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
Joonseok Lee
Jaejin Lee
Seunggeun Lee
Min-hwan Oh
Hyung-Sin Kim
Sang Kyun Cha
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%