10 Areas of Expertise Within Data Science

10 Data Science Specialties

expertise in data science

Data science and the many areas of data science therein, 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.

  • Statistics and Probability
  • Python
  • Machine Learning
  • Data Processing
  • Data Visualization
  • Data Mining
  • Predictive Analytics
  • Big Data
  • Modeling
  • Data Consultancy

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.

  • Directly-Involved Careers
  • Epidemiologist
  • Statistician
  • Business Intelligence Analyst
  • Social Science Data Analyst
  • General Data Scientist

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.

  • Directly-Involved Careers
  • General Data Scientist
  • Python Engineer
  • Software Developer
  • Web Developer
  • Researcher

3. Machine Learning

specializations in data science

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.

  • Directly-Involved Careers
  • Machine Learning Engineer
  • General Data Scientist
  • Big Data Developer
  • Machine Learning Research Scientist
  • Java and AI Developer

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.

  • Directly-Involved Careers
  • Data Engineer
  • Data Processing Specialist
  • Product Administrator
  • Database Technician
  • Data Analyst

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.

  • Directly-Involved Careers
  • Analytics Engineer
  • Data Engineer
  • Data Science Manager
  • Data System Project Coordinator
  • Graphics Designer

6. Data Mining

how to specialize data science degree

Data mining is the important data science specialty area that focuses on finding certain patterns in large pools of otherwise loose data. Once these patterns and associated values are established, they can be further utilized in machine learning, big data, and numerous other data science venues. Ultimately, to be effective at data mining, one must understand and operate by the seven foundational elements of data mining process. These key elements consist of pattern tracking, classification, association, outlier detection, clustering, regression, and prediction.

  • Directly-Involved Careers
  • Data Scientist
  • Data Scientist-Analyst
  • Data Engineer
  • Content Data Expert
  • Cartographer

7. Predictive Analytics

Predictive analytics is used all throughout the data science sector as well as throughout many of the other data science specialty areas mentioned here. Such prominent use and demand stems from this concept’s value in looking ahead and essentially predicting certain outcomes and circumstances via a specialized look into data sets. The value in being able to foresee a range of future events is obviously incredibly high in any industry out there, and as such, this specialty area of data science will always be an important part of the picture.

  • Directly-Involved Careers
  • Analytics Predictive Analyst
  • Advanced Analytics and Statistics Manager
  • Data Scientist
  • Predictive Analytics Modeler
  • Research and Analytics Specialist

8. Big Data

specialized data science

As its name suggests, “Big Data” is the term given to extremely large sets of data. In the data science world, these particular sets of data are said to be characterized by “The 4 Vs”. These four, telling attributes, all starting with the letter V, are volume, variety, velocity, and veracity.

Places where one might find such significantly expansive data sets like this include media companies, policing agencies, government bodies, financial institutions, and many others. Here, these extremely large data sets manage all kinds of info from customer information to financial figures, demographics info, and so on. The potential causes for the use of big data sets are virtually endless.

  • Directly-Involved Careers
  • Data and Analysis Engineer
  • Data Analyst
  • Data Engineer
  • Data Integrator
  • Big Data Developer

9. Modeling

In data science, there is a very regular need for the use of illustrations and diagrams in looking at varying kinds of data. Through the use of these visual tools, workers can then identify valuable patterns and other markers as well as generally work with that data. An example of data modeling at work might be a graph and chart setup designed to show a company’s purchase costs history. Workers can then visually see a representation of the data, courtesy of data modeling, which makes conceptualization much easier than in looking at simple, large sets of numbers in rows and columns.

  • Directly-Involved Careers
  • Enterprise Architect
  • Business Objects Developer
  • Data Analyst
  • Predictive Analytics Modeler
  • Data Scientist

10. Data Consultancy

Finally, data consultancy is a sort of operational collection of the many specialties and even general practice areas of data science. In this line of expertise, the worker, in this case, called a “consultant”, works with clientele from different companies to help provide advice on their various data science needs. This is an outside contracting position in which the worker provides their services to paying customers with whom they have no other affiliations. From start to finish, a basic rundown of the process includes initial consultation, assignment to work on a specific issue or issues, investigation of those issues, presentation of a report on the findings, and subsequent work with the client to fix or improve upon those issues.

  • Directly-Involved Careers
  • Data Scientist
  • Data Researcher
  • Data Science Consultant
  • Predictive Analytics Modeler
  • Common Data Science FAQ

How Do I Get Started in Data Science?

Today’s data science field is larger than ever. With such rapid expansion, there are many ways with which one can get started learning about data science. One of the most common approaches is the traditional pursuit of the subject via technical schooling or college classes. The current list of colleges and tech schools offering courses on the many areas of data science is incredibly large and growing.

Another route to getting started in data science is through self-study and the use of free online learning platforms. There are many great books and videos out there that cover these subjects, from the basics, up to advanced practices. In addition, a growing list of online data science teaching resources is available from sites such as Google AI Education, Coursera, Toward Data Science, and many others.

When Did the Data Science Field Begin?

Most experts call 1962 the official beginning of the data science field. It was at this time that mathematician John W. Tukey theorized the coming of the field through the coming emergence of modern electronic computing. Soon after, in 1964, the first desktop computer, the Programma 101, was unveiled to the public, and this began modern computing and the subsequent use of data science therein. Later, in the 2000s, various scholarly institutions began to officially recognize data science as a legitimate science, and subsequently, the science was born as a very real, studied, and taught discipline.

What Do Experts Forecast for the Future of Data Science?

Predictions for the future of data science can be considerably difficult to form due to the complexity of this particular science. What we do know, though, is that some of the most in-demand and growing areas of data science are those of artificial intelligence and machine learning. Does this mean that eventually, data science will all be handled automatically via machines? This could be possible, but such a future, according to the experts, is still quite some time away.

Another great indicator one can look to in order to better understand the future of this field of work is the Bureau of Labor Statistics. Per the BLS, the many occupations in data science are in great demand and will continue to be for the foreseeable future. Through 2029, the bureau indicates a continued 15% demand growth rate. Also per the bureau, this rate is significantly above average for most occupation growth rates (https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm).

What Are Some Similar Fields to Data Science?

For those interested in working in a related field but one that is just outside of the general spectrum of data science, there are a number of great choices to choose from. Information system managers are one example in which the worker coordinates, plans, and directs the many computer-related tasks that take place in a company. Database administrators also perform related work to those working directly in the data science field, except these particular professionals focus solely on methodology for storing and organizing data. They then use specialized software to help perform this task.

Information security analysts work in close proximity to data science workers in their pursuit of maintaining a secure environment for their company’s various computing and data systems. Likewise, network and computer systems administrators also try to maintain such secure environments while simultaneously coordinating day-to-day system functions. These are just a handful of examples of the many career option that are very similar to those in data science but that are slightly outside of its spectrum.

Additional Resources

For those interested in learning even more about data science and the many specialty areas therein, there are a number of excellent resources out there with which further inquiry is highly recommended. The following represent some of those high-quality, industry-leading points of contact.

Association of Data Scientists

The Association of Data Scientists is a leading, collective group composed of many data scientists and machine learning professionals around the globe. While membership brings with it some great, additional perks, no membership is required in order to simply inquire with and work with the organization in a wide variety of ways. As a non-member here, one can find loads of expert guidance, contacts, networking opportunities, and plenty more.


Informs is a vast network of data science and data research experts. In fact, this organization is widely considered to be the largest, single, professional society for analytics and data science in the world. With little time needed, visitors to the Informs website can find all sorts of resources, contacts, and networking avenues for the data science sector as a whole. If a particular subject area of info is tough to find at the website, the organization will help and provide guidance readily upon quick contact.


IDEAS is the International Data Engineering and Science Association, and this is a big-hitter in the data science field and among the various groups therein. IDEAS acts as a total industry consortium for those currently working in data science as well as anyone interested. In either case, all interested in learning more about this field of expertise are encouraged to check out this group.

Data Science Central

Yet one more, excellent resource point for those interested in learning about data science and its many applications is that of Data Science Central. Data Science Central is a noteworthy organization in the industry composed of all manner of experts therein. For those looking for the full array of resources, this is a great spot to start. Find job listings, information resources, expert guidance, industry event info, contact points, and plenty more, all right here.

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

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