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Course summary

  • Type MOOC course
  • Period Always open
  • Learning Time 10hr
  • Course approval method Automatic approval
  • Certificate Issue Online
http://kooc.kaist.ac.kr/data-science-programming1
둘러보기
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Instructor Introduction

  • KAIST 산업및시스템공학과 문일철 교수

    KAIST 산업및시스템공학과 교수
    KAIST 김재철AI대학원 겸임교수
    KAIST 항공우주공학과 겸임교수
    KAIST 안보융합연구원 겸임교수
    한국인공지능학회 교육이사

Lecture plan

강의
  1. CHAPTER 1 Data Handling and Descriptive Statistics
    1. 1-1. Value chain of datascience programming
    1. 1-2. Dataset
    1. 1-3. Dataset Read with Pandas
    1. 1-4. Histogram and Bin size selection
    1. 1-5. Descriptive Statistics
    1. 1-6. Unbiased Estimator
    1. 1-7. Matplotlib
    1. 1-8. Pivot Table
    1. Lecture Note Chapter 1
    1. Quiz 1
  2. CHAPTER 2 Distribution and Parameter Inference
    1. 2-1. detour - Bin Size Selection and Model Complexity
    1. 2-2. Distribution
    1. 2-3. Normal Distribution
    1. 2-4. Gamma Distribution and Moment Statistics
    1. 2-5. Method of Moments
    1. 2-6. Method of Moments - Gamma Dist
    1. 2-7. Result from Method of Memoments
    1. 2-8. MLE of Gaussian Distribution
    1. 2-9. MLE of Gamma Distribution
    1. 2-10. Parameter Estimation by SciPy
    1. Lecture Note Chapter 2
    1. Quiz 2
  3. CHAPTER 3 Confidence Interval and Bootstrapping
    1. 3-1. Sample and Population
    1. 3-2. Point Estimate on Multiple Samples
    1. 3-3. Central Limit Theorem
    1. 3-4. Properties of Expectation
    1. 3-5. Distribution of Sample Statistics
    1. 3-6. Confidence Interval of Normal Distribution
    1. 3-7. Bootstrapping
    1. 3-8. Implementation of Bootstrapping
    1. 3-9. Confidence Interval By SciPy
    1. Lecture Note Chapter 3
    1. Quiz 3
  4. CHAPTER 4 Multivariate Distribution and Covariance
    1. 4-1. Multivariate Gaussian Distribution
    1. 4-2. Samples of Multivariate Gaussian Distribution
    1. 4-3. Covariance Matrix
    1. 4-4. Pearson Correlation
    1. 4-5. Scatter Plot Matrix
    1. 4-6. Kernel Density Estimation
    1. 4-7. Correlation Table
    1. Lecture Note Chapter 4
    1. Quiz 4
  5. CHAPTER 5 Linear Regression
    1. 5-1. Deterministic Relation
    1. 5-2. Estimation of Dependent Variable
    1. 5-3. Estimating the parameter, beta
    1. 5-4. Parameter Estimation by Least Square
    1. 5-5. Implementation of Simple Linear Regression
    1. 5-6. Evaluation of Linear Regression
    1. 5-7. R-Square
    1. Lecture Note Chapter 5
    1. Quiz 5
  6. ★강의 수강 후 의견을 부탁드리겠습니다.★
    1. 교수님 강의에 대한 별점을 매겨주세요. 여러분의 의견이 많은 도움이 됩니다:D

Additional Info

본 강좌는 Python3 를 기반으로 진행되는 강좌 입니다.
기초적인 내용부터 시작하기 때문에, 누구나 수강할 수 있는 강좌입니다.
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* 강좌 수료 기준 충족 시 수료증을 제공합니다:)