Data Science using SAS

Data Science using SAS
Course duration:

40 hours | 2 months (Live online/classroom training+ Projects + assignment/case studies + Interview preparation)

Training Mode:​

Online & Classroom

Course Fees:
  • Group training — INR 15000 USD 450 (Other countries)
  • Individual training– INR 32000 | USD 750 (Other countries)
Pre-requisites for Data Science course:

To attend this course candidates must have good understanding of basic and advance SAS and basic statistics.

Certification:

At end of our course, you will be work on various projects and assignmetns. Once you completed your assigned projects with expected results we will issue Certificate.

Syllabus - program of study:

  • Introduction to Data Analytic's and Statistical Techniques
  • Types of Variables, Measures of Central Tendency and Dispersion
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Central Limit Theorem and Application
  • Parametric method vs. Non-Parametric method
  • Null Hypothesis
  • Alternative Hypothesis
  • P Value Interpretation
  • Z Test
  • T test
  • One Sample t test
  • Paired Sample t test
  • Two sample (Independent) t test
  • Analysis of Variance (ANOVA)
  • Chi Square Test
  • Correlation Analysis
  • Dealing with Duplicates
  • Outlier treatment
  • Missing values
  • Dummy creation
  • Variable Reduction
  • Introduction of variable reduction techniques
  • Introduction to Factor Analysis
  • Introduction to PCA Analysis
  • Scree plot
  • Eigenvalue , Eigenvector
  • Factor Rotation and Extraction
  • Result Interpretation
  • Segmentation
  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques
  • Value Based
  • RFM Segmentation
  • Life Stage Segmentation
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Introduction to Cluster Techniques
  • Cluster evaluation and profiling
  • Interpretation of results - Implementation on new data.
  • Introduction of Linear Regression
  • Applications and Assumptions of Linear Regression
  • Create training and test samples
  • Building Linear Regression Model
  • Understanding standard metrics
  • Variable significance
  • R- square
  • Adjusted R-square
  • Global hypothesis etc.
  • Validation of Models: Training-Validation approach
  • Standard Business Outputs
  • Decile Analysis,
  • Error distribution (histogram)
  • Model equation
  • Drivers etc.
  • Interpretation of Results - Business Validation - Implementation on new data
  • Interpretation of model parameters.
  • Introduction of Logistic Regression
  • Applications and Assumptions of Logistic Regression
  • Create training and test samples
  • Building Logistic Regression Model
  • Understanding standard model metrics
  • Concordance
  • Hosmer Lemeshov Test
  • Gini, KS , Somers'D
  • Misclassifications, etc.
  • Confusion Matrix
  • Validation of Logistic Regression Models
  • Standard Business Outputs (ROC Curve, AUC, Decile Analysis, etc.)
  • Interpretation of Results - Business Validation
  • Implementation on new data Logistic Regression
  • Case studies
  • Assignments
  • projects with industry data