In the Data Science industry, the year 2018 will be the one to watch in US as the business community faces a severe demand-supply gap in Data Science jobs. McKinsey Global has projected fresh Data Science job openings of up to 180,000 in US alone. This forecast may very well reflect the global need for Data Scientists in that year.
The salary reviewer Glassdoor states that the average salary that a Data Scientist fetches is an impressive $116,000. Moreover, the global Big Data market is expanding at breakneck speed as more and more businesses are going through a widespread Big Data adoption, further contributing to the gap in demand and supply within the Data Science community. Thus, it is evident that highly skilled Data Scientists are in urgent demand, and yet the average workplace is so short of experienced employees.
The Forbes blog post titled Why Data Scientist Is the Best Job to Pursue in 2016 probably echoes the above sentiments aptly. A natural approach to tackle this talent gap in the Data Science field is to train up the existing breed of senior programmers to fill the void. In many businesses, the business owners and operators are seriously considering training up the highly qualified and experienced programmers to fit into the newer Data Scientist roles. The easiest way to do this is to filter out the interested and experienced programmers and counsel them on available retraining choices for stepping into Data Science.
According to What is a Data Scientist and should we instead be talking about data teams? Data Science, in many cases, has become synonymous with Data Analysis. This is a rather unfortunate situation for business as the so called analysts simply extract data from huge datasets without paying any attention to the extent of data corruption in those data sets. The modern breed of Data Scientists have to be appropriately trained to recognize and extract the right “data” for answering their questions, so that the answers are completely free of preconceived notions or biases. Data Science Training for Programmers Approach 1: Going Back to School for Advanced Degrees in Data Science
In 9 Must Have Skills for a Data Scientist Burtch Works points out the most important programmers need to have to be considered for Data Science positions. The significant observation here is that most Data Scientists usually have a Master’s or PhD, as an advanced degree prepares the candidate for a research-intensive job. The other popular academic degrees among data professionals are mathematics, computer science, statistics, and computer engineering.
Some working professionals prefer advanced academic degrees as they feel that an MS or PhD in Data Science or Computer Science may offer a strong theoretical foundation required for complex business projects. As a strong background in computer science or Data Science is more essential than that of mathematics or computer engineering, the candidates going back to school should only opt for the best campus programs offered. Going back to school is not easy, as it requires full-time commitment in time, availability of funding, and availability of savings to pull through family expenses during the academic coursework. Thus, normally working programmers or junior Data Analysts may be intimidated by the thought of making such a serious commitment for their future.
So, HR Departments, in conjunction with management, may have to go through some rigorous candidate review process to find existing staff for possible academic training for Data Scientists. If the candidates show strong promise, employers may even think of partially or fully sponsoring their degree programs as a means to invest in future.
What are boot camps? Boot camps, for those who are unfamiliar with this concept, are basically brief, drill-intensive training programs to get working professionals up to speed in Data Science technologies.
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