Eight Data Science Specializations, and Why You Should Pick One

The COVID-19 pandemic hasn’t stopped the rise of data science — businesses across all industries are continuing to harness the power of data for a competitive edge. The U.S. Bureau of Labor Statistics projects rapid employment growth in the data science field over the next decade, predicting that the number of jobs will increase by about 31% through 2030.

Data science is also a field that spans numerous industries and encompasses both quantitative and creative skills. With increased interest and demand, the scope of what it means to be a data scientist has evolved considerably alongside increasing investment in both data science and broader analytics fields. A company that’s hiring for a data scientist or building a data science team could be looking for a statistician, a machine learning engineer, or a database manager, among numerous other roles.

Mastering data science requires a set of core skills, ranging from advanced mathematics to the ability to look at any given problem and think about what data sets and statistical methodologies could help you discover a solution. However, data scientists should still consider specializing within a domain.

Specialization lets you establish yourself as a trusted resource within your domain, helping you to increase your influence when you need to feature your expertise on a resume, or when you need to present your ideas to other stakeholders in an organization. Most importantly, specializing gives you more freedom to leverage your strengths and work on projects that you’re especially passionate about.

Many data scientists pursue graduate education as a way to gain the comprehensive skill sets they need to successfully navigate the field. One of the most important factors to consider for a data science program is the option to customize curriculum to your unique interests with your choice of elective courses. Elective courses allow you to focus on topics that suit your career path so that you can begin to develop a unique specialization.

Let’s take a look at some of the areas of specialization within data science.

Data mining and statistical analysis

 
Data mining involves the analysis of large sets of data to produce meaningful information. Experts in this specialization apply statistics and predictive models to reveal patterns, trends, and correlations in data. This information can be used to predict future outcomes and to develop business solutions.

Data engineering

 
You can picture a data science team as a relay race, where a data engineer hands off the baton to a data scientist. Data engineers build and maintain frameworks that transform data into a format that is useful for analysis. This involves consolidating, cleansing, and structuring data from different sources into a single warehouse.

Database management and architecture

 
Data architects visualize and design the “blueprint” for the complete digital framework of an organization. Specialists in this domain often work with business leaders and data science teams to create new solutions for how information within an enterprise will be organized and used by various stakeholders. Data architects normally start off as data engineers, and move up in position as they develop expertise in information management.

Machine learning engineering

 
Let’s return to the analogy of a data science team being a relay race. During the final leg of the race, a data scientist hands off the baton to a machine learning engineer. Data scientists develop theoretical models, which machine learning engineers feed into self-running software to make the model work on a larger scale. Compared to general data scientists, machine learning engineers have a strong focus in software engineering principles.

Business intelligence and strategy

 
Business intelligence analysts work hand-in-hand with data scientists to analyze data and develop insights that can help improve business performance. Through the use of data visualization, data analytics, and data modeling, business intelligence analysts identify patterns and trends that help inform a company’s future strategy. Data scientists primarily focus on designing new algorithms to answer hypothetical questions, whereas business intelligence analysts apply existing algorithms to uncover information about the performance of a business.

Data visualization

 
Data visualization specialists present data with interactive visual tools, such as graphs, charts, and infographics. Visual tools allow data science teams to better comprehend trends, outliers, and patterns in data so that they can derive meaningful insights from the data. Visual tools can also be used to communicate information to business stakeholders in an impactful way.

Operations data analysis

 
Operations analysts identify areas of improvement in business operations using data provided by other members of the data science team. Then, they use statistical software to evaluate practical solutions to business problems and advise the managers on the best course of action. The operations analyst specialization requires complex problem-solving skills, but it’s less technical than other domains of data science.

Marketing data analysis

 
Marketing analytics is the practice of studying data to measure and improve the effectiveness of marketing campaigns. Analytics tools help marketing analysts to determine the return on investment of marketing efforts, to understand big-picture marketing trends, and to identify opportunities that accommodate customer preferences.

 
The Pace University online Master of Science in Data Science features a STEM-designated curriculum that can expand your knowledge of effective data governance and prepare you to apply industry-standard tools. Data science courses at Pace are led by Seidenberg faculty, including practitioners with backgrounds in the private sector and researchers who actively push the boundaries of the field. You’ll explore the theoretical concepts and best practices that have become vital to daily operations as well as long-term strategic planning for organizations.

Students in the data science master’s program build the skills to:

• Implement tools including Spark, Hadoop, MapReduce, MATLAB, and Weka
• Discover strategic insights through data mining and predictive analytics
• Deploy automations for managing data efficiently and ethically
• Use programming languages such as Python, R, and SQL
• Clean and structure data for a variety of applications
• Work with machine learning algorithms