The industry is promising immense opportunities. The compensations are among the highest. Roles are multidimensional and multifaceted. Clearly, the prospects look bright for the Data Science segment, and possibly make it the ‘aspiration of the decade’.
Such technology-driven surge may lead the youth to take up different courses in Data Science, more often thoughtlessly. Adding to the confusion is a wide variety of online courses, available to promise a similar career progression as any other degree. Surely, there is more than one way to make to the top, yet having a degree from a reputed institution helps.
So, what is the homework one needs to do before taking up a program in Data Science? What are the questions you must ask before joining a Data Science Program? Here are 5 of them!
1. Is the course content of the program recently developed, or randomly repurposed?
Data Science is a contemporary domain. The course content needs to evolve continuously and remain relevant to the demands of the industry. Most of the Data Science concepts have been developed in the recent past, not earlier than the past decade, building upon the strides made by computer technologies. In such a case, it is critical to confirm that the course being undertaken is relevant and contemporary, and not just repurposed and repacked from archaic course materials which are redundant.
2. Are the system languages being taught updated and in-use?
Most of the Data Science programs offered today are around the different system languages like Python, SAS, R, SQL, etc. However, with technological advances, these languages are getting updated at a fast pace. At the same time many of the languages are getting redundant and out of use. Thus, it is critical to take up a course which is futuristic, in sync with the industry and imparting training in the languages which are there to stay.
3.What vocational role is the particular program leading you too?
Industry offers multiple job roles for Data Science graduates. While data lies at the core of all, they differ basis the language skill each vocation require. A Data Architect is more of a software engineer who helps in developing and maintaining different data systems. A business analyst might not be required to know much about the technology, but only about the insights one can derive out of them. Hence, it is important to understand one’s competency, interest and goal while undertaking a Data Science program.
4. Is the program strengthening your basics and soft skills?
The world of Data Science is evolving. Quick and radical changes in technology may trigger complete obsolesce of certain set of skills. Yet, the fundamentals and the soft skills are there to go a long way. Be it the persuasive communication skill, or managing a team, it is important to check if the course offers beyond the system information and languages. An addition of basic statistics and analytics is certainly a plus.
5. What is the value add being offered?
Data Science leads one to multi-dimensional and diverse roles. The scope goes beyond data munging, processing or analytics. A lot of work is around usage of the knowledge out of data by different business processes. Thus, a value add of Project Management or Business Analytics certification makes for a good all-round program design. A few extra dollars spent would never hurt.
Above all, it is important to understand your ‘calling’. All the noise around Data Science might make it look like the-thing-to-chase. Yet, it is important to realize one’s own aptitude. Nonetheless, if one has keen interest in data, is at ease with technology, and has the knack for analysis and insights, then Data Science is surely the program for you to take.