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
Min-hwan Oh
Seunggeun Lee
Jaejin Lee
Hyung-Sin Kim
Sang Kyun Cha
Hyunwoo Park
Sanghack Lee
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%
