## Data Science with SAS

## Welcome to Handson!

## 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.

- 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

### Find the best course for you !

Choose the right course is a decision that you should make wisely. Launch your career with a course closely related to your career outcomes.

## 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.

## Call Me Back or Download Syllabus

- Non-biased career guidance
- Counselling based on your skills and preferrence
- No repetitive calls, only as per convenience

- Need Assistance?

### +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

Using a hands-on approach to training, participants will adopt the form of learning that typically benefit from the most. It allows people to learn by doing.

### Live Instructor-led training

Removes travel expenses, Save money – No hotels, no rental cars, or meal costs, Save time – Flexible schedule allows you to stay in touch with the plant, Easy to use virtual classroom, Each class is recorded, review as much as you like.

### Handson for Business

I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet

### Self-Paced Training

This comprehensive collection gives your professional development. Access to our self faced training courses with Gamifying learning approach and resources that will enable, educate, prepare, and empower with very reasonable fees.

### Join the Virtual Internship

Handson virtual internship program includes a 4-6 week program on different subject areas for 3^{rd} year/final year’s students to gain guaranteed internship experience. Gain Hands-on experience which will help you crack your campus/off-campus interviews.

### Handson PlaceMentor

Handson^{TM} PlaceMentor is a dedicated online mentor to which will help you to reach your professional goal.