This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.
| Sr No | Notebook Topic | Colab |
|---|---|---|
| 1 | Elements of Structured Data |  |
| 2 | Rectangular Data | |
| 3 | Estimates of Location | |
| 4 | Estimates of Variability | |
| 5 | Exploring the Data Distribution | |
| 6 | Exploring Binary and Categorical Data | |
| 7 | Correlation | |
| 8 | Exploring Two or More Variables | |
| 9 | Random Sampling and Sample Bias | |
| 10 | Selection Bias | |
| 11 | Sampling Distribution of a Statistic | |
| 12 | The Bootstrap | |
| 13 | Confidence Intervals | |
| 14 | Normal Distribution | |
| 15 | Long-Tailed Distributions | |
| 16 | Student’s t-Distribution | |
| 17 | Binomial Distribution | |
| 18 | Chi-Square Distribution | |
| 19 | F-Distribution | |
| 20 | Poisson and Related Distributions | |
| 21 | A/B Testing | |
| 22 | Hypothesis Tests | |
| 23 | Resampling | |
| 24 | Statistical Significance and p-Values | |
| 25 | t-Tests | |
| 26 | Multiple Testing | |
| 27 | Degrees of Freedom | |
| 28 | ANOVA | |
| 29 | Chi-Square Test | |
| 30 | Multi-Arm Bandit Algorithm | |
| 31 | Power and Sample Size | |
| 32 | Simple Linear Regression | |
| 33 | Multiple Linear Regression | |
| 34 | Prediction Using Regression | |
| 35 | Factor Variables in Regression | |
| 36 | Interpreting the Regression Equation | |
| 37 | Regression Diagnostics | |
| 38 | Polynomial and Spline Regression | |
| 39 | Naïve Bayes | |
| 40 | Discriminant Analysis | |
| 41 | Logistic Regression | |
| 42 | Evaluating Classification Models | |
| 43 | Strategies for Imbalanced Data | |
| 44 | K-Nearest Neighbors | |
| 45 | Tree Models | |
| 46 | Bagging and the Random Forest | |
| 47 | Boosting | |
| 48 | Principal Components Analysis | |
| 49 | K-Means Clustering | |
| 50 | Hierarchical Clustering | |
| 51 | Model-Based Clustering | |
| 52 | Scaling and Categorical Variables | |