Data Science Track at KAIST

Author(s): Duman

Last updated of: 5 June 2020

Introduction

You might know that there’s no separate department for Data Science and no single department at KAIST is able to cover this topic effectively. This is why this course guideline was created.

I intentionally tried to make this guideline independent of the department you’re in, but still, it would be better if you majored in either Industrial Engineering or Computer Science, as many courses listed here are mostly from these departments.

I would also advise you to pursue Individually Designed Major instead of declaring a minor/double major. Why? Because this way you’re not going to take unnecessary courses from both CS & IE departments, and what’s even better — any course that you took in a department other than your major is gonna be counted towards IDM. Don’t worry about IDM sounding “not serious” — recruiters won’t be looking at your IDM, they will be looking at your skills and knowledge.

This list is huge and probably you won’t be able to take all of these courses. This is why I decided to break it down into the main topics in Data Science field so that you decide which courses to take on your own.

Basics

• CS101 - Intro to Programming
• MAS101, MAS102 - Calculus I & II
• MAS109 - Intro to Linear Algebra
• CoE202 - Big Data and AI

  Note: you should take linear algebra & be familiar with basic statistics before taking this course.

• IE241 - Engineering Statistics I OR
• MAS250 - Probability & Statistics OR
• EE210 - Probability and Introductory Random Processes

  Note: I haven’t taken the last two courses, but I have heard that the probability course at EE is a good one.

Major Courses, divided by topics

Foundations

• IE242 - Engineering Statistics II
• CS206 - Data Structure
• CS300 - Introduction to Algorithms
• TS251 - Data Science Overview
• IE343 - Statistical Machine Learning AND/OR
• CS376 Machine Learning

  Note: IE343 is less rigorous than CS376, but it’s better to take if you know nothing about Machine Learning & want to get familiarized with concepts and math behind ML. IE343 also covers fewer topics than CS376, but each topic is covered at greater depth. The choice is up to you. You could even take both. It won't hurt to solidify your knowledge.

• IE481 - Special Topics in Industrial Engineering: Data Visualization
• EE488 - Database and Big data system
• KSE521 - Business Intelligence

  Note: This course covers the basics of Data Analytics, including database, SQL, predictive modeling, clustering, model evaluation, and BI reporting.

Mathematical Modeling & Operations Research

• IE232 - Operations Research: Stochastic Models
• IE342 - Regression Analysis and Experimental Designs

  Note: While it doesn't directly relate to Data Science, it teaches the essentials of experiment design and avoiding bias — which is very useful for your career in Data Science.

• IE432 - Decision Analysis & Risk Management

  Note: a decent course that introduces the basics of decision analysis, modeling, and convex optimization. While you’re not gonna code there, this course teaches a lot of relevant theory. Personally, I've been asked some questions on Data Science interviews that were taught on this course.

• IE437 - Data-Driven Decision Making and Control
• IE481 - Special Topics: Game Theory and Multi-Agent Reinforcement Learning

  Notes: IE437 is a prerequisite. Understanding Neural Networks and Deep learning would be beneficial.

Computing Courses

• EE412 - Foundation of Big Data Analytics
• KSE525 - Data Mining - Knowledge Service Engineering Department
• KSE526 - Analytical Methodologies for Big Data

Machine Learning-based courses

• BIS335 - Biomedical Statistics & Statistical Learning

  Note: this course also teaches you R, a commonly used language in Data Science. You might think this course is for Bio&Brain students only, but it has a solid coverage, and a huge load as well.

• CS372 - Natural Language Processing
• CS470 - Introduction to Artificial Intelligence

  Note: it’s recommended to take this course after taking the CS376/IE343 Machine Learning course.

• CS474 - Text Mining
• CS492 - Special Topics in Computer Science - Any Machine Learning/Data Science related course topic: i.e. Systems for ML, Deep Learning for Real-World Problems, etc.

Professor recommendations (will be updated)

Until now all the suggestions were content-based. However, we know that the instructor matters as well. A course may be very useful but the instructor is not. Here are some instructor recommendations:

• Steven Whang Has a class about big data/ databases.
• Meeyoung Cha Data Science Lab at KAIST This lab is under IBS, read more about IBS below: https://www.ibs.re.kr/datascience/
• Alice Oh

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