2nd Major in Data Science and Analytics
What will this second major do for you?
DSA enables you to:
- Turn data into insights
Companies are collecting ever-increasing amounts of data. The winners are those who can extract value from this data. Consequently, data scientists and analysts are in high demand. - Learn the data skills you need in relevant programming languages
Get experience in Python, R, and SQL. Learn how to query databases, code pipelines for data wrangling, create interactive visualizations to explore relationships and trends using JavaScript libraries, and more. - Explore statistical learning
Training and testing statistical models that describe and generalize datasets is a powerful thing. Learn how to use statistical models, and machine learning algorithms, to generate predictions and gain additional insights. - Sharpen your competitive edge
Acquire the data science and analytics skills you need for the future of work. Including big data and big compute technologies such as MySQL, Hadoop, and Spark. - Apply your skills in real-world projects
Practice communicating the results of your analysis to project stakeholders. Share your though process using GitHub and GitHub Pages. Build a portfolio of projects to showcase at internship and job interviews. - Join a vibrant community
All DSA Second Major students are supported in their data science journey by the DSA Society at SMU.
Structure & Curriculum
To fulfil the requirements of the DSA major, students must complete the following:
Probability Theory and Applications (STAT201)
Statistical Inference for Data Science (DSA201)
Statistical Learning with R (DSA211) *
Data Analytics with R (DSA212)
Computational Thinking and Programming (COR-IS1704) **
* Statistical Learning with R is mutually exclusive with Statistical Programming, which is a compulsory Accounting Core course for BAcc students. BAcc students can therefore take Statistical Programming instead of Statistical Learning with R to fulfil the requirements.
** Computational Thinking and Programming is also a Core Curriculum course under the Capabilities (Modes of Thinking) basket. Students may not double count this course towards both the Core Curriculum and the DSA Second Major. Therefore, students must complete (a) an alternative course to fulfil the Capabilities (Modes of Thinking) basket requirement of the Core Curriculum or (b) an extra DSA Second Major Elective. As Computational Thinking and Programming is a compulsory Core Curriculum course under the Capabilities (Modes of Thinking) basket for BSc (CL), BSc (IS) and BSc (SE) students, these students are required to complete 5 CUs of DSA Second Major Electives instead of 4 CUs.
Choose any four courses in the Data Analysis (DA) List and Computing Technology (CT) List, with at least one course in each list.
DA List
Time Series Data Analysis (DSA301) or Economic Forecasting (ECON233)
Spatial Data Analysis – SMU-X (DSA303)
Panel Data Analysis (DSA305)
Big Data Analytics (DSA306) or Big Data with Spark (DSA307)
Applied Healthcare Analytics (ECON245)
Marketing Analytics (MKTG228) or Service and Operations Analytics (OPIM326) or Forecasting and Forensic Analytics (ACCT420)
Machine Learning with Applications in Economics (DSA311)
Data Science with Python (DSA312)
CT List
Modeling and Data Analytics (COR1305) or Data Management (IS112) or Business Data Management (IS105)
Visual Analytics for Business Intelligence (IS428) or Geospatial Analytics and Applications (IS415)
Introduction to Artificial Intelligence (CS420)
Principles of Machine Learning (CS421) or Machine Learning and Applications (IS460)
Data Mining and Business Analytics (IS424) or Data Warehousing and Business Analytics (IS417)
Text Mining and Language Processing (IS450)
Reinforcement Learning in Portfolio Optimisation (QF210)