Data Science with SAS - 24:00 hoursCOURSE OBJECTIVES:
• Understand analytics, the various analytics techniques, and the widely used tools
• Gain an understanding of SAS, the role of GUI, Library statements, importing and exporting of data and variable attributes
• Gain an in-depth understanding of statistics, hypothesis testing, and advanced statistics techniques like Clustering, decision trees, linear regression, and logistic regression
• Learn the various techniques for combining and modifying datasets like concatenation, interleaving, one-to-one merging and reading. You will also learn the various SAS functions and procedure for data manipulation
• Understand PROC SQL, its syntax, and master the various PROC statements and subsequent statistical procedures used for analytics including PROC UNIVARIATE, PROC MEANS, PROC FREQ, PROC CORP, etc.
• Understand the power of SAS Macros and how it can be used for faster data manipulation and for reducing the amount of regular SAS code required for analytics
• Gain an in-depth understanding of the various types of Macro variables, Macro function SYMBOLGEN System options, SQL clauses, and the %Macro statement
• Learn and perform data exploration techniques using SAS
• Understand various time series models and work on those using SAS
• Model, formulate, and solve data optimization by using SAS and OPTMODEL procedure
COURSE LESSONS:
Lesson 01 - Analytics Overview
1.1 Introduction
1.2 Introduction to Business Analytics
1.3 Types of Analytics
1.4 Areas of Analytics
1.5 Analytical Tools
1.6 Analytical Techniques
1.7 Quiz
1.8 Key Takeaways
Lesson 02 - Introduction to SAS
2.1 Introduction
2.2 What is SAS
2.3 Navigating in the SAS Console
2.4 SAS Language Input Files
2.5 DATA Step
2.6 PROC Step and DATA Step - Example
2.7 DATA Step Processing
2.8 SAS Libraries
2.9 Demo - Importing Data
2.10 Demo - Exporting Data
2.11 Knowledge Check
2.12 Assignment
2.13 Quiz
2.14 Key Takeaways
Lesson 03 - Combining and Modifying Datasets
3.1 Introduction
3.2 Why Combine or Modify Data?
3.3 Concatenating Datasets
3.4 Interleaving Method
3.5 Knowledge Check 1
3.6 One-to-one Reading
3.7 One-to-one Merging
3.8 Knowledge Check 2
3.9 Data Manipulation
3.10 Modifying Variable Attributes
3.11 Assignment 1
3.12 Assignment 1 - Solution
3.13 Assignment 2
3.14 Assignment 2 - Solution
3.15 Activity
3.16 Quiz
3.17 Key Takeaways
Lesson 04 - PROC SQL
4.1 Introduction
4.2 What is PROC SQL
4.3 Retrieving Data from a Table
4.4 Demo-Retrieve Data from a Table
4.5 Knowledge Check 1
4.6 Selecting Columns in a Table
4.7 Knowledge Check 2
4.8 Retrieving Data from Multiple Tables
4.9 Selecting Data from Multiple Tables
4.10 Concatenating Query Results
4.11 Activity
4.12 Assignment 1
4.13 Assignment 1 - Solution
4.14 Assignment 2
4.15 Assignment 2 - Solution
4.16 Quiz
4.17 Key Takeaways
Lesson 05 - SAS Macros
5.1 Introduction
5.2 Need for SAS Macros
5.3 Macro Functions
5.4 Macro Functions Examples
5.5 SQL Clauses for Macros
5.6 Knowledge Check
5.7 The %Macro Statement
5.8 The Conditional Statement
5.9 Activity
5.10 Assignment
5.11 Assignment - Solution
5.12 Quiz
5.13 Key Takeaways
Lesson 06 - Basics of S
tatistics
6.1 Introduction
6.2 Introduction to Statistics
6.3 Statistical Terms
6.4 Procedures in SAS for Descriptive Statistics
6.5 Demo - Descriptive Statistics
6.6 Knowledge Check 1
6.7 Hypothesis Testing
6.8 Variable Types
6.9 Hypothesis Testing - Process
6.10 Knowledge Check 2
6.11 Demo - Hypothesis Testing
6.12 Parametric and Non - parametric Tests
6.13 Parametric Tests
6.14 Non-parametric Tests
6.15 Parametric Tests - Advantages and Disadvantages
6.16 Quiz
6.17 Key Takeaways
Lesson 07 - Statistical Procedures
7.1 Introduction
7.2 Statistical Procedures
7.3 PROC Means
7.4 PROC Means - Examples
7.5 Knowledge Check 1
7.6 PROC FREQ
7.7 Demo - PROC FREQ
7.8 PROC UNIVARIATE
7.9 Demo - PROC UNIVARIATE
7.10 Knowledge Check 2
7.11 PROC CORR
7.12 PROC CORR Options
7.13 Demo - PROC CORR
7.14 PROC REG
7.15 PROC REG Options
7.16 Demo - PROC REG
7.17 Knowledge Check 3
7.18 PROC ANOVA
7.19 Demo - PROC ANOVA
7.20 Activity
7.21 Assignment 1
7.22 Assignment 1 - Solution
7.23 Assignment 2
7.24 Assignment 2 - Solution
7.25 Quiz
7.26 Key Takeaways
Lesson 08 - Data Exploration
8.1 Introduction
8.2 Data Preparation
8.3 General Comments and Observations on Data Cleaning
8.4 Knowledge Check
8.5 Data Type Conversion
8.6 Character Functions
8.7 SCAN Function
8.8 Date/Time Functions
8.9 Missing Value Treatment
8.10 Various Functions to Handle Missing Value
8.11 Data Summarization
8.12 Assignment
8.13 Assignment - Solution
8.14 Quiz
8.15 Key Takeaways
Lesson 09 - Advanced Statistics
9.1 Introduction
9.2 Introduction to Cluster
9.3 Clustering Methodologies
9.4 Demo - Clustering Method
9.5 K Means Clustering
9.6 Knowledge Check
9.7 Decision Tree
9.8 Regression
9.9 Logistic Regression
9.10 Assignment 1
9.11 Assignment 1 - Solution
9.12 Assignment 2
9.13 Assignment 2 - Solution
9.14 Quiz
9.15 Key Takeaways
Lesson 10 - Working with Time Series Data
10.1 Introduction
10.2 Need for Time Series Analysis
10.3 Time Series Analysis-Options
10.4 Reading Date and Datetime Values
10.5 Knowledge Check 1
10.6 White Noise Process
10.7 Stationarity of a Time Series
10.8 Knowledge Check 2
10.9 Demo - Stages of ARIMA Modelling
10.10 Plot, Transform, Transpose, and Interpolating Time Series Data
10.11 Assignment
10.12 Assignment - Solution
10.13 Quiz
10.14 Key Takeaways
Lesson 11 - Designing Optimization Models
11.1 Introduction
11.2 Need for Optimization
11.3 Optimization Problems
11.4 PROC OPTMODEL
11.5 Optimization - Example 1
11.6 Optimization - Example 2
11.7 Assignment
11.8 Assignment - Solution
11.9 Quiz
11.10 Key Takeaways
Projects:
Project 01
Project 01-Data-Driven Macro Calls
Project 02
Project 02-Customer Segmentation with RFM Methodology
Project 03
Project 03-Attrition Analysis
Project 04
Project 04-Retail Analysis
Test Papers:
Data Science with SAS Simulation Test 1
Data Science with SAS Simulation Test 2
Data Science with SAS Simulation Test 3 |