10 Top Data Science Fields

The field of 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 data science field. The following represent ten of these important, specialized data science fields right now.  What areas of data science interest you?

  • 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. Statistics and probability are one of the most widely used fields of 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. Data scientists with training in this area are a great asset to general and specialized areas of the data science industry today including:

  • Epidemiologist
  • Statistician
  • Business Intelligence Analyst
  • Social Science Data Analyst
  • General Data Scientist

2. Python


Understanding the ins and outs of Python is a beneficial skill for data scientists.  Python was created several decades ago but remains an incredibly important programming language.  It is used in countless computer applications today. In addition, applications that do not utilize Python often require interpretation so that they work in tandem with programs that do. In the end, Python is a valuable specialty asset to know in data science.  Job titles for data scientists who need skills in Python include:

  • 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.  Watson is used in:

  • weather prediction
  • computer academia
  • even seen on the popular game show, “Jeopardy.”

IBM describes machine learning as “a form of AI that enables a system to learn from data rather than through explicit programming.” The implications of this in itself are huge and worthy of extensive literature.  It is clear to see the important connections between data science and machine learning.  Popular job titles include:

  • 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.  Beyond these basics, data is:

  • moved
  • encrypted
  • translated
  • compressed
  • decompressed

Data processing, subsequently, is the specialty knowledge area of data science that specifically handles all of these data processes.  Job roles that use data processing include:

  • Data Engineers
  • Data Processing Specialist
  • Product Administrator
  • Database Technician
  • Data Analyst

5. Data Visualization

Data Visualization

As its name suggests, data visualization is the data science specialty area focused on presenting data in a visual manner. A large portion of computer use today has to provide the end user with a way to see and visualize the data being presented. Examples of data visualization concepts include:

  • readable text
  • holograms
  • interactive data displays
  • on-screen charts and graphs

This specialty field creates new ways of visualization as well as ways to improve older methods. Data scientists working with data visualization include:

  • Analytics Engineer
  • Data Engineers
  • Data Science Manager
  • Data System Project Coordinator
  • Graphics Designer

6. Data Mining

how to specialize data science degree

Data mining is one of the most popular areas of data science.  Data mining focuses on finding patterns in large pools of otherwise loose data. Once these patterns and associated values are established, they can be further utilized in machine learning and big data analysis. 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
  • prediction

Data science experts using data mining include:

  • Data Scientist
  • Data Scientist-Analyst
  • Data Engineer
  • Content Data Expert
  • Cartographer

7. Predictive Analytics

Predictive analytics are used all throughout the different branches of data science.  Predictive Analytics are also found throughout many of the other data science categories mentioned here.  Professionals use predictive analytics to look ahead and predict certain outcomes and circumstances via specialized data analysis. The value in being able to foresee a range of future events is obviously incredibly high in any industry.  Predictive Analytics will always be one of the most important data science areas.  Jobs for this data scientist specialization include:

  • 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 attributes, all starting with the letter V, are:

  • volume
  • variety
  • velocity
  • veracity

Big data is found in a variety of different types of organizations including:

  • media companies
  • policing agencies
  • government bodies
  • financial institutions

These extremely large data sets manage all kinds of information from customer data to financial figures and demographics. The potential causes for the use of these huge data sets are virtually endless.  It is important that organizations and corporations have data science professionals who can conduct complex data analysis.  These job titles might include:

  • Data and Analysis Engineer
  • Data Analyst
  • Data Engineers
  • Data Integrator
  • Big Data Developer

9. Modeling

Data experts need to use illustrations and diagrams to look at varying kinds of data. Using these visual tools, data professionals can identify valuable patterns and other markers when 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.  This makes conceptualization much easier than in looking at simple, large sets of numbers in rows and columns.

  • 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
  • subsequent work with the client to fix or improve upon those issues

Consultants are needed in a variety of professional roles including:

  • Data Scientist
  • Data Researcher
  • Data Science Consultant
  • Predictive Analytics Modeler
  • Common Data Science FAQ

How Do I Get Started in Data Science?

How Do I Get Started in Data Science?

The field is larger than ever. With such rapid expansion, there are many ways to begin 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 in the different data science branches is incredibly large and growing.

Another way to learn about 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.   In addition, a growing list of online teaching resources are available from sites such as:

  • Google AI Education
  • Coursera
  • Toward Data Science

When Did the Field of Data Science Begin?

Most experts call 1962 the official beginning of the data science industry. 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.  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.  The science was born as a very real, studied, and taught discipline.

What Do Experts Forecast for the Future of the Field?

Predictions for the future 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, all data analysis will 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 we 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.

What Are Some Related Fields?

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 such example.  System managers coordinate, plan, and direct the many computer-related tasks that take place in a company. Database administrators (DBAs) also perform related work. A DBA focuses solely on methodology for storing and organizing data. They then use specialized software to help perform this task.

Information security analysts are in close proximity to data science professionals as they work to maintain 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 options available in the extended field.

Additional Resources

For those interested in learning even more about the field 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 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:

  • expert guidance
  • contacts
  • networking opportunities


Informs is a vast network of data professionals and data research experts. In fact, this organization is widely considered to be the largest, single, professional society for analytics and data scientists 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 field and among the various groups therein. IDEAS acts as a total industry consortium for those currently working in the industry 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 analysis 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

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 ten specialized areas are among some of the most important right now.

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