The Data science is a concept to unify statistics, data analysis, data mining, machine learning and their related techniques and theories to extract meaningful insight from various data sources to forecast the future. This information can then be used to optimize the processes to increase the overall efficiency of a business or system. The M.S. in Data Science provides students with technical expertise in computational modeling, data collection and integration, data storage and retrieval, data processing, modeling and analytics, and visualization.
Why Master Degree in Data Science?
The job opportunities in the field of data science and data analytics are rapidly growing. International Data Corp. (IDC) expects worldwide revenue for big data and business analytics (BDA) solutions to reach $260 billion in 2022. As a result, there is a strong need of data scientists who are skilled in organizing and analyzing massive amounts of data and data scientists are among the most sought-after positions in these days and nearly 40% of advanced data and business analyst positions require a master’s degree or Ph.D.
- Top 5 jobs by Google Careers are related to data science.
- According to the U.S Bureau of Labor and Statistics, the average wage of the U.S data scientists in 2016 is $130,000.
- Data scientist has been named the best job in America for three years running, according to Glassdoor’s rankings.
Why Choose the Data Science Graduate Degree at Marshall University?
With exponentially increasing amounts of data accumulating in real-time, every business and entrepreneur in today’s society needs data scientist with the ability to turn data into a competitive advantage to augment their competitive position relative to others in the field. The elite data scientist must master essential skills: programming, quantitative skills, technologies, domain knowledge, and critical thinking. Data scientists should know how to program and have mathematical knowledge to include probability and statistics in order to conduct numerical and statistical analysis. They should also be familiar with a wide range of technologies to include analytical tools, platforms, hardware, and software. All data scientists need to have a strong understanding of the business and domain knowledge they operate in, which enables data scientists to have insight and communicate effectively with different stakeholders. The M.S. in Data Science degree program at Marshall University provides curriculum to build those critical skills and knowledge through courses in computer science, statistics and domain knowledge to prepares graduates to succeed in professional careers in a rapidly growing data science field.
What Careers in the Data Science?
Common data science jobs include Business Intelligence (BI) Developer, Data Analyst, Data Engineer, Data Architect, Enterprise Architect, Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager in various industries including government, health industries, IT, bank, engineering, education, etc.
Minimum admission requirement for full admission includes completion of a four-year bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or related program with GPA of 2. 75 or higher on 4.0 scale. Applicants with a baccalaureate degree in a major other than computer science or related program may be admitted to the program and must successfully complete the following three additional bridge courses with a grade of B or above in the first two semesters of the program:
- Data Structure and Algorithms (CS 210)
- Data Engineering (CS 410)
- Applied Probability and Statistics (ST A 345)
Whether an applicant meets the above requirements will be based on the information provided in the admission application and transcripts. International students must meet MU English proficiency standards and all other admission criteria prior to registering for the first semester of courses.
The MSDS degree requires 30 credit hours (CR) of graduate work. The 30 CR is comprised of the following components:
- Required Core courses (18 CR)
CS 511 Advanced Programming
CS 630 Machine Learning
CS 660 Big Data Systems
CS 670 Visual Analytics
STA 535 Statistical Data Mining
STA 634 Statistical Methods for Researchers
- Domain Emphasis (12 CR)
Domain Emphasis gives students a good understanding of a particular domain. A student is required to take 9 credits hours in one domain emphasis and 3 credit hours of free elective in any of the three domain areas:
- Computing Domain
This domain emphasis tackles computing areas including high performance computing, cloud computing, IoT, Artificial Intelligence, Cybersecurity, bioinformatics, etc. Students in Computing Domain should take any three courses from the list below:
CS 505 Computing for Bioinformatics
CS 539 Introduction to Artificial Intelligence
CS 540 Digital Image Processing
CS 600 Advanced Web Technology
CS 601 The Internet of Things
CS 602 Cloud Computing
CS 620 Applied Algorithms
CS 645 Advanced Topics in Bioinformatics
CS 681 Thesis
- Information Systems
This domain emphasizes the use of information technology and their expected utility of their information systems. Students in Information Systems Domain should take any three courses from the list below:
IS 545 Health Care Data Analytics
IS 600 Management Information Systems
IS 610 Systems Quality Assurance
IS 621 Information Structures I
IS 622 Emerging Tech in Info Systems
IS 623 Database Management
IS 624 Data Warehousing
IS 665 Health Care Enterprise Info Syst
IS 681 Thesis
- Predictive Analytics
This domain emphasis gives students the opportunity to learn the use of various statistical modeling techniques that are applicable to predictive analytics. Students in Predictive Analytics Domain should take any three courses from the list below:
STA 512 Regression Analysis
STA 513 Experimental Designs
STA 520 Nonparametric Statistics
STA 564 Statistical Computing
STA 570 Applied Survival Analysis
STA 662 Applied Multivariate Statistical Methods
STA 663 Time Series Forecasting
STA 664 Bayesian Statistics
STA 665 Advanced Statistical Learning
STA 681 Thesis
- Computing Domain
Thesis Option in Domain Emphasis
Student may choose a thesis option replacing two courses from in the Domain Emphasis.
The thesis option (Thesis 1 and 2) offers students an opportunity for in-depth understanding and investigation into an area of interest. Students must summarize their thesis work in the form of a formal written document and deliver an oral presentation. Thesis work is typically conducted over two semesters. The thesis option can be taken after the completion of 12 credit hours. The 6 CR of the thesis option cannot be combined in a semester. If a student in the thesis option wishes to switch to the non-thesis option, the credit hours for the thesis will not count toward fulfilling the graduation requirement.
Plan of Study
Below is a typical two-year study plan for full-time (9 credit hours a semester) students:
|1st||FA||CS 511 Advanced Programming||3|
|STA 634 Statistical Methods for Researchers||3|
|Domain Emphasis Course 1||3|
|SP||CS 630 Machine Learning||3|
|STA 535 Statistical Data Mining||3|
|Domain Emphasis course 2||3|
|2nd||FA||CS 670 Visual Analytics||3|
|CS 660 Big Data Systems||3|
|Domain Emphasis course 3 or Thesis 1||3|
|SP||Domain Emphasis course 4 or Thesis 2||3|
Note: All required core courses will be offered every semester. However, some elective courses may only be offered one semester a year. Students should work closely with advisors in developing a study plan.