The perspective of the word DATA has changed drastically over the decades and more so in recent years. The practice of collecting data in yore for mere bookkeeping has today become a matter of wise investment to create a gold mine for future. Hence every sector like government, corporations and academia are investing heavily on getting their Enterprise Information Management Architectures in place.
The prospect of tapping into the DATA generated in an enterprise backyard or on the World-Wide-Web, has suddenly spurred the need of unique talent. The name given to that talent ranges from the fancy ones like Data Scientists/Big Data Engineer to a little humble ones like Data Engineer/BI engineer/Data Architect to that of an old school ones like Data/Business Analyst. Currently these designations are more or less interchangeably and ambiguously used in the industry for work which fundamentally involves Storing of data from varied sources – Summarizing it – Reporting it for varied window of time periods and Building insights from it to essentially evaluate a problem or an opportunity and build a use case around it.
The reason these designations are sometimes fuzzy to their actual work definition is because the gap between the Information Management and Decision Science teams is narrowing. As the data driven business strategies evolve gradually, these roles are carving their distinct responsibilities and eligibility criteria so as not to be confused or overlapped with their actual strengths and expectations. But in that list, the Data Scientist role stands out as it’s a role where the overlapping of those strengths is more or less a mandate than a consequence. And because of the rareness and rising demand of this interdisciplinary talent, it is labeled as the sexiest job of the 21st Century by Harvard. Today when everybody is waking up to the call of DATA driven fortune making, corporate houses are desperately seeking this One-Man-Army called Data Scientist, who can visualize and execute an end-to-end data driven strategy to solve complex business problems and tap on growth opportunities.
A Data Scientist is someone with deliberate dual personality who can first build a curious business case defined with a telescopic vision and can then dive deep with microscopic lens to sift through DATA to reach the goal while defining and executing all the intermittent tasks.
Now, with almost a decade long tryst with “data” driven development, I am just adding my own thoughts to give a broader scope of what Data Science is and what it takes to get started on the path of becoming a Data Scientist. A Data Scientist is someone with deliberate dual personality who can first build a curious business case defined with a telescopic vision and can then dive deep with microscopic lens to sift through DATA to reach the goal while defining and executing all the intermittent tasks. Each of those intermediate stages requires knowledge of tools techniques and domain which can sometimes be very much diversified. Some of those broader topics which are used in those stages and which are a must for an aspiring Data Scientist are listed below.
The above list is just for the mindset benchmarking and gives a broad overview of what one minimally needs to get started on core Data Science road map. The list can get quite exhaustive with specifics but that is not the intent of this article. There are many Data Scientists and evangelists who have taken a dig at this and few of them whom I personally love are Drew Conway’s Venn Diagram and Swami Chandrasekaran’s Metro Map.
Hope this adds some value to your own thoughts. Feel free to comment as even I am learner and would love to know your views.
Originally posted on my website Sapanpatel.in
Author: Sapan Patel, Data Engineer @ Amazon || Sapanpatel.in - © 2014, all rights reserved