Chapter 1: Statistics and Probability Chapter Goal: Introduction and hands on approach to central limit theorem, distributions, confidence intervals, statistical tests, ROC curves, plots, probabilities, permutations and combinations No of pages: 70-80 Sub -Topics 1. Exploratory Data analysis 2. Probability Distributions 3. Concept of Permutations and Combinations 4. Statistical tests 5. Applications in the industry 6. Case study Chapter 2: Regression Chapter Goal: Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models. No of pages: 50-60 Sub - Topics 1. Concept of Regression 2. Linear regression 3. Polynomial order regression 4. Statistical tests 5. Applications in the industry 6. Case study & amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; Chapter 3: Time series models Chapter Goal: Introduction and hands on approach to concepts of trends, cycles, seasonal variations, anomaly detection, exponential smoothing, rolling moving averages, ARIMA, ARMA, over fitting. No of pages: 60-70 Sub - Topics: 1. Concept of trends, cycles, and seasonal variations 2. Time series decomposition 3. ARIMA, and ARMA models 4. Concept of over fitting 5. Statistical tests 6. Applications in the industry 7. Case study Chapter 4: Classification and Clustering Chapter Goal: Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networks No of pages: 80-90 Sub - Topics: 1. Concept of Classification and clustering 2. Deep neur3. Support Vector Machines 4. Concept of Gradient descent 5. Statistical tests 6. Applications in the industry 7. Case study Chapter 5: Ensemble methods Chapter Goal: Introduction and hands on approach to Bagging, and Gradient Boosting No of pages: 50-60 Sub - Topics: 1. Concept of ensemble methods 2. Concept of Bagging 3. Concept of Gradient Boosting 4. Statistical tests 5. Applications in the industry 6. Case study