https://github.com/mh-pedro/data-science-notes
Notes about Data Science
https://github.com/mh-pedro/data-science-notes
data-analysis data-science machine-learning pandas python scipy
Last synced: 2 months ago
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Notes about Data Science
- Host: GitHub
- URL: https://github.com/mh-pedro/data-science-notes
- Owner: mh-pedro
- License: mit
- Created: 2025-03-14T21:40:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-14T21:43:04.000Z (over 1 year ago)
- Last Synced: 2025-03-14T22:28:42.832Z (over 1 year ago)
- Topics: data-analysis, data-science, machine-learning, pandas, python, scipy
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📌 Data Science Study Notes
Welcome to the **Data Science Study Notes** repository!
This project is a comprehensive collection of study materials and notes focused on various **statistical methods and hypothesis testing techniques** essential for data science.
The notes are organized into chapters and sections, covering both **parametric and non-parametric tests**, hypothesis formulation, and error analysis.
---
## 📖 Table of Contents
### **Chapter 1: Hypothesis Testing**
#### **Hypothesis**
- What is a hypothesis?
- Formulating a hypothesis
- Null and alternative hypothesis
- Types of hypotheses
- Directional and non-directional hypotheses
#### **Hypothesis Test**
- Probability of error in hypothesis testing
- Level of significance
- Types of errors
- P-value
#### **Basics of the Z-test**
- Z-score and Z-statistic
#### **Z-test for Means**
- One-sample Z-test
- Two-sample Z-test
#### **Z-test for Proportions**
- One-proportion Z-test
- Two-proportion Z-test
---
### **Chapter 2: Parametric Tests**
#### **Assumptions of Parametric Tests**
- Testing for normally distributed data
- Visual inspection
- Kolmogorov-Smirnov test
- Anderson-Darling test
- Testing for equal variance
- Levene's test
- Fisher's F-test
#### **T-test**
- T-test for means
#### **Tests with More Than Two Groups and ANOVA**
- Bonferroni correction
- ANOVA
- Pearson's correlation coefficient
---
### **Chapter 3: Non-Parametric Tests**
#### **When Parametric Test Assumptions Are Violated**
- Permutations test
#### **The Rank-Sum Test**
- Test statistic procedure
- Normal approximation
- Rank-Sum example
#### **The Signed-Rank Test**
#### **The Kruskal-Wallis Test**
#### **The Chi-square Distribution**
#### **The Chi-square Goodness-of-Fit**
---
## 📌 **Usage**
These notes are intended to serve as a **reference** for students and professionals in data science who are looking to deepen their understanding of **statistical methods**.
Each section provides **clear explanations and examples** to help you grasp the concepts effectively.
---
## 📜 **License**
This project is licensed under the **MIT License** - see the LICENSE file for details.
📚 **Happy studying!**