Selecting a computer science concentration: Data analytics vs. machine learning

  • Selecting a computer science concentration: Data analytics vs. machine learning

    If you have earned a bachelor’s degree in Computer Science/Information Systems, or if you are currently immersed in a career in this discipline, you may be interested in gaining more perspective in this fascinating subject area. An advanced degree in computer science can help you improve your understanding of this insight-driven line of work. When selecting a graduate degree, you may want to choose a specialization in one of the two major areas of computer science, which are data analytics and machine learning.

    Find out more about these two disciplines of computer science, view a sample of relevant graduate degrees and decide your career path by taking a look at the functions and job outlooks for these professions.

    Data analytics vs. machine learning

    When deciding what type of computer science program you’d like to apply to, you should decide which field of this analytical practice you would choose as your specialization: Data analytics or machine learning.

    Data analytics involves the creation of reports or models of past and present data and drawing useful conclusions from this insight to move business forward. This area helps organizations track their goals and make educated decisions.

    Meanwhile, machine learning is the discipline of compiling large amounts of information and generating predictions that can benefit organizations. This process can cut business spending, create an improved customer experience and develop higher-level processes at all ends.

    Organizations typically use either data analytics or machine learning in certain aspects of their business. When might a business use one over the other? According to Medium, organizations are more likely to benefit from machine learning when they have an idea of their end goal but do not have access to the information to make it happen. In contrast, they are more likely to use data analytics when they have this information but perhaps do not know what decisions to make with them. Though they both help businesses reach an end goal, these processes are not interchangeable.

    If you feel more engaged with automation processes, you may benefit from a graduate degree that specializes in machine learning. However, if you are more interested in the data-driven, analytical side of business, you may participate in more courses in this scope.

    Sample computer science graduate programs

    If you’d like to specialize in machine learning, you might favor Carnegie Mellon University, which welcomed the first machine learning department in the world. At this illustrious university, you can pursue a Master of Science in Machine Learning, which incorporates core courses with various electives and a research opportunity under the supervision of a faculty member. If you’d like to further enhance your education in this field, you might apply for the Ph.D. in Machine Learning program. This competitive doctoral program can mold future leaders through interdisciplinary coursework, hands-on practical elements and forward-thinking research.

    The University of Washington has a program specifically designed for full-time professionals who plan on balancing their graduate education with their present work schedules. The Allen School’s Professional Master’s Program (PMP) offers a M.S. in Computer Science & Engineering, which is typically earned in approximately two years. This program can help individuals gain skills to advance their careers and improve the quality of their everyday work. A sample of the courses offered by the PMP program include:

    • Machine Learning/Data Mining
    • Applied Algorithms
    • Artificial Intelligence
    • Performance Engineering
    • Applied Cryptography

    Computer science career outlook

    Careers in computer science and data analytics are some of the most lucrative, in-demand jobs on the market. Since they require a high level of skill and accuracy, these professionals are not a dime a dozen.

    If you’d like to implement your expertise in machine learning in a professional environment, you might pursue a career as a machine learning engineer. These highly analytical individuals develop new technologies using machine learning frameworks and create algorithms to improve on business metrics. PayScale reported a median salary of $112,362 for these professionals across a variety of experience and education levels.

    One of the most common career paths for individuals who study data analytics and machine learning is a computer and information research scientist. These professionals help scientists and engineers with complex computing problems they face on a regular basis and develop software systems that improve the efficiency of computer systems. With a job growth outlook of 19 percent through 2026 and a median salary of $114,520, as reported by the U.S. Bureau of Labor Statistics, this career is as lucrative as it is crucial.

    If you are more interested in the mathematical aspect of analytics, you might be interested in a career as a statistician or mathematician. In this profession, you can assist business employees and engineers in solving problems related to data analytics and mathematical techniques. According to the BLS, the job outlook for these professionals is extremely high, with an expected growth of 33 percent through 2026.

    In order to get a career as a computer and information research scientist or statistician, you need to have a master’s degree in Computer Science/Information Systems or a related field. Since these positions require in-depth analysis and high-level processes, many candidates benefit from gaining a doctorate degree during their career. Although you may be able to get a career as a computer hardware engineer, computer network architect or other related field with a bachelor’s degree alone, you may want to earn a master’s degree to get a leg above the job market competition.

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