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Causality and experiments
數據科學方法<br>Data Science
Introduction
[1.1] 課程敘述
[1.2] 軟體工具
Causality and experiments
[2.1] Data
[2.2] Disciplines
Homework #1
Randomness and probability
[3] Probability (I)
[4] Probability (II)
Random sampling
[5] Sampling
Homework #2: blackjack (simplified)
Statistical models
[6] Statistical models
Estimation
[7] Point estimation
Homework #3: logistic regression
[8] Interval estimation
Homework #4: bootstrap confidence interval
Statistical inferencing
[9] Statistical inferencing
[10] Statistical inferencing (II)
[11] Statistical inferencing (III)
Homework #5: PM 2.5 Concentrations
期中考
Introduction to machine learning
[12] Head-in ML
Homework #6: cross-validation
Regression problems
[13] Regression
Homework #7: Diabetes dataset
Classification
[14] Classification
Homework #8: multiclass logistic regression
Clustering
K-means clustering
Hierarchical & spectral clustering
Model-based clustering
Homework #9: k-means clustering
Explorative data analysis
Exploratory data analysis with Pandas
Which chart is right for my data?
Introduction to Data Visualization.ipynb
線上課程
線上課程2
Homework #10: does the logistic regression overfit?
期末報告
期末報告
期末報告
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[2.1] Data