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#### Course Overview

Software used in this program is to enriched and inspires customers round the world to transfigure data into intelligence. Statistical Analysis Software is a programming language for analyzing statistical data and data visualization. Completion of this course is perfect for the beginners and it also digs into more intermediate topics. Here in this course, you will learn three module of Statistical Analysis Software Programming I & II, SQL & Macro and PREDICTIVE MODELING.

#### Advantage

This course is the gateway of Data Science, Machine Learning and Artificial Intelligence. It will open the door to World of A.I. World wide recognized certificate for your career enrichment.

#### What you'll learn

- Programming I :Essential
- Programming II:Data Step Manipulation
- SQL
- Macro
- Statistical Concepts
- Predictive Modeling

#### Statistical Analysis Software

- Components of the Statistical Analysis Software
- Data-Driven Tasks
- Turning Data into Information

- Introducing to Statistical Analysis Software Programs
- Running Statistical Analysis Software Programs
- Mastering Fundamental Concepts
- Diagnosing and Correcting Syntax Errors

- 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 Data Sets and Creating Variables
- Conditional Processing
- Dropping and Keeping Variables
- Reading Excel Spreadsheets Containing Date Fields

- Concatenating Data Sets
- Merging Data Sets
- Combining Data Sets : Additional Features

- Introduction to Summary Reports
- Basic Summary Reports
- The Report Procedure
- The Tabulate Procedure

- Producing Bar and pie Chart
- Enhancing output
- Producing Plots

- Overview
- Review
- Review of DATA Step Processing
- Review of Displaying Data Sets
- Working with Existing Data Sets

- Outputting Multiple Observations
- Writing to Multiple 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 Statemen
- Using the DEBUG Option

- Do Loop Processing
- Array Processing
- Using Arrays

- Match-merging Two or more Data Sets
- Simple Joins Using the SQL Procedure
- Mastering Fundamental Concepts
- Diagnosing and Correcting Syntax Errors

#### Advanced Statistical Analysis Software

- What is SQL?
- What is the SQL Procedure?
- Terminology
- Comparing PROC SQL with the 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 Statistical Analysis Software
- Creating and Using Integrity Constraints in a Table

- Introduction
- Using Proc SQL Options to Create and Debug Quires
- Improving Query Performance
- Accessing System Information Using DICTIONARY Tables
- Using Proc SQL with the Macro Facility
- Formatting PROC SQL output Using the Report Procedure
- Accessing a DBMS

- 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

- Macro Overview
- Macro Variables
- Scope of Macro variables
- Defining Macros
- Inserting Comments in Macros
- Macros with Arguments
- Conditional Macros
- Macros Repeating PROC Execution
- Macro Language
- Macro Processor

#### Predictive Modelling

- Introduction and Overview
- Statistical Modeling: Types of Variables
- Overview of Models
- Explanatory versus Predictive Modeling
- Quick Review of Statistical Concepts
- Population Parameters and Sample Statistics
- Normal (Gaussian) Distribution
- Standard Error of the Mean
- Confidence Intervals
- Statistical Hypothesis Test
- p-Value: Effect Size and Sample Size Influence
- One-Sample t Tests and Scenario
- Performing a t Test

- Introduction and Overview
- Graphical Analysis of Associations
- Identifying Associations in ANOVA with Box Plots
- Identifying Associations in Linear Regression with Scatter Plots
- One-Way ANOVA
- The ANOVA Hypothesis
- Partitioning Variability in ANOVA
- Coefficient of Determination
- F Statistic and Critical Values
- The ANOVA Model
- What Does a CLASS Statement Do?
- Performing a One-Way ANOVA
- ANOVA Post Hoc Tests
- Multiple Comparison Methods
- Tukey's and Dunnett's Multiple Comparison Methods
- Diffograms and Control Plots
- Using Correlation to Measure Relationships between Continuous Variables
- Hypothesis Testing for a Correlation
- Avoiding Common Errors When Interpreting Correlations
- Fitting a Simple Linear Regression Model

- Introduction and Overview
- Two-Way ANOVA and Interactions
- Applying the Two-Way ANOVA Model
- Interactions
- STORE Statement
- Performing a Two-Way ANOVA
- Multiple Regression
- The Multiple Linear Regression Model
- Hypothesis Testing for Multiple Regression
- Multiple Linear Regression versus Simple Linear Regression
- Adjusted R-Square
- Performing Multiple Regression

- Introduction and Overview
- Approaches to Selecting Models
- The All-Possible Regressions Approach to Model Building
- The Stepwise Selection Approach to Model Building
- Interpreting p-Values and Parameter Estimates
- Optional Stepwise Selection Method Code
- Using Significance-Level Model Selection Techniques
- Information Criterion and Other Selection Options
- Information Criteria Penalty Components
- Adjusted R-Square and Mallows' Cp

- Introduction and overview
- Examining Residuals
- Assumptions for Regression
- Verifying Assumptions Using Residual Plots
- Influential Observations
- Identifying Influential Observations
- Checking for Outliers with STUDENT Residuals
- Detecting Influential Observations with DFBETAS
- Handling Influential Observations
- Generating Potential Outliers
- Exploring and visualizing Collinearity
- Using an Effective Modeling Cycle
- Assessing Collinearity

- Introduction and Overview
- Predictive Modeling Terminology
- Model Complexity
- Building a Predictive Model
- Model Assessment and Selection
- Partitioning a Data Set Using PROC GLMSELECT
- Building a Predictive Model Using PRC GLMSELECT to Partition
- Scoring Predictive Models
- Preparing for Scoring
- Methods of Scoring
- Using the SCORE Statement in PROC GLMSELECT

- Introduction and Overview
- Categorical Data Analysis
- Associations between Categorical Variables
- Tests of Association
- Inserting Comments in Macros
- The Pearson Chi-Square Test
- Odds Ratios
- The Mantel-Haenszel Chi-Square Test
- The Spearman Correlation Statistic
- Performing Tests and Measures of Association
- Introduction to Logistic Regression
- Modeling a Binary Response
- Interpreting the Odds Ratio
- Comparing Pairs to Assess the Fit of a Logistic Regression Model
- Performing a Binary Logistic Regression Analysis
- Logistic Regression with Categorical Predictors
- Specifying a Parameterization Method
- Stepwise Selection with Interactions and Predictions
- Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions

#### Admission Process

Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor

+91-9831765780

+91-9830247087

#### Who can join?

Any graduate with knowledge of basic computing.

#### Requirment

1.Personal computer/laptop with webcam and microphone

2.Stable internet connections

#### Payment details

Bank Details:

KLMS HANDS-ON SYSTEMS PRIVET LIMITED

Account Number: 19700200000420

IFSC Code: BARB0SALTLA (5th letter is numeric zero)

UPI Payment: [email protected]

##### This Course Include:

- Live Instructor-Led Course
- Project and Case Studies
- Certificate of completion
- Learn from Experts
- Placement Assistance
- Assistance for Global Certification