Data Science with SAS

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Data Science with SAS Course Overview

This is an integrated course developed with SAS programming essentials with data stem manipulation, Predictive modelling, SAS SQL & Macro. This program is most suitable for job seekers who want to become a Data Scientist on SAS. Manipulate data, perform queries and analyses, and generate reports, with predictive modeling techniques. This program will cover the curriculum for SAS Global certifications. 

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30,240 / $ 403

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Course Description!

Data Science with SAS Course Curriculum

This course covers SAS Base programming, Macro, SQL, Predictive modeling, core statistics with case studies an and capstone project.

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Course content

  • What is analytics & Data Science?
  • Different between Data Science Big data and data analysis
  • Data understanding: real life example
  • Why data science and machine learning is future
  • Analytics vs. Data warehousing
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Overview of analytics tools & their popularity
  • List of steps in Data science projects
  • Process of Data science projects
  • .Identify the most appropriate solution design for the given problem
    statement
  • Necessary Programming Languages and Software Used in Data Science
  • Why R and Python for data science?
  • What is R
  • What is S
  • History of R
  • Features of R
  • SAS versus R
  • Installing R
  • Packages
  • Input/output
  • R interfaces
  • R Library
  • Basic operations in R
  • Different data types and data structures in R
  • Sub setting in R
  • Additional topics on data structures
  • Importing data sets in R
  • R loops and special functions
  • Calculation of commission and simple interest
  • Plots and charts in R
  • Merging and sorting functions in R
  • Summarising Data
  • Calculations of the measures of central tendency and measures of variability
  • Concept of hypothesis
  • Null hypothesis
  • Alternative hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Interval
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Two independent sample T test
  • Paired Sample T test
  • Chi square Test for Independence of Attributes.
  • One Way Anova
  • Two Way Anova
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Types of Clusters
  • Metric and linkage
  • Ward’s Minimum Variance Criteria
  • Semi-Partial R-Square and R-Square
  • Diagrammatic Representation of clusters
  • Problems of Cluster Analysis
  • Concept of Regression and features of Linear line.
  • Assumptions of Classical Linear Model
  • Method of Least Squares
  • Understanding the Goodness of Fit
  • Test of Significance of The Estimated Parameters
  • Multiple linear Regression with their Assumptions
  • Concept of Multocollinearity
  • Signs of Multicollinearity
  • The Idea Of Autocorrelation
  • Concept and Applications of Logistic Regression
  • Principles Behind Logistic Regression
  • Comparison between Linear probability Model and Logistic Regression
  • Mathematical Concepts related to Logistic Regression
  • Concordant Pairs, Discordant Pairs and Tied Pairs
  • Classification Table
  • Graphical Representation Related to logistic Regression.
  • Decision Tree
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
  • Introduction to python
  • Installation of Anaconda
  • Python Variable
  • Understanding more about variable
  • User input variable
  • Calculate discounted price
  • Conditional Statements
  • Python string functions
  • Python Numeric function
  • Introduction to Loop
  • FOR loop for Sum
  • FOR loop for multiplication
  • While loop
  • Conditional statement(if elif else)
  • More of if else elif
  • Introduction to python list
  • Indexing of Python List
  • slicing of list
  • modifying a list
  • List method
  • Python Tuple
  • Functions on Tuple
  • Python Dictionary
  • Fuctions on dictionary
  • Methods of Dictionary

  • Python Set
  • Create your own functions
  • Classes in python
  • Inheritance in Python
  • Time management function
  • Introduction to numpy
  • Array in Numpy
  • Matrices using Numpy
  • Mathematical fuctions using numpy
  • Introduction to panda
  • Data frame in pandas
  • Group-by in pandas
  • Recap of linear regression theory
  • Application of linear regression using Python Library
  • Recap of Logistic regression theory
  • Application of logistic regression using Python Library
  • SVM theory: Linear, Dual SVM and Kernel Trick
  • SVM code along
  • Curse of dimensionality
  • Principal Component Analysis
  • Singular Value Decomposition
  • Independent Component Analysis
  • Fisher Linear Discriminate Analysis
  • k-Means Clustering: Theory and Code-along
  • Hierarchical Clustering: Theory and Code-along
  • Gaussian Mixture Model: Theory and Code-along
  • An Overview of the SAS System
  • Introduction to SAS Programs
  • Running SAS Programs
  • Mastering Fundamental Concepts
  • Diagnosing and Correcting Syntax Errors
  • Exploring Your SAS Environment
  • SAS Data Libraries
  • Getting Started With the PRINT Procedure
  • Sequencing and Grouping Observations
  • Identifying Observations
  • Special WHERE Statement Operators   
  • Customizing Report Appearance
  • Formatting Data Values
  • Creating HTML Reports
  • Reading Raw Data Files: Column Input
  • Reading Raw Data Files: Formatted Input
  • Examining Data Errors
  • Assigning Variable Attributes
  • Changing Variable Attributes    
  • Reading Excel Spreadsheets
  • Reading SAS Data Sets and Creating Variables
  • Conditional Processing
  • Dropping and Keeping Variables
  • Reading Excel Spreadsheets Containing Date Fields
  • Concatenating SAS Data Sets
  • Merging SAS Data Sets
  • Combining SAS Data Sets : Additional Features
  • Introduction of Summary Reports.
  • Basic Summary Reports
  • The Report Procedure
  • The Tabulate Procedure
  • Producing Bar and pie Chart
  • Enhancing output
  • Producing Plots
  • Overview
  • Review of SAS basics
  • Review of DATA Step Processing
  • Review of Displaying SAS Data Sets
  • Working with Existing SAS Data Sets
  • Outputting Multiple Observations
  • Writing to Multiple SAS Data Sets
  • Selecting Variables and Observations
  • Writing to an External File
  • Creating an Accumulating Total variable
  • Accumulating Totals for a Group of Data
  • Reading Delimited Raw Data Files
  • Controlling When a Record Loads
  • Reading Hierarchical Raw data Files
  • Introduction
  • Manipulating Character values
  • Manipulating Numeric values
  • Manipulating Numeric values based on Dates
  • Converting variable Type
  • Using the PUT Statement
  • Using the DEBUG Option
  • Do Loop Processing
  • SAS Array Processing
  • Using SAS Arrays
  • Match-merging Two or more SAS Data Sets
  • Simple Joins Using the SQL Procedure
  • What is SQL?
  • What is the SQL Procedure?
  • Terminology
  • Comparing PROC SQL with the SAS DATA step
  • Note about the Example Table
  • Overview of the select Statement
  • Selecting Columns in a Table
  • Creating New Columns
  • Sorting Data
  • Retrieving rows that satisfy a Condition
  • Summarizing Data
  • Grouping Data
  • Filtering Grouped Data
  • Introduction
  • Selecting Data from More Than One Table by Using joins
  • Using Subqueries to Select Data
  • When to Use Joins and Subqueries
  • Combining Queries with Set Operators
  • Introduction
  • Creating Tables
  • Inserting Rows into Tables
  • Updating Data Values in a Table
  • Deleting Rows
  • Altering Columns
  • Creating an Index
  • Deleting a Table
  • Using SQL Procedure Tables in SAS Software
  • Creating and Using Integrity Constraints in a Table
  • Introduction
  • Using Proc SQL Options to Create and Debug Quires
  • Improving Query Performance
  • Accessing SAS System Information Using DICTIONRY Tables
  • Using Proc SQL with the SAS Macro Facility
  • Formatting PROC SQL output Using the Report Procedure
  • Accessing a DBMS with SAS/ACCESS Software
  • Overview
  • Computing a Weighted Average
  • Comparing Tables
  • Overlaying Missing Data Values
  • Computing Percentages within Subtotals
  • Counting Duplicate Rows in a Table
  • Expanding Hierarchical Data in a Table
  • Summarizing Data in Multiple Columns
  • Creating a Summary Report
  • Creating a Customized Sort Order
  • Conditionally Updating a Table
  • Updating a Table with Values from Another Table
  • Creating and Using Macro Variables
  • SAS Macro Overview
  • SAS Macro Variables
  • Scope of Macro variables
  • Defining SAS Macros
  • Inserting Comments in Macros
  • Macros with Arguments
  • Conditional Macros
  • Macros Repeating PROC Execution
  • Macro Language
  • SAS Macro Processor
  • Types of Analytics
  • Properties of Measurements
  • Scales of Measurement
  • Types of Data
  • Measures of Central Tendency
  • Measures of Dispersion
  • Measures of Location
  • Presentation of Data
  • Skewness and Kurtosis
  • Three Approaches towards Probability
  • Concept of a Random Variable
  • Probability Mass Function
  • Probability Density Function
  • Expectation of A Random Variable
  • Probability Distributions
  • Concept of population and sample
  • Techniques of Sampling
  • Sampling Distributions
  • Concept of estimation
  • Different types of Estimation
  • Concept of hypothesis
  • Null hypothesis
  • Alternative hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Interval
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Two independent sample T test
  • Paired Sample T test
  • Chi square Test for Independence of Attributes.
  • One Way Anova
  • Two Way Anova
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Types of Clusters
  • Metric and linkage
  • Ward’s Minimum Variance Criteria
  • Semi-Partial R-Square and R-Square
  • Diagrammatic Representation of clusters
  • Problems of Cluster Analysis
  • Concept of Regression and features of Linear line.
  • Assumptions of Classical Linear Model
  • Method of Least Squares
  • Understanding the Goodness of Fit
  • Test of Significance of The Estimated Parameters
  • Multiple linear Regression with their Assumptions
  • Concept of Multocollinearity
  • Signs of Multicollinearity
  • The Idea Of Autocorrelation
  • Concept and Applications of Logistic Regression
  • Principles Behind Logistic Regression
  • Comparison between Linear probability Model and Logistic Regression
  • Mathematical Concepts related to Logistic Regression
  • Concordant Pairs, Discordant Pairs and Tied Pairs
  • Classification Table
  • Graphical Representation Related to logistic Regression.
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
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