6 Important Skills That a Data Scientist Must Have

15-Dec-2020

Technology has revolutionized most industries and domains in various ways. New innovations and breakthroughs are made almost daily, most of them owing their existence to Data science. Since businesses and organizations around the world rely so heavily on technology now, they are producing a lot of electronic data. All these ever-growing mounds of data need to be stored, processed, and analyzed. To do this, companies have to hire and rely on data scientists. Thus data science is a very rapidly growing field which is expected to become more and more lucrative as time passes and the world relies more and more heavily on technology. To break into this field one should take a Post graduate program in Data science course. Let us see which are the most essential skills that a data scientist must possess in order to function effectively in the industry.

Which Skills are Required to Become Data Scientists?

• Programming Skills

A data scientist should have knowledge of at least a few of the most popular and common programming languages. In the course of their career as a data scientist, they will be required to process, clean, and manipulate data in various ways. They will have to operate on data in such a way that it becomes usable and manageable. They have to convert it into a format which they can input into their various sophisticated data science and data analytics tools to evaluate the data and run various processes and algorithms on it. A programming language such as Python, Perl, C/C++, SQL, or Java will help the data scientist manipulate data. Many programming languages also come with their own libraries and packages which contain readymade data science functions which data scientists can use immediately. Careerera’s PGP in Data science course teaches all these different programming languages.

• Good Working Knowledge of Analytical Tools

A data scientist should have a solid understanding of the most popular and common data analytical tools such as Microsoft Excel, Tableau, Power BI, SQL, SAS etc. These tools are full of very helpful functions and are packed with dynamic features which simplify the task of a data scientist greatly. Since these tools already contain many of the functions which data scientists use on an everyday basis, they save the data scientists from having to write new programs to perform those functions on data. So they don’t have to reinvent the wheel every time they want to perform a common and everyday function on data. A well-designed data science certification training course should contain modules on all these different tools.

You can also check "Programming Language for Data Science in 2020"

• Able to Understand and Use Unstructured Data

What happens most often is that companies start their businesses and operations with a pipeline which isn’t strictly dependent on technology and then try to shift it onto a technological stack later. This means that a lot of the data that exists in the company is most often unstructured and not even electronic. It might be in the form of ledgers, registers, punch marks, tokens etc. But later when the company want their operations to run on a technological stack, they subsequently also want all their data to be made available to them in an electronic format which they can easily analyze, evaluate, and process. So it becomes the job of the data scientist to convert all that unstructured data into a structured and electronic form. One can learn this by undergoing data science certification training

• Should have a Sharp Business Acumen

A data scientist doesn’t need only technical skills, but some non-technical ones too. In particular, he should have a sharp business acumen. He should be able to foresee which problems or challenges a company will face. If he is able to do that, he will be able to tailor his solutions in such a way that they end up addressing the problems before they even arise. He should have at least a rudimentary knowledge of the business domain so that when the management of the company try to communicate their issues to him, he is able to comprehend them easily. He should be able to grasp the foundations of business strategies readily and modify his algorithms of data science accordingly.

• Good Communication Skills

A data scientist usually has a well-rounded education in mathematics, statistics, machine learning, and artificial intelligence. His tools and techniques are all heavily steeped in technical and complex mathematical concepts. The algorithms and processes that he runs on data are very obscure to a layperson and seem almost like gibberish to him if they try to comprehend and understand them. But a data scientist does not work in isolation. He has to collaborate with other team members on the same projects. His team members often do not have a strong foundation in data science or even computer science. Often, they may be completely management oriented and non-technical. So a data scientist should have very strong communication skills so that he can explain his work and his ideas to them effectively. He should be able to explain his contribution to the project using easy to understand visuals and illustrate his work using simple and non-technical words.

• An Intuitive Feel of Data 

Meaningful data insights are not always visible or apparent immediately. This is especially true for large data sets, and even more so if they are unstructured. Also, before starting to manipulate and process the data a data scientist should know what the aim of their operations is. In order to decide this aim the data scientist has to extract some kind of rudimentary pattern or meaningful insight from the data just by studying it in its original state. This takes a highly attuned feel for the data. This kind of intuition only comes from experience in working with large data sets. This is why a data scientist should always keep themselves occupied with new projects so that they can gain a bread of experience and exposure to new kinds of data sets.

Conclusion 

From the above list, it can be easily seen that becoming a data scientist is not so easy. It requires a lot of different skills and qualifications which are very diverse. It is not so easy for one person to gather all these different skills. To develop all these skills and gain the experience in applying them, one should get a data science certification such as Careerera’s PGP in Data science course.

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Important Skills of Data Scientist Important Skills That a Data Scientist Must Have