Syllabus of Data Analytics Online Training in Michigan
Module 1: Descriptive Statistics
- 1. Introduction to the course
- 2. Descriptive Statistics
- 3. Probability Distributions
Module 2: Inferential Statistics
- 1. Inferential Statistics through hypothesis tests
- 2. Permutation & Randomization Test
Module 3: Regression & ANOVA
- 1. Regression
- 2. ANOVA(Analysis of Variance)
Module 4: Machine Learning: Introduction and Concepts
- 1. Differentiating algorithmic and model based
- 2. frameworks
- 3. Regression : Ordinary Least Squares, Ridge
- 4. Regression, Lasso Regression
Module 5: Supervised Learning with Regression and Classification techniques -1
- 1. Bias-Variance Dichotomy
- 2. NPTEl
- 3. Department of Management
- 4. StudiesIIT Madras
- 5. Model Validation Approaches
- 6. Logistic Regression
- 7. Linear DiscriminantAnalysis
- 8. Quadratic DiscriminantAnalysis
- 9. Regression and Classification Trees
- 10. Support Vector Machines
Module 6: Supervised Learning with Regression and Classification techniques -2
- 1. Ensemble Methods: Random Forest
- 2. Neural Networks
- 3. Deep learning
Module 7: Unsupervised Learning and Challenges for Big Data Analytics
- 1. Clustering
- 2. Associative Rule Mining
- 3. Challenges for big data anlalytics
Module 8: Prescriptive analytics
- 1. Creating data for analytics through designed experiments
- 2. Creating data for analytics through Active learning
- 3. Creating data for analytics through Reinforcement learning
