DATA SCIENCE USING SAS

Our courses are available for individuals with various backgrounds to help them propel their career into analytics.

DATA SCIENCE
USING SAS

COURSE ID – DBAL-103     DURATION – 32 Hours

 
Program Objective
Starting from the basics of both SAS and Data Science (Introduction to SAS programming, Data importing, data manipulation, basic statistical concepts, etc.) to advanced topics (Predictive Analytics, Forecasting, etc.), this course will encompass all to help you emerge as an Industry ready professional in the analytics field. Teaching methodology will include a high focus on core concepts along with implementation on varied industry use-cases. Participants will be awarded a SAS Data Scientist Certification on completion of this course.
After the completion of Data Science using SAS course, you will be able to:

  • Use SAS for data importing, basic and advanced data manipulation, data analysis and visualization.
  • Use SAS for handling large data sets with optimized manner
  • Work on Advanced and predictive analytics techniques using SAS
  • Work on end to end data science projects
Who Should do this course?
This analytics certification course is for all those aspirants who want to switch into the field of data science/ business analytics and are keen to enhance their technical skills with exposure to cutting-edge practices. The Students/professionals/Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want to head start their career in analytics.
Pre-requisites
There are no prerequisites for this course. Knowledge of any programming and data analytics exposure would be an advantage. For beginners, its highly recommend to complete the “Data Analytics using Excel – Tableau” course prior to this course.
Modules & Topics

Introduction to Data ScienceSAS

  • Base SAS Refresher
  • Advanced SAS (Proc SQL – Macros) – Optimizing SAS Codes

Introduction to Statistics

  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
  • Inferential Statistics -Sampling – Concept of Hypothesis Testing
  • Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

Data Preparation

  • Need of Data preparation
  • Data Audit Report and Its importance
  • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
  • Variable Reduction Techniques – Factor & PCA Analysis

Segmentation

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioural Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling
  • Interpretation of results – Implementation on new data

Linear Regression

  • Introduction – Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers)
  • Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression

  • Introduction – Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers)
  • Interpretation of Results – Business Validation – Implementation on new data

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