The goal of this course is to study basic concepts and techniques for data analysis and exercise with open source tools such as Python and data analysis libraries.

- Prerequisites: CSED233 Data structure (or some knowledge on basic data structure and algorithms) and some level of programming skills.
- 수강 정원이 찼을 경우, POSTECH 컴공과 학부생 및 컴공지망 무은재학부생만 추가 등록 가능. (If the course is full, only POSTECH CSE undergraduates are eligible for additional enrollment.)

There is no required textbook for this class, and you should be able to learn everything from lecture notes and public websites.

W1 Introduction
W2 Numpy
W3 Pandas I
W4 Pandas II
W5-1 k-NN Search: From Linear Scan to KD-tree
W5-2 DiskANN (with proofs)
W6 Decision Tree
W7 Overfitting and its theoretical explanation
W8 Midterm
W9 kNN
W9 Evaluation
W10 Machine learning with scikit-learn
W10 Univariate Analysis Visualization
W11-1 Bivariate Data Analysis and Visualization
W11-2 Regular Expression
W12-1 Titanic Survival Prediction (Data Science Pipeline)
W12-2 Titanic Survival Prediction: From EDA to a Top-3% Kaggle Solution
W13-1 Perceptron
W13-2 Bagging & Random Forest
W14-1 Gradient Decent, Boosting and AdaBoost
W14-2 Kmeans
W15 SQL
W16 Final Exam

Many of the lectures will use a flipped learning approach.
There is no required textbook for this class, so it is important to take notes during class.
Lecture notes will be posted to the PLMS before class.

You are expected to complete all homework and coding assignments independently. You may use Large Language Models (LLMs) to assist with your projects, provided you explicitly cite which parts were generated by AI and explain how the tool improved your code.