Principles and Application of Data Science
(데이터사이언스 원리와 응용)

Goals

Combining computing, machine learning algorithms, and inferential thinking based on probability/statistics, data science provides a new paradigm for deriving insights and new knowledge from data and solving real-world problems. The purpose of this course is to introduce core principles and techniques of data science such as basic theoretical knowledge and analytical skills necessary for students of various majors to solve data-driven problems. In addition, students will learn data-centric thinking and decision-making based on data.

Instructors

오민환

Min-hwan Oh

이승근

Seunggeun Lee

이재진

Jaejin Lee

김형신

Hyung-Sin Kim

차상균

Sang Kyun Cha

hyunwoo park

Hyunwoo Park

이상학2

Sanghack Lee

이준석

Joonseok Lee

Content

Statistics & Computing

Date
Topic
Instructor

Introduction

· Course Logistics
· What is Data Science?
Min-hwan Oh
Sang Kyung Cha

Statistics for Data Science

· Probability, Random variables, Expectation & Variance
Seunggeun Lee

Statistics for Data Science

· Bias-variance tradeoff, MSE
Seunggeun Lee

Computing

· Big O (time & space complexity)
Jaejin Lee

Computing

· Searching
Jaejin Lee

Computing

· Sorting
Jaejin Lee

Computing

· Data structures: Array, Linked list, Queue, Stack
Hyung-Sin Kim

Computing

· Data structures: Binary Search Tree
Hyung-Sin Kim

Computing

· Data structures: Trees
Hyung-Sin Kim

Computing

· Data structures: Graph
Hyung-Sin Kim

Database & Visualization

Date
Topic
Instructor

Database

· Intro to database
Sang Kyun Cha

Database

· SQL
Sang Kyun Cha

Database

· Graph database
Sang Kyun Cha

Midterm Exam

Visualization

· Visualization I
Hyunwoo Park

Visualization

· Visualization II
Hyunwoo Park

Visualization

· Visualization III
Hyunwoo Park

Machine Learning

Date
Topic
Instructor
· Introduction to ML, Linear regression
Sanghack Lee
· Logistic Regression
Sanghack Lee
· Decision Trees, Random forests
Sanghack Lee
· Overfitting, Regularizaion
Sanghack Lee
· Nearest Neighbor Classifiers
Joonseok Lee
· Gradient descent, Cross validation
Joonseok Lee
· Neural networks, Backpropagation
Joonseok Lee
· Deep learning, Convolutional neural networks (CNN)
Joonseok Lee
· Unsupervised learning: Clustering
Min-hwan Oh
· Dimension reduction, PCA
Min-hwan Oh
· Online decision making, Multi-armed bandits
Min-hwan Oh
· Reinforcement Learning
Min-hwan Oh

Final Exam

Grading Policy

  • : Assignment 35%, Mid-term 30%, Final exam 30%, Attendance 5%