Statistics Learning Journey 📊

A structured and practical learning journey covering the fundamental and applied concepts of Statistics using Python and Jupyter Notebook.

This repository focuses on understanding statistical concepts clearly, applying them with Python, visualizing data, and connecting Statistics with Data Science and Machine Learning.

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🎯 Learning Objectives

The main goal of this repository is to build a strong practical understanding of Statistics and learn how statistical concepts are used in real-world data analysis and Machine Learning.

Throughout this journey, I have worked on:

• Understanding fundamental statistical concepts
• Describing and summarizing data
• Understanding probability and probability distributions
• Working with different sampling techniques
• Studying relationships between variables
• Detecting and understanding outliers
• Performing hypothesis and statistical tests
• Understanding feature relationships
• Visualizing statistical data
• Connecting Statistics with Machine Learning

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📚 Topics Covered

1. Introduction to Statistics

• Basic statistical concepts
• Population and sample
• Types of data
• Descriptive and inferential statistics

2. Central Tendency

• Mean
• Median
• Mode
• Practical interpretation of central values

3. Measures of Dispersion

• Range
• Variance
• Standard Deviation
• Understanding data spread

4. Data Distribution

• Understanding distributions
• Distribution patterns
• Practical interpretation of data

5. Probability

• Basic probability concepts
• Probability calculations
• Real-world applications

6. Probability Distributions

• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution
• Uniform Distribution
• Normal Distribution
• Exponential Distribution

7. Sampling

• Sampling concepts
• Random Sampling
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling
• Sampling Bias
• Sampling Error
• Sample Size

8. Correlation

• Correlation
• Covariance
• Positive Correlation
• Negative Correlation
• Pearson Correlation
• Spearman Correlation
• Correlation Matrix
• Heatmap

9. Outlier Detection

• Understanding Outliers
• Types of Outliers
• IQR Method
• Z-Score Method
• Box Plot
• Scatter Plot

10. Hypothesis Testing

• Null Hypothesis
• Alternative Hypothesis
• p-value
• Confidence Interval
• Significance Level
• Type I Error
• Type II Error
• One-tailed Test
• Two-tailed Test

11. Statistical Tests

• T-Test
• Z-Test
• Chi-Square Test
• ANOVA
• Mann-Whitney U Test
• Shapiro-Wilk Test

12. Feature Relationships

• Correlation Matrix
• Multicollinearity
• Variance Inflation Factor (VIF)
• Feature Importance
• Feature Selection Basics

13. Statistical Data Visualization

• Statistical visualizations
• Distribution plots
• Comparison plots
• Relationship plots
• Data interpretation through visualization

14. Statistics in Machine Learning

• Role of Statistics in Machine Learning
• Statistical concepts used during ML workflows
• Understanding data before model building
• Statistical thinking for feature analysis and model evaluation

15. Mini Assessment

• Practical assessment of the Statistics concepts covered throughout the learning journey
• Application of statistical concepts using Python
• Review and reinforcement of key topics

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🧰 Tools & Technologies

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• SciPy
• Scikit-learn

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🗂️ Repository Structure

Statistics-Learning-Journey/
│
├── 01_Introduction_to_Statistics.ipynb
├── 02_Central_Tendency.ipynb
├── 03_Measures_of_Dispersion.ipynb
├── 04_Data_Distribution.ipynb
├── 05_Probability.ipynb
├── 06_Probability_Distributions.ipynb
├── 07_Sampling.ipynb
├── 08_Correlation.ipynb
├── 09_Outliers.ipynb
├── 10_Hypothesis_Testing.ipynb
├── 11_Statistical_Tests.ipynb
├── 12_Feature_Relationships.ipynb
├── 13_Statistical_Visualization.ipynb
├── 14_Statistics_in_ML.ipynb
└── 15_Mini_Assessment.ipynb