Data Science: Three Critical Skills
Data science is the process of deducing insights, information and other meaningful things from bodies of data. It necessitates a combination of skills in three core areas, namely, Mathematics, Technology and Business.
At the root of drawing data insight and constructing data product is the ability to look at the data from a quantitative perspective. There are textures, dimensions, and connections in data that can be conveyed mathematically. Seeking solutions that depend on data becomes a puzzle of quantitative technique and heuristics. Relief to several business complications entail drawing up analytic models anchored in the hard math, where an understanding of such models’ implicit mechanics is the trick to their successful construction. There is also a misconception about data science being all about statistics. Although statistics is definitely important, it is not the only math used in the field (for instance, linear algebra is also used).
As a necessary skill in data science, technology is associated with hacking, or the ingenious use of technology in formulating clever answers to problems. Why does this field require hacking ability? Because data scientists apply technology to work with ginormous data sets and work with complicated algorithms, requiring tools far beyond Excel. Data scientists must to be able to code, create fast solutions, and integrate with multiple data systems. There are four key languages data scientists use, namely, SQL, Python, R, and SAS. Also used, albeit to a lesser extent, are Java, Scala, Julia, and many others. But it does not just require knowledge of language basics. A hacker is a technical virtuoso, able to successfully steer their way around technical hitches to make their code work.
A data science hacker is hence an algorithmic thinker, possessing the ability to dissect sizable problems and reconstitute them so they are simpler to solve. This is vital as data scientists work with so much algorithmic complexity.
A data scientist should be a strategic business consultant. Since they deal mainly with data, they can learn from data in ways that only they can. This forms the responsibility to interpret observations to impart knowledge, as well as to contribute to the solution of integral business problems. This shows that a core competency of data science is applying data in cogently narrating a story. No data baring – instead, give a coherent take of problem and solution with the use of data insights as support, leading to guidance.
Business acumen and tech and algorithm acumen are of equal importance. There must be clear consistency between data science projects and business goals. But neither data nor tech will finally offer the value. It is sourced from leveraging all the necessary skills en route to developing required capabilities and strong business influence.