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AD-702 (D) · Predictive Analytics/Official Syllabus

Predictive Analytics (AD-702 (D)) - Official Syllabus

1
Unit 1

Introduction and Understanding Data

  • Introduction to predictive analytics
  • Business analytics: types, applications
  • Predictive models
  • Descriptive models
  • Decision models
  • Analytical techniques
  • Data types and associated techniques
  • Complexities of data
  • Data preparation, pre-processing
  • Exploratory data analysis
2
Unit 2

Principles and Techniques

  • Propensity models
  • Cluster models
  • Collaborative filtering
  • Univariate Statistical analysis
  • Multivariate Statistical analysis
  • Model Selection
  • Supervised versus unsupervised methods
  • Statistical and data mining methodology
  • Cross-validation
  • Overfitting
  • Bias-variance trade-off
  • Balancing the training dataset
  • Establishing baseline performance
3
Unit 3

Regression and Classification Models

  • Measuring Performance in Regression Models
  • Linear Regression and Its Cousins
  • Non-Linear Regression Models
  • Regression Trees and Rule-Based Models
  • Case Study: Compressive Strength of Concrete Mixtures
  • Measuring Performance in Classification Models
  • Discriminant Analysis and Other Linear Classification Models
  • Non-Linear Classification Models
  • Classification Trees and Rule-Based Models
  • Model Evaluation Techniques
4
Unit 4

Time Series Analysis

  • Introduction, Examples of time series
  • Stationary models and autocorrelation function
  • Estimation and elimination of trend and seasonal components
  • Stationary Process and ARMA Models - Basic properties and linear processes
  • Introduction to ARMA models
  • Properties of sample mean and autocorrelation function
  • Forecasting stationary time series
  • ARMA(p, q) processes
  • ACF and PACF
  • Modeling and Forecasting with ARMA
5
Unit 5

Nonstationary and Seasonal Time Series Models

  • ARIMA models
  • Identification techniques
  • Unit roots in time series
  • Forecasting ARIMA models
  • Seasonal ARIMA models
  • Regression with ARMA errors
  • Multivariate Time Series analysis
  • State-Space Models
  • Deep Learning techniques of time series forecasting

Reference Books

  • Predictive analytics for dummies — Anasse Bari, Mohamed Chaouchi, Tommy Jung
  • Data Science and Predictive Analytics: Biomedical and Health Applications using R — ID Dinov
  • Data Mining and Predictive analytics — Daniel T. Larose, Chantal D. Larose
  • Data Mining and Predictive Analytics, 2ed (An Indian Adaptation) — Daniel Larose, OP Wali
  • Predictive analytics using R — Jeffrey Strickland
  • Applied Predictive Modeling — Max Kuhn, Kjell Johnson
  • Introduction to Time Series and Forecasting — Peter J. Brockwell, Richard A. Davis
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