{"id":26295534,"url":"https://github.com/mh-pedro/data-science-notes","last_synced_at":"2026-04-14T15:31:48.222Z","repository":{"id":282479903,"uuid":"948736332","full_name":"mh-pedro/Data-Science-Notes","owner":"mh-pedro","description":"Notes about Data Science","archived":false,"fork":false,"pushed_at":"2025-03-14T21:43:04.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-14T22:28:42.832Z","etag":null,"topics":["data-analysis","data-science","machine-learning","pandas","python","scipy"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mh-pedro.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-14T21:40:20.000Z","updated_at":"2025-03-14T21:48:21.000Z","dependencies_parsed_at":"2025-03-14T22:38:45.563Z","dependency_job_id":null,"html_url":"https://github.com/mh-pedro/Data-Science-Notes","commit_stats":null,"previous_names":["mh-pedro/data-science-notes"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mh-pedro%2FData-Science-Notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mh-pedro%2FData-Science-Notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mh-pedro%2FData-Science-Notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mh-pedro%2FData-Science-Notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mh-pedro","download_url":"https://codeload.github.com/mh-pedro/Data-Science-Notes/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243681077,"owners_count":20330155,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","machine-learning","pandas","python","scipy"],"created_at":"2025-03-15T04:14:20.775Z","updated_at":"2026-04-14T15:31:48.177Z","avatar_url":"https://github.com/mh-pedro.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📌 Data Science Study Notes\n\nWelcome to the **Data Science Study Notes** repository!  \nThis project is a comprehensive collection of study materials and notes focused on various **statistical methods and hypothesis testing techniques** essential for data science.  \nThe notes are organized into chapters and sections, covering both **parametric and non-parametric tests**, hypothesis formulation, and error analysis.\n\n---\n\n## 📖 Table of Contents\n\n### **Chapter 1: Hypothesis Testing**\n#### **Hypothesis**\n- What is a hypothesis?\n- Formulating a hypothesis\n- Null and alternative hypothesis\n- Types of hypotheses\n- Directional and non-directional hypotheses\n\n#### **Hypothesis Test**\n- Probability of error in hypothesis testing\n- Level of significance\n- Types of errors\n- P-value\n\n#### **Basics of the Z-test**\n- Z-score and Z-statistic\n\n#### **Z-test for Means**\n- One-sample Z-test\n- Two-sample Z-test\n\n#### **Z-test for Proportions**\n- One-proportion Z-test\n- Two-proportion Z-test\n\n---\n\n### **Chapter 2: Parametric Tests**\n#### **Assumptions of Parametric Tests**\n- Testing for normally distributed data\n  - Visual inspection\n  - Kolmogorov-Smirnov test\n  - Anderson-Darling test\n- Testing for equal variance\n  - Levene's test\n  - Fisher's F-test\n\n#### **T-test**\n- T-test for means\n\n#### **Tests with More Than Two Groups and ANOVA**\n- Bonferroni correction\n- ANOVA\n- Pearson's correlation coefficient\n\n---\n\n### **Chapter 3: Non-Parametric Tests**\n#### **When Parametric Test Assumptions Are Violated**\n- Permutations test\n\n#### **The Rank-Sum Test**\n- Test statistic procedure\n- Normal approximation\n- Rank-Sum example\n\n#### **The Signed-Rank Test**\n#### **The Kruskal-Wallis Test**\n#### **The Chi-square Distribution**\n#### **The Chi-square Goodness-of-Fit**\n\n---\n\n## 📌 **Usage**\nThese 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**.  \nEach section provides **clear explanations and examples** to help you grasp the concepts effectively.\n\n\n---\n\n## 📜 **License**\nThis project is licensed under the **MIT License** - see the LICENSE file for details.\n\n📚 **Happy studying!**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmh-pedro%2Fdata-science-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmh-pedro%2Fdata-science-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmh-pedro%2Fdata-science-notes/lists"}