Master of Science in Data Analytics

Curriculum

Curriculum

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Business, Technology, Data & Analytics

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In this 22-month program, MSDA students are required to complete a total of 45 credit hours, consisting of 21 credits for core and 24 credits for electives. For regular MSDA students, 9 credits must be obtained through taking International Immersion (3 credits), Internship (3 credits) and Capstone Project (3 credits). To provide more job opportunities, TOPIK IV or approximately 400 hours of Korean Language will be a requirement to graduate from this program.

FIRST YEAR SECOND YEAR
SPRING SUMMER FALL WINTER SPRING SUMMER FALL
CREDITS 9 3 9 3 9 3 9
CORE Statistical Analysis Field Study in korea Exploratory Data Analysis & Visualization
Computer Programming with R Statistical Modeling & Inference
Regression Analysis with R for Business Introduction to Machine Learning
ELECTIVES International Immersion* Electives
(9 ~ 10 Credits)		 Internship* Capstone Project*
Electives (5 ~ Credits)
EXTRACURRICULAR Mandatory Extracurricular Activity: Guided Personal Learning
　 50% Statistics and Programming 50% Marketing and Analytics

* Required Courses (graduation requirement for regular students).

Core Courses

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Core Courses Credits
Statistical Analysis ▼
This is an introductory course in statistics designed to provide students with the basic concepts of data analysis and statistical computing. Topics covered include Credit Hours: 3 SolBridge 2021 MS in Marketing Analytics curriculum basic descriptive measures, measures of association, probability theory, confidence intervals, and hypothesis testing. The main objective is to provide students with pragmatic tools for assessing statistical claims and conducting their own statistical analyses.
3
Computer Programming with R ▼
This course introduces the basics of computer programming R language. Topics include programming with R using data types, algorithms, object-oriented analysis and design. The course also takes up various programming techniques such as design, implementation, testing, trouble shooting and documentation.
3
Regression Analysis with R for Business ▼
As most research in social sciences is aimed at quantifying relationships among variables that either measure the outcome of some process or are likely to affect the process, where the process in question could be any economic, business, or management process of interest to the social scientist. The quantification of the process may be as simple as determining the degree of association or as complicated as estimating the parameters of a detailed nonlinear system. Regardless of the complexity of the model, the most powerful and widely used statistical method for estimating the parameters of interest is the method of least squares. Researchers choose the most appropriate model for the project at hand, the parameters of the model are then estimated such that model predictions and the observed data are in as good agreement as possible as measured by the least squares criterion, minimization of the sum of squared differences between the predicted and the observed points.

In Applied Regression Analysis with R, we will learn what is and how to use regression by analyzing a variety of real world problem. Heavy emphasis will be placed on analysis of actual datasets. Topics covered include: review of probability and statistics; simple linear regression (SLR); multiple linear regression (MLR); inference; dummy ariables; asymptotics; further issues on MLR; heteroskedasticity; specification and data problems; limited dependent variables; time series; instrumental variables (IV) and two-stage least squares (2SLS) (optional); simultaneous equations (optional); panel data (optional).
3
Exploratory Data Analysis & Visualization ▼
This course provides students with a thorough study of exploratory data analysis and visualization techniques. Exploratory Data Analysis employs a variety of techniques, mostly graphical, that enable the data to reveal its structural secrets and provide new insight into the data. This approach allows the data scientist to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. This course provides hands-on experience in data visualization and summary statistics using real-world data examples.
3
Statistical Modeling and Inference ▼
This course introduces two key components of statistical data analysis –
1) modeling and 2) inference. Analysis of the complex problems arising in practice requires an understanding of fundamental statistical principles together with knowledge of how to use suitable modelling techniques. Computation using a high-level software is also an essential element of modern statistical practice. This course provides students with these skills by giving an introduction to the principles of statistical modeling and inference using the freely available statistical package R.
3
Introduction to Machine Learning ▼
This course introduces several fundamental concepts and methods for machine learning. The objective is to familiarize students with some basic machine learning algorithms/techniques and their applications. The course also covers general principles and approaches related to analyzing and handling big data sets.
3

Elective Courses

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For detailed information about the elective courses, download this course description file.pdf

Technology Electives Credits Other Electives Credits
International Immersion 3 Master’s Thesis 3
Capstone Project 3 Data Analytics for Business 3
Internship 3 Statistical Analysis 3
Database Marketing 3 Project Management 3
Social Media and Digital Marketing 3 Consumer Behavior and Decision Making 3
Pricing Analytics 3 Marketing Strategy 2
Data Analytics for Business 3 Marketing Communications and Advertising 2
Managerial Skills 3 International Marketing 2
Business Communication 3 Special Topics in Marketing 2
International Business in Asia 3 Investment Analysis 3
Management Information Systems 3 Mergers & Acquisitions 3
Doing Business in Korea 3 Corporate Finance 3
Doing Business in China 3 Financial Markets and Institutions 3
Cybersecurity 2 Financial Derivatives 3
Digital Business & Innovation 2 Special Topic In Finance 3
Tech Entrepreneurship & Product Development 2 Accounting & Decision Making 3
Database Management 2 Business Communication 3
Software Engineering 2 Business Economics 3
Strategy for Tech 2

Graduation Requirements

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  • Complete 21 credit hours of core courses.
  • Complete 24 credit hours in electives, preferably including an internship or a capstone project, by the end of the fourth semester. If a student is unable to obtain an internship or a capstone mentorship, the amount of credit hours needed must be met by the available electives.


Students will go through an initial test early in their first semester and will be classified according to their Korean language skills. The Korean Language classes will be available from Beginner to Advanced level to better accommodate each student’s proficiency.

Exceptions will be given to those who wish to do an extracurricular activity other than Korean Language classes, which must also be 400 hours. Some examples of extracurricular activities are online advanced programming courses (e.g., Advanced R Programming, Advanced SAS Programming, TensorFlow: Advanced Techniques from Coursera), certificated courses or programs from other institutions (e.g., online Master of Computer and Information Technology from the University of Pennsylvania), and other language programs (e.g., The Spanish, Japanese, Chinese, Korean courses offered by EdX online). Please, be aware that any chosen activity must have a certificate proving your efforts and contribution.

To apply for any extracurricular activity, the student must:

  • Submit a detailed proposal to the MSDA program director.
  • Receive approval from the director before initiating the activity.
  • Submit the results, achievements, and certificates to the program director before the end of the last semester.


The equivalency and validity of the activities reported will be decided by the MSDA program director and SolBridge’s administration.

Students with a TOPIK 4 score or higher or native Korean are not required to take the Korean Language classes or deliver the extracurricular activity.

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