A & P Modelling Course

Predictive modelling leverages statistics to predict outcomes.

Analytics and Predictive Modelling Online Training

SAS Rapid Predictive Modeler automatically guides users through a behind-the-scenes workflow of data preparation and data mining tasks, enabling them to generate their own models, derive on-demand insights and act on them to solve business problems quickly and effectively.

What you will learn

It also allows statisticians or modelers to generate quick, baseline models when they are short on time and resources. For the purposes of this paper, a customer level dataset is one in which each customer is represented on its own rowin a table so that customers can be the unit of analysis fora predictive model.

  • Introduction to Statistics
  • Analysing Categorical Data
  • Predictive Modelling

Analytics and Predictive Modelling Online Training Course Content

 

  • Introduction to Statistics
  • Introduction
  • Variables
  • Data Types
  • Scaling Techniques
  • Frequency Distributions
  • Descriptive Statistics
  • Introduction
  • Measure of Central Tendency
  • Measure of Dispersion
  • Skew ness and Kurtosis
  • Frequency Distributions
  • Bar Graphs
  • Plotting Data
  • Histograms, QQ Plots, and Probability Plots
  • Probability & Sampling
  • Introduction
  • Random Variable
  • Expectations
  • Continuous Probability Distributions [Uniform, Normal, Exponential]
  • Discrete Probability Distributions[Binomial, Poisson, Negative Binomial, Hyper Geometric]
  • Estimations
  • Sampling Theory[Probability and Non Probability]
  • Inferential Statistics
  • Introduction
  • Inferential Statistics – Hypothesis Testing [Parametric & Non-Parametric]
  • T-test: Testing Single Means
  • T-test: Testing Differences between Two Means
  • Random Assignment of Subjects
  • Two Independent Samples: Distribution Free Tests
  • One-tailed versus Two-tailed Tests
  • Paired T-tests (Related Samples)
  • F – Test for Variances
  • Analysis of Variance
  • Introduction
  • One-way Analysis of Variance
  • Two-way Analysis of Variance
  • Interpreting Significant Interactions
  • Unbalanced Designs: PROC GLM
  • Analysis of Covariance [ANCOVA]

 

  • Analysing Categorical Data
  • Introduction
  • Questionnaire Design and Analysis
  • Two-way Frequency Tables
  • A Short-cut Way to Request Multiple Tables
  • Computing Chi-square from Frequency Counts
  • McNamara’s Test for Paired Data
  • Computing the Kappa Statistics (Coefficient of Agreement)
  • Odds Ratios
  • Relative Risk
  • Chi-square Test for Trend
  • Mantel-Haenszel Chi-square for Stratified Tables and Meta-Analysis
  • Correlation and Simple Regression
  • Correlation
  • Significance of a Correlation Coefficient
  • Partial Correlations
  • Linear Regression
  • Partitioning the Total Sum of Squares
  • Plotting the Points on the Regression Line
  • Plotting Residuals and Confidence Limits
  • Adding a Quadratic Term to the Regression Equation
  • Transforming Data
  • Predictive Modelling
  • Introduction
  • Preparation of Data
  • Multiple Regression [Model diagnostics]
  • Logistic Regression [Model diagnostics]
  • Factor Analysis - PCA
  • Discriminant Analysis
  • Pattern Discovery [Cluster Analysis, Market Basket Analysis]
  • Forecasting [MA, AR, ARMA, ARIMA]
  • Decision Tree Analysis [CHAID, CART]
  • Neural Networks
  • Modell Assessment
  • Model Implementation
  • This Course is taught through one of the Real Time Projects
  • Capital Market Domain,
  • Market Research Domain,
  • Finance Domain[Credit Card/ Insurance and Banking Domains]
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