Data Science (2018 SPRING)

“Student-teacher relationships are based on trust. Acts, which violate this trust, undermine the educational process. Your classmates and the instructor will not tolerate violations of academic integrity”

• 임현교: https://github.com/glenn89/data_science_2018
• 김주봉: https://github.com/jupithunder/data_science_2018
• 권도형: https://github.com/tfedohk/data_science_2018
• 신성근: https://github.com/sgshin83/data_science_2018
• 이찬일: https://github.com/ChIlLee2018/data_science_2018
• 이종서: https://github.com/JongseoLee/data_science_2018
• 이의혁: https://github.com/LeeEuiHyeok/data_science_2018
• 최성준: https://github.com/fumika27/data_science_2018
• 박준홍: https://github.com/astrada1/data_science_2018
• 정상구: https://github.com/SangGooJeong/data_science_2018
• 최요한: https://github.com/CrlAYh/data_science_2018
• 황제민: https://github.com/HwangJeMin/data_science_2018
• 정진서: https://github.com/JS-Jeong/data_science_2018
• 김열호: https://github.com/KimYeolHo/data_science_2018
• 정용진: https://github.com/jungyj0211/data_science_2018
• 김호연: https://github.com/guymode/data_science_2018

1. Course Schedule & Lecture Notes

• March 07 – Course Introduction and Data Science 101
  • Data Science 101
    • Lecture Note
  • Supervised vs. Unsupervised Learning, Predictive Model, Data Handling and EDA (Exploratory data analysis)
    • Lecture Note
  • Laboratory
    • Iris Data Set
  • Book Laboratory
    • 1. Introduction

• March 14 – Supervised Learning – I
  • K-Nearest Neighbors (kNN)
    • Lecture Note 1
    • Lecture Note 2
  • Laboratory
    • kNN Practice
  • Book Laboratory
    • 2. Supervised Learning

• March 21 – Supervised Learning – II

  • Decision Tree and Random Forest
    • Lecture Note
  • Laboratory
    • Entropy and IG
    • Decision Tree
    • Mushroom Data Set
    • Mushroom Data Set – DT and RF
  • Book Laboratory
    • 2. Supervised Learning
  • Assignment – Due Date: 3월 27일 23시 59분
    • 제출 방법: jupyter notebook 에서 작업 이후 산출된 ipynb 파일을 github 등에 올리고 해당 URL을 메일로 보냄

• March 28 – Supervised Learning – III

  • Linear Regression
    • Lecture Note (jupyter notebook)
  • Gradient Descendent
    • Lecture Note (jupyter notebook)
  • Book Laboratory
    • 2. Supervised Learning
      

• April 04 – Supervised Learning – IV

  • Logistic Regression

    • Lecture Note (jupyter notebook)

  • Book Laboratory

    • 3. Unsupervised Learning

  • Assignment – Due Date: 10월 30일 23시 59분

    • 제출 방법: jupyter notebook 에서 작업 이후 산출된 ipynb 파일을 github 등에 올리고 해당 URL을 메일로 보냄

• April 11 – Unsupervised Learning and Preprocessing

  • K-Means

    • Lecture Note

  • Laboratory

    • k-Means Practice

  • Book Laboratory

    • 3. Unsupervised Learning

• April 18- Unsupervised Learning and Preprocessing

  • Principal Component Analysis

    • Eigenvector and Diagonalization
    • PCA

  • Laboratory

    • Eigenvector
    • Normalization
    • PCA – From Scratch
    • Health Data – PCA
    • MNIST – PCA

  • Book Laboratory

    • 3. Unsupervised Learning

• April 25 – [휴강]

• May 02 – Representing Data and Engineering Features

  • Book Laboratory

    • 4. Representing Data and Engineering Features

• May 09 – Model Evaluation and Improvement

  • Book Laboratory

    • 5. Model Evaluation and Improvement

• May 16 – Algorithm Chains and Pipelines

  • Book Laboratory

    • 6. Algorithm Chains and Pipelines

• May 23 – Working with Text Data

  • Book Laboratory

    • 7. Working with Text Data

• May 30 – Wrapping Up

  • Book Laboratory

    • 8. Wrapping Up

• June 07 – [기말고사]

2. Course Information

• Anaconda (Python) + Jupyter 코딩 환경 구축

  • 본 수업에서 활용할 프로그래밍 환경

    • 1) anaconda-3.6 설치
      • jupyter notebook 실행하여 코딩
    • 2) SourceTree 설치하여 Git 활용

  • Term-Project 제출 방법

    • 1) https://github.com/bluebibi/data_science_2018 –> Fork하여 본인의 Giuhub로 Repository 가져오기
    • 2) Git (or SourceTree)를 사용하여 로컬 컴퓨터에 Clone 하기
    • 3) 각 소챕터별로 jupyter notebook 활용하여 코드 작성
      • jupyter (구 iPython) 사용법
      • 코드 작성시 부가적인 설명을 적을 시에 Markdown 문법으로 입력해야 함
      • Markdown 사용법은 각자 익힐 것
        • Markdown 사용법
        • Markdown Tutorial
        • Markdown Online Editor
    • 4) 각 소챕터별로 생성한 ipynb 파일을 Commit & Push (Github 내 적절한 폴더로 업로드) 후 URL 얻기
    • 5) 다음 사이트에 해당 URL을 입력
      • http://nbviewer.jupyter.org
      • 위 사이트에서 보여지는 내용를 확인하고 해당 nbviewer URL을 활용하여 각 챕터별로 링크 달기
      • nbviewer URL 예: http://nbviewer.jupyter.org/urls/dl.dropbox.com/s/t9nmklgjkp7w4ok/kmeans.ipynb

  • 파이썬 학습

    • 파이썬 학습 사이트
    • Pandas – 10 minutes tutorial
    • Regular Expression 학습 사이트
    • Databricks Community Edition

• Professor: Youn-Hee Han (Rm. B303, Tel: 560-1486, yhhan@koreatech.ac.kr)

• Classes: Wednesday (10:00 – 12:50)

• Lecture Room: 4공학관 4층 세미나실

• Lecture Materials

  • 파이썬 라이브러리를 활용한 머신러닝

• Github Repository

  • https://github.com/link-kut/introduction_to_ml_with_python

• Prerequisites

  • Programming background and experience with Python required.

• References

  • 포스터 프로보스트, 톰 포셋 (강권학 옮김), 비즈니스를 위한 데이터 과학 빅데이터를 바라보는 데이터 마이닝과 분석적 사고, 한빛미디어, 2014년 7월
  • 사토 히로유키 외 (정인식 옮김), 데이터 과학자, 무엇을 배울 것인가 : 빅 데이터 시대의 데이터 과학자 양성 독본, 제이펍, 2014년 6월
  • 니시우치 히로무 (신현호 옮김), 빅데이터를 지배하는 통계의 힘: 통계학이 최강의 학문이다, 비전코리아, 2013년 7월
  • 니시우치 히로무 (신현호 옮김), 빅데이터를 지배하는 통계의 힘: 실무활용 편, 비전코리아, 2015년 8월
  • Lei Tang and Huan Liu, Community Detection and Mining in Social Media, Synthesis Lectures on Data Mining and Knowledge Discovery, Vol. 2, No. 1, Pages 1-137, 2010.
  • Maksim Tsvetovat and Alexander Kouznetsov, Social Network Analysis for Startups: Finding connections on the social web, O’Reilly Media, October 7, 2011.
  • Matthew A. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O’Reilly Media, February 8, 2011.
  • Choosing the right estimator for machine learning
  • SINAN OZDEMIR’s Python for Data Science
  • Revisiting Numerai

3. Logistics

• Attendance – One class absence will result in the deduction of two points out of 100 points. Three absences will not result in ten points deduction, but “failure” (i.e., grade ‘F’) in this course.
• Exam – There will be the final exam for the evaluation of the knowledge learned from the class.
• Term Project – Much evaluation mark will be counted.

4. Evaluation

• Attendance (10%)
• Term Project (40%)
• Final Examination (60%)