statistical-data-analysis

BSc Statistical Data Analysis - Complete 5-lesson course covering regression, hypothesis testing, PCA/EFA, clustering, and time series analysis with Beamer presentations and Python visualizations

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Information

Property	Value
Language	HTML
Stars	0
Forks	0
Watchers	0
Open Issues	0
License	MIT License
Created	2025-12-16
Last Updated	2026-03-16
Last Push	2026-03-16
Contributors	1
Default Branch	main
Visibility	private

Notebooks

This repository contains 1 notebook(s):

Notebook	Language	Type

| semester_thesis | PYTHON | jupyter |

Datasets

This repository includes 25 dataset(s):

Dataset	Format	Size

| charts.json | .json | 48.01 KB |

| questions.json | .json | 14.71 KB |

| datasets | | 0.0 KB |

| README.md | .md | 3.59 KB |

| agricultural_experiment.csv | .csv | 3.81 KB |

| clinical_trial.csv | .csv | 2.18 KB |

| drug_dosage_study.csv | .csv | 3.42 KB |

| education_intervention.csv | .csv | 0.6 KB |

| employee_satisfaction.csv | .csv | 3.41 KB |

| environmental_study.csv | .csv | 6.36 KB |

| manufacturing_quality.csv | .csv | 0.64 KB |

| marketing_campaigns.csv | .csv | 4.03 KB |

| medical_treatment.csv | .csv | 2.32 KB |

| reaction_time_study.csv | .csv | 2.99 KB |

| website_testing.csv | .csv | 71.91 KB |

| questions.json | .json | 14.57 KB |

| questions.json | .json | 16.95 KB |

| questions.json | .json | 15.57 KB |

| questions.json | .json | 18.0 KB |

| bullet_reduction_results.json | .json | 2.31 KB |

| deep_slide_review.json | .json | 436.25 KB |

| slide_analysis.json | .json | 461.55 KB |

| slide_improvement_results.json | .json | 23.23 KB |

| slide_improvement_tasks.json | .json | 126.56 KB |

| ultra_deep_review.json | .json | 618.07 KB |

Reproducibility

This repository includes reproducibility tools:

Python requirements.txt

Status

Issues: Enabled
Wiki: Enabled
Pages: Enabled

README

Statistical Data Analysis

Teaching materials for a BSc-level course in Statistical Data Analysis, covering regression, hypothesis testing, dimensionality reduction, clustering, and time series analysis.

Course Overview

This repository contains complete lecture materials including Beamer slide decks, Python visualization scripts, R code examples, interactive quizzes, and practice datasets. Materials emphasize practical applications with real-world examples and hands-on exercises.

Course Structure

Lesson	Topic	Slides	Charts	Quiz
1	Linear Regression & Survival Analysis	120 frames (2 decks)	110	20 questions
2	Hypothesis Testing	25 frames	39	20 questions
3	PCA & Exploratory Factor Analysis	38 frames	43	20 questions
4	Cluster Analysis	106 frames	32	20 questions
5	Time Series Analysis	53 frames	40	20 questions

Technical Details

Format: LaTeX Beamer presentations (Madrid theme, 8pt, 16:9 aspect ratio)
Charts: Python (matplotlib, seaborn) — PNG for Lessons 1–4, standalone chart.py → PDF for Lesson 5
Code examples: R and Python throughout all lessons
Quizzes: 100 multiple-choice questions total — interactive HTML + LaTeX PDF per lesson
Practice datasets: 11 CSV files for hypothesis testing exercises

Folder Structure

statistical-data-analysis/
├── lesson1/                          # Regression & Survival Analysis
│   ├── linear_regression_complete.tex
│   ├── survival_km_slides.tex
│   ├── images/                       # 110 generated PNG charts
│   ├── questions.json                # 20 MC questions
│   ├── lesson1_quiz.tex
│   ├── generate_*.py                 # Chart generation scripts
│   └── regression_examples.R
├── lesson2_hypothesis/               # Hypothesis Testing
│   ├── hypothesis_testing.tex
│   ├── images/                       # 39 generated PNG charts
│   ├── datasets/                     # 11 practice CSV files
│   ├── questions.json
│   └── lesson2_hypothesis_quiz.tex
├── lesson3_pca_efa/                  # PCA & EFA
│   ├── pca_efa_komplett.tex
│   ├── images/                       # 43 generated PNG charts
│   ├── questions.json
│   └── lesson3_pca_efa_quiz.tex
├── lesson4_clustering/               # Cluster Analysis
│   ├── cluster_analysis_with_distance_slides.tex
│   ├── images/                       # 32 generated PNG charts
│   ├── questions.json
│   └── lesson4_clustering_quiz.tex
├── lesson5_timeseries/               # Time Series Analysis
│   ├── time_series_analysis.tex
│   ├── [40 chart folders]/           # Each with chart.py → chart.pdf
│   ├── questions.json
│   └── lesson5_timeseries_quiz.tex
├── quiz/                             # Interactive HTML quizzes
│   ├── quiz_L01.html ... quiz_L05.html
├── utils/                            # Shared utilities
│   └── generate_quiz.py
├── reviews/                          # Quality review tools
├── docs/                             # GitHub Pages dashboard
│   └── index.html
├── .github/workflows/                # CI/CD
│   ├── compile_slides.yml
│   ├── validate_charts.yml
│   └── validate_quizzes.yml
├── requirements.txt                  # Python dependencies
├── notation.tex                      # Shared LaTeX notation commands
├── notation_style_guide.md           # Notation conventions
├── charts.json                       # Chart inventory (264 total)
├── CHANGELOG.md
├── CONTRIBUTING.md
└── template_beamer_final.tex         # Beamer template

Requirements

Python 3.12+

pip install -r requirements.txt

Key packages: matplotlib, numpy, scipy, seaborn, pandas, scikit-learn, lifelines, statsmodels, arch

LaTeX

A LaTeX distribution with Beamer support (e.g., MiKTeX on Windows, TeX Live on Linux/macOS).

R

Packages: ggplot2, survival, cluster, factoextra, forecast, tseries

Quick Start

# Clone the repository
git clone https://github.com/Digital-AI-Finance/statistical-data-analysis.git
cd statistical-data-analysis

# Install Python dependencies
pip install -r requirements.txt

# Compile a slide deck
cd lesson1
pdflatex linear_regression_complete.tex

# Generate charts for Lesson 5
cd ../lesson5_timeseries
python ts_decomposition/chart.py

# Run quiz generation
cd ..
python generate_all_quizzes.py

Interactive Quizzes

Each lesson includes 20 multiple-choice questions available in two formats: - HTML (quiz/quiz_L01.html – quiz_L05.html): Interactive browser-based with KaTeX math rendering - PDF (lesson/lesson_quiz.tex): LaTeX Beamer with pause-reveal answers

Course Website

https://digital-ai-finance.github.io/statistical-data-analysis/

Contributing

See CONTRIBUTING.md for guidelines.

License

Educational use only.