Master’s in Data Science
What is Data Science?
Data science is an evolving field of study and professional practice; it is a high-demand career path for technology professionals. Data science studies information and an area called Big Data. The field of data science applies traditional skills and knowledge, including data analysis, data mining, and programming language skills. Data scientists must always move beyond the traditional methods and to discover useful intelligence for their clients and organizations. Data science uses the complete data science life cycle, and data scientists must demonstrate flexibility and deep understanding of each cycle to provide maximum benefits for their clients and employer organizations.
The backgrounds that contribute to excellence in the field of data science include computer science, information technology, and statistics. These fields of study make the way for data analysis and interpretation using scientific methods. Master’s degree programs in data science prepare students to use the established methods for managing data and to scale them to work with new sources of data, massive amounts of data, and data that comes from technology advances and innovations like sensing and communications breakthroughs.
The effectiveness of data scientists sets them apart in the crowded and overlapping fields of information technology and computer science. Data scientists can create questions that are relevant to a company or organization. They can collect data from many types of sources both internal and external to the organization. Data science organizes information and translates the data into meaningful results. The results can provide business and organizational solutions. Communication is a key aspect of effective data science. Data science can enhance business decision-making and create productive business activity.
The demand for data scientists is high. The skills and contributions of data scientists are essential to nearly every kind of business or organization. According to the Center for Digital Education, they describe Data Scientists as highly educated professionals, with eighty-eight percent of data scientists holding at least a master’s degree. They also indicated that a combination of knowledge through education and experience are needed to be successful in the field.
A master’s degree in data science is a ticket to a career in high-growth technology fields that exist and that develop through further advances. A master’s program equips students with theory about data acquisition and data systems architecture. A master’s program in data science requires a solid background in one or more information or computer science disciplines.
A Master’s Degree Program in Data Science
The master’s degree in data science is a one- or two-year program that consists of about 30 semester hours of coursework and some type of capstone project. The coursework will have a core of about 18 credit hours and electives for about ten credit hours. Most programs have a capstone which usually involves a hands-n project or internship.
The prerequisites are a bachelor’s degree in a related field, including mathematics, statistics, computer science, information technology, and data analysis.
Potential Job Titles
The data science titles are often defined by the business focus of the employer organization. Machine leaning companies might title theory positions differently than a company focused on market analysis. The below-listed items describe some of the widely used current job titles for data scientists.
Career and Salary Outlook
The US Bureau of Labor Statistics does not list Data Scientist as a standard classification. The BLS Occupational Outlook Handbook places data science in a category of Computer and Information Research Scientists. 3/ Within the broader grouping, the BLS estimates above average growth for data science. The expected growth range is approximately 19 percent, which is about twice the average rate of growth for all occupations.
The salary outlook is robust. According to PayScale, the entry-level range is approximately $88,000 per year. The mid-career range shows the value of experience as it rises to $110,000. While the occupation is relatively new and still evolving, the Late-Career estimate demonstrates long term career benefits, the average for the most experienced scientists is above $145,000 per year.
The typical Master of Science degree curriculum consists of about 30 to 36 semester credit hours. At most schools, the curriculum has a core of about 18 to 21 credit hours and electives for up to 18 credit hours.
Core course subject areas
The typical core course requirement is 18 to 21 credit hours or six to seven core courses. The below-listed items describe core course subject areas.
- Data Analysis
- Data Processing (Big Data, Database Organization, Data Computing)
- Machine Learning
Typical Electives (About nine semester hours):
The usual format requires three or more courses for nine to 12 credit hours. The student may choose courses that suit their interests and career goals. Most schools offer advisors that can assist and approve the selection of electives to ensure that the student can reach his and her goals.
Computer Science – This area of study has the fundamentals of computation. They include database organization, algorithms, operating systems, parallel and distributed processing, and cloud computing.
Mathematics – This area includes computational math and physics, and other types of scientific computation like bioinformatics, and biotechnology.
Statistical Mathematics – This course group adds intensive exploration in probability and statistics. The coursework includes modeling, probability, statistical learning, and Bayesian Computations.
Capstone Project (about four to six hours):
The capstone can be a project, a supervised internship, or a practicum of another type. Capstones can be individual or group projects that students can add to a portfolio of their work to present to employers and educational institutions.
Each school determines the requirements for the degree and the courses that may credit towards completion. In the field of data science, students must have an in-depth understanding of computer programming and strong skills in one or more languages. Some frequently used formats are R, Hadoop, and Python. Some programs will put these into prerequisites and others may add them as parts of the core or recommended electives.