## Data Science using SAS

## Welcome to Handson!

## Data Science using 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.

- Hands-On Capstone project
- For the industry by the industry
- Case studies
- Cutting edge curriculum

- 4 industry-based projects
- Lifetime access to self-paced learning

- Hands-On Capstone project
- Case studies

- For the industry by the industry
- Cutting edge curriculum

### Course Fees:Rs.30000

- Duration : 4 months
**No Cost EMI :**INR 7500 x 4 month

NEFT Payment :Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

Support :9830247087

## Course Description!

## Data Science using SAS Course Curriculum

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

## Call Me Back or Download Syllabus

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- Counselling based on your skills and preferrence
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### +91 9830247087

## 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

## Why to choose Handson?

### Hands-On Training

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