About the course

This course is designed to understand how data manipulation and optimization techniques are applied; statistical concepts like clustering, regression, decision trees and data analysis methods to tackle real-world business problems and predictive modeling techniques

What are the requirements?

"No prior knowledge of Statistics the language of SAS or analytic techniques is required."

What I am going to get from this course

  • Understand the role of data scientists, various analytics techniques, and widely used tools
  • Know about SAS, the role of GUI, library statements, and how to import and export the data and variable attributes
  • Gain an inside and out comprehension statistics, hypothesis testing, and advanced statistical techniques like clustering, decision trees, linear regression, and logistic regression
  • Understand the various techniques for combining and modifying datasets like concatenation, interleaving, one-to-one merging and reading. You will likewise learn the various SAS functions and procedure for data manipulation
  • Learn the power of SAS Macros and how they can be utilized for quicker data manipulation and for diminishing the amount of regular SAS code required for analytics
  • Learn and perform data exploration techniques using SAS.

Who is the target audience?

Analytics professionals who want to work with SAS IT professionals looking for a career switch in the fields of analytics Graduates looking to build a career in analytics and data science

Curriculum


Module 1: Data Analytics

  • 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

Module 2: 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
  • 2.7  DATA Step Processing
  • 2.8  SAS Libraries
  • 2.9  Importing and Exporting Data

Module 3: Working with DataSets

  • 3.1  Introduction
  • 3.2  Why Combine or Modify Data
  • 3.3  Concatenating Datasets
  • 3.4  Interleaving Method
  • 3.5  One - to - one Reading
  • 3.6  One - to - one Merging
  • 3.7  Data Manipulation
  • 3.8  Modifying Variable Attributes

Module 4: 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

Module 5: 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

Module 6: Understanding Statistics

  • 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

Module 7: Statistics Procedure

  • 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

Module 8: 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

Module 9: 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

Module 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

Module 11: Designing Optimization Models

  • 11.1  Introduction
  • 11.2  Need for Optimization
  • 11.3  Optimization Problems
  • 11.4  PROC OPTMODEL
Request a detailed syllabus.

Get Answers (Answering their questions)

What kind of learning does ITlearn360.com provide?

ITlearn360.com offers instructor-led online live sessions and classroom-based corporate trainings and bootcamps for various courses and certifications to the learners.

Who are the instructors @ITlearn360.com?

@ITlearn360.com, we have an instructor community of industry professionals who are working in leading organizations and are veterans in their respective fields. These experts belong to various industries and are willing to share their talent with learners like you.

Are classes @ITlearn360.com conducted through online video streaming?

Yes, the classes @ITlearn360.com are conducted through online video streaming where there is two-way communication between users and instructors. The users can speak by using a microphone, chat by sending a message through a chat window and share their screens with an instructor. For better understanding, users also get recorded video of the class.

Sign for next demo class