability to tell a coherent story will help you achieve that.

Simplifying complex Ideas

This soft skill is is extremely important to acquire for a data scientist. This is because the statistics, data, reports, findings, etc., that you have is too complicated for a non-technical person to understand. And your job requires you to explain it to the laymen constantly. This can only be done if you have a practice of explaining complex ideas simply. This skill can only be acquired through practice. Start by explaining the theory of special relativity to a six-year-old, maybe?


A lot of times, while extracting and studying data, you will end up finding “tremendous” results. A novice will fall for it. A person of high objectivism, though, will try to look through the “tremendousness” of the results. And more often than not, s/he will find a data glitch. As a data scientist, you regularly have to question everything — even your own process. And in your job, there is absolutely no scope of subjectivity.


Curiosity is a characteristic element that is common to all scientists. If you are not by nature a curious person, you better acquire it. Since your job as a data scientist requires a lot of experimentation, a good deal of curiosity is needed to see through or initiate the experiments. Curiosity will also help you come up with interesting ideas that will take your community forward as a whole.

Unconventional thinking

Analytics is being used in almost every industry. Then why do some industries or companies do it better than others? It is because their data scientists come up with unconventional and original ideas to help shape their businesses. Since everyone is doing it, you need to find different ways to do the same old thing every time. This can only happen if you think unconventionally, and have the courage to implement the ideas.

Converting abstract business issues to analytical solutions

Business issues are generally abstract and quantitative. Abstract problems can never have practical solutions. Therefore, data scientists are important. They are the ones who identify business issues quantitatively and objectively and propose solutions based on data-driven insights. Therefore, you need the ability to understand abstract business issues and convert them into analytical solutions.


In the real world, Data is often messy and misleading. It takes a real skeptic to see through the follies of real data and find solutions from it. You just cannot do it if you’re not a skeptic. You will be misled if you do not question everything. As simple as that.


As mentioned earlier, becoming a data scientist is by no means an easy job. You need much more than just the tech knowledge. Everything from your personality type, your natural tend, to your acquired knowledge can make a huge difference in how you do the job. However, if you do acquire the skills mentioned above, you will definitely hit the nail on the head as a data scientist.