According to the United States of Labor Statistics, the employment options for data scientists are projected to rise by at least 19% by 2026. Keep in mind that this is just a projection with current technological resources in mind. Who knows what achievements we might see in the coming years.

For data scientists, it’s becoming increasingly important to acquire new skills. We’ve assembled a list of 11 essential skills and tips that data scientists need in 2019. If you manage to master all of them, the job market is yours for the taking. Do you think you can do it?

  1. SHAP (SHapley Additive exPlanations)

SHAP is a unique approach that has the explanation of machine learning as a goal. It started off as a Python by-product, but instead went on to incorporate several key elements such as local explanations and game theory.

Data scientists still haven’t adopted this skill quite yet, making this an ideal time to learn it and rise above the competition. It’s relatively new, but there are online courses.

  1. What-If Tool

Developed by Google, the What-If Tool is one of the most essential skills data scientists need to learn in 2019. It’s still fairly young but presents a lot of benefits to professionals in this niche.

With interactive interphase, this tool gives you the necessary skills to analyze machine learning formats without even a line of code. It’s supposed to be the analytics tool of the future.

  1. Content writing

When you’re presenting projects and solutions to clients or your superiors, you have to elaborate on everything you do with clever wording. A skilled data scientist needs to know how to write good copy.

Nobody will understand equations and complex sets of code. Everything you do has to be explained in plain English to be able to get through to your clients.

  1. Writing tools

Data scientists have a lot of work to do. Sometimes, it’s not possible to write a project all by yourself. To traverse this obstacle, you have to use writing tools.

The best options are custom essay writing servicesNinjaEssays and Pro Essay Writing. All you have to do is give them the instructions concerning your project and you will get presentations that will impress anyone who sees them. Outsourcing is the future.

  1. Python

If there has to be an essential programming language for data scientists, it will be Python. Not only is it easy to learn, but it’s the most wanted language in all job requirements.

If you learn Python properly, you can master any other programming language due to simply understanding the architecture. It’s something you want to devote time to. Find exercises and problems to solve online, too.

  1. Reinforcement learning (RL)

This special area of machine learning (ML) is devoted to improving the work of data scientists. If you want to rise to the top of your game, RL is something you ought to learn in 2019.

This area of ML deals with possible solutions that can help us notice patterns. RL allows you to determine whether some actions in data interaction are isolated incidents or if their function should be reinforced for repetition.

  1. R

R is one of the most used tools in data science and therefore a key skill that you should acquire. Its advantages lie in the open source nature of the software and the graphic representations of data.

Graphics and statistical computing were forever changed by the introduction of R. In data science, this language is a must. Many big corporations such as Microsoft give support to R and contribute to the creation of new packages. Check out Microsoft RSTudio, too.

  1. Java

Other than data science-specific languages, you should also master some general purpose languages. Java is the best one due to so many systems and software packages running on the Java Virtual Machine Principle.

Data scientists also need Java because of its portability across different computing platforms, both in theory and in practice. If you master Java, you can also expand your craft in the fields of software engineering and DevOps engineering.

  1. Scala

Also known as Scalable language, Scala gives you the opportunity to enjoy an open source base of data, tips and advancements.

It’s an ideal language for those who work in big data and have to process a large quantity of data patterns on a daily basis. It’s developed to run on JVM, so if you learn Java too, you will have interoperability mastered.

  1. MATLAB0

For data science, mathematical algorithms are essential. MATLAB gives you a chance to use a numerical computing language that will notice patterns and quantify large chunks for data for easier analysis.

It’s a bit difficult to learn, so you should leave until you have some experience in the field. Statisticians and data scientists around the world use MATLAB-based principles.

Conclusion

Like in any career, it’s important for young data scientists to constantly improve. With internet resources at your disposal, you acquire all the essential skills in a matter of months. Even the biggest corporations prioritize potential and skills when hiring data scientists, not formal education. Stay inspired and you will make a wonderful career for yourself.

Author bio

Kurt Walker is a copywriter, data scientist and blogger who’s been working in London for years now. Although he specializes in data science, he has been using his skills to improve tools such as assignmentholic.co.ukbesttermpaper and paperwritingpro.com. Kurt writes blogs about data science, education and technology is a whole, in addition to his main occupation.