**This is what you will be learning in the course:**

- Introduction to R and RStudio
- Inferential Statistics
- Probability
- Statistics (Central Tendency, Spread of Data, Data Distributions, ANOVA, CHI SQUARE Test)
- Univariate Analysis
- Bivariate Analysis
- Hypothesis Testing (t-test, z-test, p-value)
- Quiz

- Linear Regression
- Understanding the concept Linear Regression
- Maths behind Linear Regression
- Lasso Regression and Ridge Regression
- Case Study 1 (Theory + Practicals + Reporting)
- Case Study 2 (Theory + Practicals + Reporting)
- Quiz

- Logistic Regression
- Understanding the concept of Logistic Regression
- Maths behind Logistic Regression
- AUC and ROC Curves, Confusion Matrix (Accuracy, Sensitivity and Specificity, Precision and Recall)
- Cutoff Methods in Logistic Regression (Min Distance method, KS Method, Lift Method, F1 Beta Method)
- Case Study 1 (Theory + Practicals + Reporting)
- Case Study 2 (Theory + Practicals + Reporting)
- Quiz

- Capstone Project 1 for Certification
- Clustering
- Hierarchical Clustering (Maths + Case Study + Practicals + Reporting)
- K-Means Clustering (Maths + Case Study + Practicals + Reporting)
- DB Scan (Maths + Case Study + Practicals + Reporting)
- Quiz

- Decision Trees
- Maths behind decision trees – Information Gain, Remainder, Loss
- C 4.5 (Maths + Case Study + Practicals + Reporting)
- Random Forest (Maths + Case Study + Practicals + Reporting)
- Quiz

- Time Series
- Understanding Time Series and its concepts
- Exponential Smoothening 1 (Maths + Case Study + Practicals + Reporting)
- Exponential Smoothening 2 (Maths + Case Study + Practicals + Reporting)
- Exponential Smoothening 3 (Maths + Case Study + Practicals + Reporting)
- ARIMA (Maths + Case Study + Practicals + Reporting)
- Quiz

- Introduction to Boosting, Bagging and Cross Validation
- Master Quiz for Certification
- Capstone Project 2 for Certification