In recent years, data science as a field has gained significant traction as organizations look to better harness the power of the massive amounts of information they routinely generate. Most of the digital data that has ever existed was created in the 2010s, thanks to the rapid rise of networked communications ranging from cloud computing services to workplace and personal chat apps like Slack, Facebook Messenger and WeChat.
The data scientist stands at the intersection of computer science and statistics, trying to formulate reliable, scalable processes for making sense of massive datasets. But how does one become a data scientist? The U.S. Bureau of Labor Statistics groups data scientists within the broader category of computer and information research scientists, noting that these professionals typically have a master’s degree in computer science or a related field. The BLS expects 19 percent growth in total positions from 2016 to 2026, much faster than the average for all professions. Median pay was more than $114,000 in 2017.
Engineering, business, mathematics and data science itself are other common specializations for data scientists. For example, the Master of Science in Engineering (MSE) at the University of California, Riverside — an online engineering degree — features data science as one of its specialization options. Students learn the specific tenets of modern data science practice after covering foundational principles in general engineering.
On the other hand, students who forgo an MSE in favor of a dedicated data science master’s degree are likely to spend more time on coursework in computer science, including topics such as machine learning and data mining. Which approach better meets your career aspirations? Let’s compare the MSE and Master of Data Science in more depth.
MSE with specialization in data science
The UCR MSE online engineering degree is all about a balance of flexibility and rigor. While all students must complete a common engineering core, the rest of the curriculum varies based on the chosen concentration.
The core consists of the following quartet of 4-hour courses:
- Engineering in the Global Environment
- Technology Innovation and Strategy for Engineers
- Introduction to Systems Engineering
- Principles of Engineering Management
Between them, these requirements ensure MSE candidates have a blend of technical skills and management expertise so they can contribute within the context of a modern engineering team. Beyond the program core, data science students must select four additional courses from the following list:
- Foundations of Applied Machine Learning
- Application of Visualization in Data Science
- Data Mining Techniques
- Advanced Computer Vision
- Statistical Computing
- Statistical Mining Methods
- Machine Learning
- Information Retrieval & Web Search
- Data Visualization & Big Data Tools
What knowledge do students develop from these options? Let’s look at a few of the options in more detail:
As its name suggests, machine learning is the practice of teaching computers to perform specific tasks. They do not have to be explicitly programmed to do these activities, instead harnessing statistical models to continually adapt to patterns. It is the closest that classic computers (i.e., non-quantum machines) can come to the way humans think, and it’s already widely used in everyday applications like web search and speech recognition.
Advanced Computer Vision
Computer vision is the use of computerized programs to extract information from videos and images. There are many use cases for computer vision, from video tracking within camera feeds (e.g., for red light cameras or surveillance/security systems) to transforming two-dimensional images into corresponding 3-D models. Computer vision can be thought of as an algorithmic and more scalable version of what humans already do with their visual sense.
Information Retrieval & Web Search
Search engines are among the most essential internet-connected applications, helping make sense of the vast collection of web pages in existence. To deliver relevant and consistent results, they tap into information retrieval systems, which feature structures like text frequency matrices to find keywords that correspond to the user’s request.
Data Mining Techniques
Data mining is used to find patterns within datasets. To that end, it utilizes machine learning, artificial intelligence and statistical modeling to perform highly efficient analysis at tremendous scale and return results that data scientists can then transform into actionable insights.
Master of Data Science
A Master of Data Science program will consist almost entirely of coursework similar to the non-core requirements in the MSE track. In other words, it will leave out the engineering portion and instead require students to complete classes in computer science, statistics, data modeling and similar relevant topics.
This setup might seem advantageous because of its narrower focus, but it has a few differences:
- First, many data scientists do not have formal degrees in the field, instead carrying credentials from relevant domains like computer science and engineering. Accordingly, you might succeed without needing to devote yourself exclusively to data science.
- Second, by restricting your graduate education to data science without any complementary advanced background in a related field, you might be narrowing your career prospects. In its analysis of the profession, the BLS noted the need for a wide variety of skills, including communications and mathematics, for success. Completing a comprehensive program like the UCR MSE can ensure you have the right preparation.
The benefits of pursuing an online engineering degree
Earning an MSE with a specialization in data science from UCR can help you compete for positions in this rapidly evolving and in-demand field. The program is 100 percent online and can be completed via an expedited route in only 13 months. In addition, the degree you receive is equivalent to an on-campus credential and is conferred by an accredited institution.