4 Basics of Data Science

Information is the lifeblood of today’s data-centric economies. At first glance, it may seem that there is little intrinsic value in collections of facts. This is a deceptive appearance. Demographics data, socioeconomic group traits and even information about lifestyles are today’s currency of choice. New approaches to manipulating data comprise the business plans upon which new ventures are formed daily.

A department of government, consumer reporting agency or lender seem to acquire access to information at little apparent cost. What is it then that gives data its value? The answer lies more in how data is used than in the data itself. One of the fastest-growing areas of business today that connects dollars with data is data science. This high-profile function is comprised, in part, of 4 areas of expertise.

Data Scientist Learning Path 2020 | by Ashish Patel | ML Research Lab |  Medium

Computer Science Knowledge

Knowing the strengths and limitations of software and hardware is a fundamental tool in the data scientist’s toolkit. Grasping how the capabilities of programming and the mechanics of processing can be used efficiently in data analysis is key.

Mathematics Knowledge

Statistics and probability comprise this area of skill required in data science. Many advanced concepts in the relationships between facts are best understood with math. Knowing how to manipulate numerical facts in a way that accurately predicts future facts is crucial in productive data analysis.

Business Knowledge

Familiarity with the unique characteristics of a particular market sector describes this domain. Every industry has peculiarities distinguishing it from other industries. Accumulated expertise in a given industry is of utmost importance in transforming raw data into decision-making tools.

Communications Knowledge

Conveying conclusions and the methodologies by which they were reached is the fourth domain that gives actual value to the raw data input. Data science must deliver some substance upon which decisions can be based. There are extremely important subtleties in analysis techniques that defy simple explanation. Skill in crafting reports and recommendations that make these easy to understand is vital.

Data science is a combination of several skill sets that bring disparate pieces of information together in creative and constructive ways. The sophistication of artificial intelligence accomplishing this is constantly improving. As a result, the commodity value of data continues to rise.