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.)

Midterm exam: 30%
Final exam: 30%
Homework & Project: 35%
Attendance / Participation: 5%

(Plagiarism is not allowed in your Homework and Projects.)

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

W01 Big data concepts, Data models
W02 Numpy
W03 Pandas
W04 Exploratory data analysis
W05 Data preprocessing
W06 Statistics4BigData, Machine learning, Evaluation measures
W07 Information theory, Decision tree
W08 Midterm
W09 Rule learning, kNN, Naive Bayes
W10 Ensembles, Linear model, Neural networks
W11 Support vector machines (SVM)
W12 Clustering
W13 PageRank and link analysis
W14 Recommender systems
W15 Time-series
W16 Final exam

- This semester, face-to-face classes will be held in the classroom.
- 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 prior to class.

You must write up HWs and code from scratch independently. The following are considered to be honor code violations:
Looking at the writeup or code of another student.
Showing your writeup or code to another student.
Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.
Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.
Looking at solutions from previous years' homeworks - either official or written up by another student.
Anyone violating the honor code will get F no matter what.