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The Data Scientist is one of the hottest career options in the USA for 2018, According to Glassdoor. It is not surprising that the annual salary paid to a data scientist professional is $123,000 on an average. Still, the data science market is not saturated and there is an estimated shortage of 190,000 data scientists in the USA alone.
If you are planning to start a career in data science then 2019 offers a plethora of job opportunities for aspirants. The aim of this blog is to highlight the top skills that are actually needed to become a successful Data Engineer.
Here, you can quickly notice that data scientist space is heavily men dominated who account for 70 percent out of 1000 profiles. Still, it does not signify that there is no space for skilled women. With an ongoing demand in the field, there is a place for everyone who is knowledgeable either male or female. The only condition is that you need to be qualified and skilled enough for getting hired by recruiters quickly. In the next section, we are listing a set of top skills needed by every Data Scientist to become successful in the domain.
To become a data scientist, there is a need for hard skills and soft skills both. It is tough to summarize skills into a few categories. Still, we tried to include maximum details here for your reference.
This is one of the most fundamental skills set of a Data Scientist. Today, the job role of a data scientist is much more applied as compared to the traditional statistician. Programming is certainly important in multiple ways and three popular styles are given below.
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The software engineering training will help you in gaining necessary skills here. It would be great if you have worked as a software programmer with some IT Company before to apply for the Data Scientist role.
Quantitative analysis is put at the heart of Data Scientist skillset. Data Science is all about understanding the behavior of a complex system by analyzing data produced by the system. Data can be analyzed either naturally or via experiments. The quantitative analysis is needed in multiple ways and few of them are given below.
Because of this tough analysis, data science field is pretty attractive to statisticians, economists, operation researchers, physicists, and more.
Product knowledge is one of the important skills set for data scientist that defines its ability to perform quantitative analysis on the system. Product knowledge means understanding data generated by complex systems and all of this collected data is analyzed by data scientists. It is important for quite a few reasons given below.
The product knowledge usually includes information about the system that is created by your Company. If it is not possible then try to get to know people who can use the product.
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This skill is probably more important to leverage all the skills listed above. It is particularly important and helps to distinguish a good data scientist from a great one. The communication skills can manifest in multiple ways as given below.
Teamwork connects all the listed skills together. A data scientist cannot exist in isolation so it is necessary for him to learn the teamwork for effective findings. Let us learn how teamwork is important in multiple ways:
Most data scientists are highly educated and research suggests that 75 percent of data scientists have either a master degree or Ph.D.
At the same time, a fancy degree is your choice but it is not mandatory. Almost 25 percent of data scientists are graduated from average universities, still, they are able to start a good career in the data science space.
Most data scientists have a degree in Computer Science, IT (Information Technology), Statistics and Maths, Social Science etc. you can be more established in the data science domain if you have a quantitative background. Therefore, you don’t have to necessarily jump to an extra academic program to acquire the skills needed to become a Data Scientist.
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To upgrade your skills, online Data Science courses are more suitable. Nearly 40 percent of data scientists reported that they enrolled in the online course and it is clearly mentioned in their LinkedIn resumes too.
The first two skills quantitative analysis and programming should be taken seriously when planning to start your career as a Data Scientist. These skills are necessary to create the technical foundation of a data scientist skillset. Last four are not technical skills but they are soft skills and are equally important. The third skill is optional and taken into consideration when you are working with a service-focused Company.
The fourth and fifth are critical to every data scientist while you start working within an organization. The education background sets the eligibility for the profile and makes you a suitable choice for the data scientist position. Good luck.
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