데이터사이언스 원론
Foundations of Data Science
Joonseok Lee, Jaejin Lee, Seunggeun Lee, Min-hwan Oh, Hyung-Sin Kim, Sang Kyun Cha, Sanghack Lee
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.
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%