Analytics & 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.

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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