About Data Sciences with SAS

In this SAS Training, you’ll become an expert in analytics techniques using the SAS data science tool. You’ll learn how to apply data manipulation and optimization techniques; advanced statistical concepts like clustering, linear regression and decision trees; data analysis methods to solve real world business problems and predictive modeling techniques. This SAS course will give you practical knowledge you can apply on your next data analysis job.

Course Content

Lesson 1: Analytics Overview
 Definition of Analytics
 Types of Analytics
 Analytics Problem Types
 Widely used tools and analytical techniques
Lesson 2: SAS Introduction
 Introduction of GUI
 Library statement, understanding of PDV
 Import / Export of Data
 Variable Attributes
 Basic Procedures
Lesson 3: Combining/Modifying Datasets
 Combining Data Sets Methods
 Concatenation
 Interleaving
 One to One Reading
 One to One Merging
 Data Manipulation steps and tools
 SAS Functions and Procedures for Data Manipulations
Lesson 4: PROC SQL
 Introduction and Advantages
 Options and Syntax – Understanding of Select Statement
 Joins in SQL
 Merge v/s Join
Lesson 5: SAS Macros
 Need for SAS Macros
 Macro Variables
 Automatic Macro Variables
 User-defined Macro Variables
 Macro Functions
 SYMBOLGEN System Option
 SQL Clauses for Macros
 The %Macro Statement
Lesson 6: Basic Statistics
 Descriptive Statistics
 Inferential Statistics
 Hypothesis testing
 Non parametric tests
Lesson 7: Basic Statistical Procedures
 PROC UNIVARIATE
 PROC MEANS
 PROC FREQ
 PROC CORR
 PROC REG
 PROC ANOVA
Lesson 8: Data Exploration
 Data Preparation
 Data Type Conversion
 Missing Value Treatment
 Summarizing Data
Lesson 9: Advanced Statistical Techniques
 Clustering
 Introduction
 Clustering Methodology
 Data Preparation
 K Means Clustering
 Cluster Profiling
 Decision Trees
 Introduction
 Creating Decision Trees
 Linear Regression
 Introduction
 Linear Regression in SAS
 Diagnostics
 Logistic Regression
 Introduction
 Logistic Regression in SAS
 Diagnostics
Lesson 10: Working with Time series Data
 Reading Simple Time Series
 Dating time series and working with SAS date and date time values
 Sub setting data and selecting observations
 Storing time series data in SAS data sets
 Plotting, transforming, transposing, interpolating
Lesson 11: Data Optimization using SAS
 Data Optimization
 Creating optimization models commonly used in industry
 Formulating and solving a data envelopment analysis
 Solving optimization problems using the OPTMODEL procedure in SAS

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