Foundations of Data Science (데이터사이언스 원론)

Goals

This course aims to cultivate theoretical knowledge and hands-on experience necessary for students from various backgrounds to deal with and analyze big data. Through this course, students learn the basic knowledge of data-oriented computing, quantitative thinking and reasoning, and exploratory data analysis. Based on this, students learn key principles and techniques for data-driven problem solving, such as data analysis methods, big data management systems, problem formulation, data collection and organization, visualization, reasoning, predictive modeling, and decision making.

Instructors

이준석

Joonseok Lee

이재진

Jaejin Lee

이승근

Seunggeun Lee

오민환

Min-hwan Oh

김형신

Hyung-Sin Kim

차상균

Sang Kyun Cha

이상학2

Sanghack Lee

Content

Statistics & Basic Programming

Date
Topic
Instructor
Due

Introduction

· Course Logistics
· What is Data Science?
· Lab: Linux, Python Programming
Joonseok Lee
Sang Kyun Cha
Jaejin Lee

Statistics for Data Science, Basic Programming

· Data sampling, Probability
· Lab: Python Programming
Seunggeun Lee
Jaejin Lee

Statistics for Data Science, Basic Programming

· Random variables and Expectation
· Lab: Python Programming
Seunggeun Lee
Jaejin Lee

Statistics for Data Science, Basic Programming

· Variance and Asymptotics
· Lab: Python Programming
Seunggeun Lee
Jaejin Lee

Statistics for Data Science, Basic Programming

· Estimation, Bias and Mean squared error
· Lab: Python Programming
Seunggeun Lee
Jaejin Lee

Computing Methodology

Date
Topic
Instructor
Due

Algorithmic Thinking, Computational Complexity

· Time and Space Complexity
· Lab: Peak Finding
Min-hwan Oh

Searching

· Searching (Binary search)
· Lab: Searching Problems
Min-hwan Oh

Sorting

· Sorting (Insertion sort, Selection sort, Merge sort, Quick sort)
· Lab: Python Programming
Min-hwan Oh

Data Structures

· Array, Linked list
· Lab: Array, Linked list Problems
Hyung-Sin Kim

Data Structures

· Stack, Queue
· Lab: Stack, Queue Problems
Hyung-Sin Kim

Data Structures

· Trees (Binary tree, Binary search tree)
· Lab: Trees Problems
Hyung-Sin Kim

Data Structures

· Graph, Hash table
· Lab: Graph, Hash table Problems
Hyung-Sin Kim

Database

Date
Topic
Instructor
Due

Introduction to Database

· Introduction to Database
· Lab: SQL
Sang Kyun Cha

Graph Database

· Graph Database
· Lab: Neo4j
Sang Kyun Cha

Mid-term Exam

Last day to unregister

Machine Learning

Date
Topic
Instructor
Due

Linear Regression

· Introduction to ML, Linear Regression
· Lab: Linear Regression
Sanghack Lee

Linear Regression

· Logistic Regression
· Lab: Logistic Regression
Sanghack Lee

Decision Trees

· Decision Trees
· Lab: Decision Trees, Random forests
Sanghack Lee

Overfitting, Regularization

· Overfitting, Regularizaion
· Lab: Regularization Methods
Sanghack Lee

Nearest Neighbors

· Nearest Neighbor Classifiers
· Lab: Handwritten Digits Classification using Nearest Neighbors
Joonseok Lee

Optimization

· Gradient descent, SGD, advanced optimization, Cross validation
· Lab: Gradient descent
Joonseok Lee

Neural Networks

· Neural networks, Backpropagation
· Lab: Neural networks with TensorFlow
Joonseok Lee

Introduction to Deep Learning

· Deep learning, Convolutional neural networks (CNN)
· Lab: CNN-based Image Classification
Joonseok Lee

Unsupervised Learning

· Clustering, Dimension reduction
· Lab: K-means Clustering for Image Compression
Joonseok Lee

Advanced Topics

Date
Topic
Instructor
Due

Decision Making

· Reinforcement learning
· Lab: Reinforcement learning
Min-hwan Oh

Ambient AI

· Ambient AI
· Lab: Ambient AI with Edge Devices
Hyung-Sin Kim

Causal Inference

· Causal Inference
· Lab: Causal Inference
Sanghack Lee

Final Exam

Grading Policy

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