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Data Science: Five Important Areas of Expertise Right Now


  • Statistics and Probability
  • Python
  • Machine Learning
  • Data Processing
  • Data Visualization

Data science, broadly defined, is the science, study, and use of data in a digital platform. As this is a massive and growing area of expertise today, there are naturally a growing number of specializations to be found within the field. The following represent five of these important, specialized areas of expertise in data science right now.


1. Statistics and Probability

Statistics and probability represent a considerable area of mathematics that also greatly impacts data science. This specialty area is all about establishing and working with finite figures as well as the effects of the ever-present factor of “chance” in all things. Those additionally learned in this particular area are a great asset to general and specialized areas of the data science industry today.

2. Python

While understanding the ins and outs of Python isn’t always required in data science jobs, it is a growing necessity that is a valuable commodity for the worker here to present with. Python was created several decades ago but remains an incredibly important programming language used in countless computer applications today. In addition, applications that do not utilize Python often require interpretation so that they may work in tandem with those programs that do. In the end, Python is a valuable specialty asset to know in data science.

3. Machine Learning

IBM is the organization that created Watson, the world-renowned artificial intelligence computer system used in weather prediction, computer academia, and even seen on the popular game show, “Jeopardy.” Directly from IBM, machine learning is described as “a form of AI that enables a system to learn from data rather than through explicit programming.” While the implications of this in itself are huge and worthy of extensive literature, one can certainly surmise, just for the purposes of this article, the important connections between data science and machine learning.

4. Data Processing

Data processing, at its core, is the term used to describe the various processes computers use in the handling of data. Most people understand the premise of data and its simple storage, but beyond these basics, data is moved, encrypted, translated, compressed and decompressed, and much more. Data processing, subsequently, is the specialty knowledge area of data science that specifically handles all of these data processes.

5. Data Visualization

As its name suggests, data visualization is the data science specialty area focused on how data can be potentially presented in a visual manner. A large portion of computer use today has to provide the end user with a way with which to see and visualize the data being presented. Examples of data visualization concepts include readable text, holograms, interactive data displays, and on-screen charts and graphs. This specialty field works on making new ways of visualization as well as improving older methods.

Data science will forever be a vital component to human technology. With the sheer size of this field and its continued growth, specialties here will always abound as well. These five expertise areas in data science today are among some of the most important right now.

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