Data Science Certification Training – SAS Programming
Data Science Certification Training – SAS Programming
1.001 Introduction
Introduction to Business Analytics
- Types of Analytics
- Areas of Analytics
- Analytical Tools
- Analytical Techniques
Lesson 02 – Introduction to SAS
- Introduction
- What is SAS
- Navigating in the SAS Console
- SAS Language Input Files
- Data Step
- PROC Step and DATA Step – Example
- DATA Step Processing
- SAS Libraries
- Demo – Importing Data
- Demo – Exporting Data
Lesson 03 – Combining and Modifying Datasets
- Introduction
- Why Combine or Modify Data
- Concatenating Datasets
- Interleaving Method
- One – to – one Reading
- One – to – one Merging
- Knowledge Check
- Data Manipulation
- Modifying Variable Attributes
Lesson 04 – PROC SQL
- Introduction
- What is PROC SQL
- Retrieving Data from a Table
- Demo – Retrieve Data from a Table
- Selecting Columns in a Table
- Knowledge Check
- Retrieving Data from Multiple Tables
- Selecting Data from Multiple Tables
- Concatenating Query Results
Lesson 05 – SAS Macros
- Introduction
- Need for SAS Macros
- Macro Functions
- Macro Functions Examples
- SQL Clauses for Macros
- Knowledge Check
- The Macro Statement
- The Conditional Statement
Lesson 06 – Basics of Statistics
- Introduction
- Introduction to Statistics
- Statistical Terms
- Procedures in SAS for Descriptive Statistics
- Demo – Descriptive Statistics
- Hypothesis Testing
- Variable Types
- Hypothesis Testing – Process
- Knowledge Check
- Demo – Hypothesis Testing
- Parametric and Non – parametric Tests
- Parametric Tests
- Non – parametric Tests
- Parametric Tests – Advantages and Disadvantages
Lesson 07 – Statistical Procedures
- Introduction
- Statistical Procedures
- PROC Means
- PROC Means – Examples
- PROC FREQ
- Demo – PROC FREQ
- PROC UNIVARIATE
- Demo – PROC UNIVARIATE
- Knowledge Check
- PROC CORR
- Proc Corr Options
- Demo – PROC CORR
- PROC REG
- Proc Reg Options
- Demo – PROC REG
- PROC ANOVA
- Demo – PROC ANOVA
Lesson 08 – Data Exploration
- Introduction
- Data Preparation
- General Comments and Observations on Data Cleaning
- Knowledge Check
- Data Type Conversion
- Character Functions
- SCAN Function
- DateTime Functions
- Missing Value Treatment
- Various Functions to Handle Missing Value
- Data Summarization
Lesson 09 – Advanced Statistics
- Introduction
- Introduction to Cluster
- Clustering Methodologies
- Demo – Clustering Method
- K Means Clustering
- Knowledge Check
- Decision Tree
- Regression
- Logistic Regression
Lesson 10 – Working with Time Series Data
- Introduction
- Need for Time Series Analysis
- Time Series Analysis – Options
- Reading Date and Datetime Values
- White Noise Process
- Stationarity of a Time Series
- Knowledge Check
- Demo — Stages of ARIMA Modelling
- Plot Transform Transpose and Interpolating Time Series Data
Lesson 11 – Designing Optimization Models
- Introduction
- Need for Optimization
- Optimization Problems
- PROC OPTMODEL
