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강좌 개요

  • 타입 MOOC 강좌
  • 기간 상시 수강
  • 학습시간 자유롭게 학습
  • 수강 승인 방식 자동 승인
  • 수료증 미발급
URL복사 밴드 페이스북 트위터
http://unist.edwith.org/data-science-2020
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좋아요 217 수강생 1297

Data Science

UNIST 임치현 교수 외

교수자 소개

강의계획

Lecture list
  1. week 1
    1. 1-1. Introduction to Data Science
    1. 1-2. Introduction to the Course.mp4
    1. 1-3. Data Science Applications I
    1. 1-4. Data Science Applications II
  2. week 2
    1. 2-1. Data Mining : Overview
    1. 2-2. Supervised Learning : Regression
    1. 2-3. Supervised Learning : Classification
    1. 2-4. Neural Network
  3. week 3
    1. 3-1. Unsupervised Learning : Clustering
    1. 3-2. Unsupervised Learning : Association Rule Mining
    1. 3-3. Word Embedding - Word2vec
    1. 3-4. Convolutional Neural Network, Recurrent Neural Network
  4. week 4
    1. 4-1. Optimization and Operations Research : Overview
    1. 4-2. Optimization Modeling Using Linear Programming
    1. 4-3. Graphical Solution of Two-Dimensional Linear Programs
    1. 4-4. Convexity
  5. week 5
    1. 5-1. Process Management Overview
  6. week 6
    1. 6-1. Statistics for Data Science I
    1. 6-2. Statistics for Data Science II
    1. 6-3. Linear Algebra for Data Science I
    1. 6-4. Linear Algebra for Data Science II
  7. week 7
    1. 7-1. Voice of Industry Experts I
    1. 7-2. Voice of Industry Experts II
    1. 7-3. Voice of Industry Experts III
    1. 7-4. Voice of Industry Experts IV
    1. 7-5. Voice of Industry Experts V
    1. 7-6. Voice of Industry Experts VI
  8. week 8
    1. 8-1. Term Project Announcement
  9. week 9
    1. 9-1. Types of Data Overview (structuredunstructured)
    1. 9-2. Unstructured Data Analysis
    1. 9-3. Time Series Data
    1. 9-4. Time Series Data Analysis
    1. 9-5. Data Quality (e.g., Imputation)
  10. week 10
    1. 10-1. Manufacturing Process Management
  11. week 11
    1. 11-1. Business Process Management I
    1. 11-2. Business Process Management II
    1. 11-3. Personal Process Management
  12. week 12
    1. 12-1. Health Informatics
    1. 12-2. Maritime Logistics
    1. 12-3. Document Mining
  13. week 13
    1. 13-1. Data Science for Transportation and Logistics Industry - (1) Optimization Modeling
    1. 13-2. Data Science for Transportation and Logistics Industry - (2) Case Study
    1. 13-3. Financial Data
    1. 13-4. Financial Data Analysis
  14. week 14
    1. 14-1. Course summary