Here are Jupyter notebooks of the course Probability and Statistics.
Korea Advanced Institute of Science and Technology (KAIST).
Course name: CC511 Probability and Statistics
Advisor: Professor. Sung-Ho Kim, Department of Mathematical Science.
Textbook: Probability and Statistics for Engineers and Scientists, 4th ed. by A. J. Hayter
Chapter 1. Probability Theory
Chapter 2. Random Variables
Chapter 3. Discrete Probability Distributions
Chapter 4. Continuous Probability Distributions
Chapter 5. Normal Distribution
Chapter 6. Descriptive Statistics
Chapter 7. Statistical Estimation and Sampling Distributions
Python version: 3.7 or later
Python package: scipy, numpy, pandas, matplotlib, stemgraphic
There are two types of notebooks., both of them cover all the content.
One is mainly used for learning this course, with comprehensive definitions and theorems and beautiful codes help you understand the calculating process.
Another one is mainly used to review this course, or works as an answer sheet, with concise summary of knowledge points and practical code to help you get the result.
For each chapter, each section, we summarized the important definitions and theorems. These will be shown on the markdown part in each section.
Then, we use Python code to implement the calculation part. With the help of code, you will easily understand the calculating process, which we believe is really important to master and practice the definitions and theorems we learned.
In each code block, we showed required parameters at the beginning, you can modify them freely with your need, and then get the result you want.
Sum up, there are three parts for each section:
- Definitions and theorems
- Code implement by Python
- Result output
If you have complete the learning of this course, you may want a more simple notebook to help you review it efficiently. These notebooks are designed for this.
Similarly, we showed definitions at the beginning of each section, but now these definitions and theorems have been quite concise. Just have a glance to refresh you mind. You can do it.
Then, there are code blocks to help you do some calculations. In each cold block, there are three parts: input, calculate and output.
You can modify values in the input freely, and the code will output the result.
If you forget the meaning of parameters, it dose not matter, we also note he parameters' definitions there.
Since these notebooks are quite simple and clear, you can easily use them to be your answer sheet in exams. That must be amazing helpful.
Step 1. Clone the repository to your computer, open the .ipynb files with Jupyter Notebook or Jupyter Lab. Or, you can use Google CoLab to load them.
Step 2. Run the first one or two code blocks to set the packages environment.
If there is something wrong with environment setting, please check you Python and packages' version.
Step 3. Do whatever you want, you can freely modify the parameters in code blocks for your calculation.
If you find any bugs, please feel free to create issues, we will deal with them as soon as possible.
