Welcome to the Statistics for Data Science repository! This repository aims to provide a comprehensive collection of resources, ipynb-tuts, and examples related to statistical techniques and their application in data science. Whether you are a beginner or an experienced data scientist, you'll find valuable content to enhance your understanding of statistics and their role in data-driven decision making.

• Descriptive Statistics
  • Mean
  • Median
  • Mode
  • Standard Deviation
  • Variance
  • Covariance
  • Correlation
  • Quartiles
  • IQR
  • Box Plots
• Inferential Statistics
  • Probability Distributions
    • Binomial
    • Poisson
    • Normal
  • Sampling
    • Confidence Intervals
    • Sample Size Selection
    • Statistical Significance
  • Hypothesis Testing
    • Z-tests
    • T-tests
    • ANOVA
    • Chi-Square tests
• Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Logistic Regression
• Time Series
  • Trend
  • Seasonality
  • Autocorrelation
  • ARIMA Models
• Statistical Visualization
  • Histograms
  • Density Plots
  • Q-Q Plots
  • Pair Plots
  • Correlation Heatmaps

Some useful resources for statistics in data science:

• Kaggle Learn Statistics Course
• Khan Academy Statistics and Probability
• Introduction to Statistical Thought by Michael Lavine
• Think Stats by Allen B. Downey
• YT Krish Naik
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron
• StatQuest with Josh Starmer

This repository is open source and contributions are welcome. If you have any ideas for hacks or tips, or if you find any errors, please feel free to open an issue or submit a pull request.

This repository is licensed under the MIT License.