統計基礎

Concepts

Statistical Thinking in Python (Part 1)・DataCamp・3 Hours Course (Intermediate)

Build the foundation you need to think statistically and to speak the language of your data.

Statistical Thinking in Python (Part 2)・DataCamp・4 Hours Course (Intermediate)

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Introduction to Statistics in Python・DataCamp・4 Hours Course (Intermediate)

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

To Be Organized

Introduction to Linear Modeling・DataCamp・4 Hours Course

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Foundations of Probability in Python・DataCamp・5 Hours Course (Intermediate)

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Survival Analysis in Python・DataCamp・4 Hours Course

Use survival analysis to work with time-to-event data and predict survival time.

Case Studies in Statistical Thinking・DataCamp・4 Hours Course

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Analyzing Survey Data in Python・DataCamp・4 Hours Course

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Statistical Simulation in Python・DataCamp・4 Hours Course

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Survival Analysis in Python・DataCamp・4 Hours Course

Use survival analysis to work with time-to-event data and predict survival time.

Foundations of Inference in Python・DataCamp・4 Hours Course

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Generalized Linear Models in Python・DataCamp・4 Hours Course

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

Performing Experiments in Python・DataCamp・4 Hours Course

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

Monte Carlo Simulations in Python・DataCamp・4 Hours Course

Learn to design and run your own Monte Carlo simulations using Python!

Intermediate Regression with statsmodels in Python・DataCamp・4 Hours Course (Intermediate)


Learn to perform linear and logistic regression with multiple explanatory variables.

Anomaly Detection in Python・DataCamp・4 Hours Course

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Introduction to Linear Modeling in Python・DataCamp・4 Hours Course

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Customer Analytics and A/B Testing in Python・DataCamp・4 Hours Course

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Foundations of Probability in Python・DataCamp・5 Hours Course

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Bayesian Data Analysis in Python・DataCamp・4 Hours Course

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

A/B Testing in Python・DataCamp・4 Hours Course

Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

Time Series Analysis in Python・DataCamp・4 Hours Course

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Sampling in Python・DataCamp・4 Hours Course (Intermediate)

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Introduction to Regression with statsmodels in Python・DataCamp・4 Hours Course (Intermediate)

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Hypothesis Testing in Python・DataCamp・4 Hours Course

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

1 Statistics Fundamentals

Statistics Fundamentals・StatQuest・[Playlist]

2 生物統計學

杜裕康、李文宗、林瑞祥、林菀俞、蕭朱杏、盧子彬、洪弘 教授

國立臺灣大學・公共衛生學院

本課程將從資料的描述方式與方法開始，介紹實際資料處理的方式。然後介紹機率的概念、定義、各種機率分配、彼此間的關係，以及機率在統計的應用。接下來會介紹統計推論的方法，並以估計與假設檢定為主，同時會佐以各類實際資料來進行統計分析。課程的最後一部分會講授迴歸分析，以幫助學生了解變數之間的影響關係。

3 基本數理統計

洪弘 教授

國立臺灣大學・公共衛生學院

This course is designed for those students in the field of Biostatistics who are interested in the principles of statistics. The goal of this class is to provide students with the concepts, methods, and related theories to make statistical inferences. More specifically, the course will focus on 3 topics: point estimation, hypothesis testing, and asymptotic evaluations. Some basic probability concepts will also be reviewed at a fast pace.


課程影片 stay tuned!

Week 1: Lesson 1 Introduction [01:04:17]

Week 2: Lesson 2 Review of probability concepts [02:24:18]

Week 3: Lesson 3 Commodity Used Distributions [02:18:39]

Week 4: Lesson 4 Multiple Random Variables [02:06:32]

Week 5: Lesson 5 Properties of a ransom sample (1) [02:03:30]

Week 6: Lesson 6 Properties of ransom sample (2) [02:09:09]

Week 7: Lesson 7 Principles of data reduction (1) [02:21:52]

Week 8: 學期考試週，暫無提供影片

Week 9: Lesson 8 Principles of data reduction (2) [02:05:01]

Week 10: Lesson 9 [02:02:14]

Week 11: Lesson 10
Part 1 [51:12]

Part 2 [48:37]

Part 3 [43:37]

Week 12: Lesson 11 [02:27:48]

Week 13: Lesson 12 [02:24:18]

Week 14: Lesson 13 [02:24:12]

Week 15: Lesson 14 [02:27:51]

Week 16: 學期考試週，暫無提供影片

Week 17: Lesson 15 [02:37:54]

Week 18: Lesson 16 [02:29:55] (END)

4 迴歸分析

Linear Regression and Linear Models・StatQuest・[Playlist]

Logistic Regression・StatQuest・[Playlist]

5 智慧醫療統計基礎・精華版

洪弘 教授

國立臺灣大學・公共衛生學院

本課程涵蓋可用於醫學 AI 的統計分析基礎。主要主題包括線性迴歸、羅吉斯迴歸，ROC 曲線分析、卜瓦松迴歸、生存分析。本課程將從連續型資料開始，介紹迴歸分析的理論架構以及其應用方式，然後再推廣到不同資料結構的分析方法，包含二元資料、計數資料、以及存活資料。本課程的另一個重心放在 R 軟體的操作以及實際資料的分析。希望修課的學生可以根據資料的結構，選擇適當的分析方法，並了解其運作原理，完成正確的統計分析。


6 智慧醫療統計基礎・完整版

洪弘 教授

國立臺灣大學，公共衛生學院

本課程涵蓋可用於醫學 AI 的統計分析基礎。主要主題包括線性迴歸、羅吉斯迴歸，ROC 曲線分析、卜瓦松迴歸、生存分析。本課程將從連續型資料開始，介紹迴歸分析的理論架構以及其應用方式，然後再推廣到不同資料結構的分析方法，包含二元資料、計數資料、以及存活資料。本課程的另一個重心放在 R 軟體的操作以及實際資料的分析。希望修課的學生可以根據資料的結構，選擇適當的分析方法，並了解其運作原理，完成正確的統計分析。

講課影片 

實作影片 

課程地圖