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Data Science with SAS - Simplilearn

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RM 1715.70
  1. 40 Hours
  1. HRDF SBL Claimable
  2. Certificate of Attendance available
  3. 180 days of access from date of purchase
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Data Science with SAS - Simplilearn

WHAT YOU WILL LEARN

The Data Science with SAS Advanced course has been designed keeping in mind the most in demand skills in the SAS Analytics Space. SAS continues to dominate the advanced analytics market and hence explores different SAS libraries and tools that help you tackle each stage of Data Analytics.

The course starts off with a brief introduction to Analytics, statistical concepts pertaining to Data Analytics, and a few basic concepts of SAS programming.

It then goes on to cover in-depth content for PROC SQL, SAS macros, and various statistical procedure. The course also covers advanced statistics and optimization models

All these concepts are presented in an easy to understand manner, using actual coding examples and demos to clarify the concepts and present the actual method of implementation.

AUDIENCE

  • Analytics professionals who want to work with SAS
  • Software professionals looking for a career switch in the field of analytics
  • Graduates looking to build a career in Analytics and Data Science
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of Data Science and analytics

COURSE OBJECTIVES

After completing this course, you will be able to:

  • Outline what Data Science and how SAS can help implement it
  • Explain the different methods used to combine and modify datasets
  • Explain what PROC SQL is and how it is used to retrieve data from single and multiple tables.
  • Describe how to use macro function to manipulate the character strings and text.
  • Explore the various testing techniques used in an inferential statistic.
  • List the various statistical procedures and learn how to create graphs and interpret the results.
  • Understand how SAS handles missing values in your datasets using various procedures.
  • Explain the ways to create a cluster and to perform cluster analysis on the dataset.
  • List the various time series models of SAS.
  • Illustrate the problems involved in optimization. Perform sharp data analysis to make business decisions.

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Modules

Lesson 1 - Introduction to Analytics
  • Analytics v/s Analysis
  • What is Analytics?
  • Popular Tools
  • Role of Data Scientist
  • Analytics Methodology
  • Problem Definition
  • Summarizing Data
  • Data Collection
  • Data Dictionary
  • Outlier Treatment
Lesson 2 - Statistical Concepts And Their Application In Business
  • Descriptive Statistics
  • Probability Theory
  • Tests of Significance
  • Non-parametric Testing
Lesson 3 - Basic Analytic Techniques - Using SAS
  • Introduction
  • Data Exploration
  • Data Visualization
  • Diagnostic Analytics
Lesson 4 - Predictive Modelling Techniques
  • Linear Regression
  • Logistic Regression
  • Cluster Analysis
  • Time Series Analysis

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