eLearning Bundle - SAS Online Course

SAS TrainingeLearning Bundle - SAS

by Certstaffix® Training

Length: 1 Course(s)      Price: $800/person (US Dollars)      Bulk Pricing: 6+ Contact Us      Access Length: 6 Months      Category: SAS


The data science with SAS certification training is designed to impart an in-depth knowledge of SAS programming language, SAS tools, and various advanced analytics techniques. The training provides a solid base for implementing these techniques. Mastering SAS and related tools: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics concepts: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS.
Purchase This Course ➥
Course Description: Print It | Download PDF | Email It

  Online SAS Classes: Learn at Your Own Pace

We will enroll you into a demo (Windows 10) eLearning session. Login information will be emailed to you.

Prefer a self-paced learning solution to fit your own schedule? Certstaffix® Training offers eLearning courses bundled together:

  • Learn at your own pace - Start and stop as it is convenient for you. Pick up where you left off.
  • Lecture utilizing video and recorded screen shots
  • 6 month subscription length
  • Any software needed for training is provided in an online lab environment.

  Detailed Training Topics

Course Description: Print It | Download PDF | Email It
SAS Training

Data Science with SAS - 24:00 hours

COURSE 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
Course Description: Print It | Download PDF | Email It