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 |