-25%

Advance Java J2EE Training

inteligeneit@gmail.com Tiwary

Last Update May 1, 2024

0 already enrolled

About This Course

Rate this courses

Learn SAS, R, and Python with InteliGenes Classroom and Online SAS + R + Python Training And Certification Courses In Delhi.

Analytics is the most demanding career and SAS is the most widely used tool. With this, we combine the power of Data Science with R and Machine Learning on Python for an evolving volatile market.SAS helps you in the entry while R & Python helps you grow adapt and evolve successfully.

Who Should Attend

Engineering and IT Students – BTech / BE, BCA, MCA, BSc-IT, MSc-IT

Commerce & Finance Students – BCom / MCom, Economics Graduates, MBA or BBA

Highly recommended for people aspiring for jobs that require data handling – Research, Marketing, IT Services, Big Data & more

People who are already employed, but want to upskill themselves in the domain of Analytics

As a prerequisite, it’s just your passion for data and hard work that is the only requirement, the rest is just a cakewalk.

Course Outcome

Understanding of basic concepts and types of data
Understanding of sampling techniques
Data Science and Machine Learning concepts with application
Understanding of frequency distributions and measures of central tendency, dispersion and shape
In-Depth Knowledge of the Hypothesis testing T-Test ANOVA
In-depth knowledge of Correlation and Regression
In-depth Knowledge of Predictive modeling using Logistic Regression
Two live projects which are full hands-on real-time industrial data

Curriculum

Basic Concepts

Introduction to SAS tool
SAS Libraries /Temporary Library/ Permanent Library
Creating Libraries
Start with a Basic SAS programs
Data Step / Proc Step / Statements/ Global statements
Variables / Datatypes / properties of Variables

Access Data

INFILE statement options to read raw data files
Creating a file refrence with filename statement
DATALINES statement with an INPUT statement

Starting With Raw Data(Basics)

Styles of Input
Reading Unaligned Data / Understanding List Input
Understanding Column Input / Reading Data Aligned in Columns

Formats and Informatics

Standard Data/Non-Standard Data
How Informatics and Format works
Working with Date/Time/DateTime informal
How and when to use Yearcutoff

Starting With Raw Data( Beyond Basics)

Formatted Input style
Using Modifiers

Mixing Styles of Input

Testing a Condition before Creating an Observation
Creating Multiple Observations from a Single Record
Reading Multiple Records to Create a Single Observation

PDV: How the DATA Step Works

Writing Basic Data Step
How SAS Processes Programs
Compilation phase
Execution Phase
Debugging a Data Step
Testing SAS Programs

Manipulating SAS Datasets

Creating & Modifying Variables
Assigning Values Conditionally
Specifying Lengths for Variables
Subsetting Data
Assigning Permanent Labels and Formats

Grouping Statements Using DO Groups

Assigning Values Conditionally Using SELECT Groups
Reading a Single Data Set
Manipulating Data
Using BY-Group Processing
Reading Observations Using Direct Access (Point= option)
Detecting the End of a Data Set(end= option)
Understanding How Data Sets Are Read through PDV
Renaming Variables
Selecting Variables

Combining SAS Data Sets

One-to-One Reading
Concatenating
Interleaving
Match-Merging
Match-Merge Processing
Excluding Unmatched Observations

Transforming Data with SAS Functions

General Form of SAS Functions
Converting Data with Functions
Restriction for WHERE Expressions
Manipulating SAS Date Values with Functions
SAS Date and Time Values
SAS Date Functions
Modifying Character Values with Functions
Modifying Numeric Values with Functions
Nesting SAS Functions

Relevant base SAS procedures

Append procedure
Sort procedure
Datasets procedure
Print to procedure
Format procedure
Transpose procedure
Import procedure
Export procedure
Print procedure
Tabulate procedure
Report procedure
Means procedure
Summary procedure
Freq procedure

Generating Data with DO Loops

Constructing DO Loops
Introduction to Constructing DO Loops
DO Loop Execution
Counting Iterations of DO Loops
Decrementing DO Loops
Nesting DO Loops
Iteratively Processing Data That Is Read from a Data Set
Conditionally Executing DO Loops
Using Conditional Clauses with the Iterative DO Statement
Creating Samples

Processing Variables with Arrays

Creating One-Dimensional Arrays
Understanding SAS Arrays
Defining an Array
Variable Lists as Array Elements
Referencing Elements of an Array
Compilation and Execution
Using the DIM Function in an Iterative DO Statement
Creating Variables in an ARRAY Statement
Creating Temporary Array Elements

Analytics-An Introduction

What is Business Analytics
Difference between Analytics and Analysis
Importance of Analytics in Industry
Application of Analytics in Industry
Learning and the growth curve of Analytics
Puzzle
Interview Preparation

Data and Variables-An introduction

What is Statististics
What is an Average- Mean Median Mode
Different types of Data and variables
Basic Statistical Measures
Puzzle
Quiz
Coding-An Introduction
Dependent and Independent Variable

Population and Sample-Sampling techniques

Why do we need sample over a population
Difference between population and sample
Sampling Techniques
Simple Random Sampling
Stratified Random Sampling
Sampling with and without replacement
Puzzle
Assignment

Normal Distribution & Central Limit Theorem

Introduction to Bell Curve
Deviation Vs Standard Deviation
Variance and Standard deviation
Outliers and their effects on basic statistical measures
Symmetric and Asymmetric curve
Bell Curve-Emperical Rule–Grading process
Quiz

Exploratory data Analysis

Univariate anlysis vs Bivariate analysis Vs Multivariate analysis
Decile, Percentile,Vintile,Quartile,Quantile
Proc Univarite- Details
Moments , Basis Statistical Measures, Test for Location, Quantiles , Extreme Observation
Assignment
Puzzle

Data Visualization

Box and whiskers plot
Ranking Algorithm-Proc Rank in detail
Outlier- Detection Removal Treatment
Missing values- How critical are they to a data
Missing value Removal and imputation
Proc for missing value Treatment

Hypothesis Testing

Hypothests testing – What does that mean
What is Hypothesis and what we do we test
Null Hypothesis and Alternate Hypothesis
Significanace level and Confidence level- P value and α value
Decision making- Reject/Fail to Reject Null Hypothesis
Confidence Interval
Accuracy and Error
Type I Error and Type II Error

Campaign Management

Test and Control group
Solicit and Non Solicit
Responder and Non Responder
Targets and Non Targets
Measuring Cost/Revenue/Profit of a campaign
2*2 profit/revenue contingency matrixActual Vs predicted conflicts
Accuracy Error Sensitivity Specificity
Puzzle
Interview preparation

One Sample T-Test

One Sample T-test- What is the hypothsis for this test
One Sample T Test using proc ttest
One Sample T Test using proc univariate
Sides of a test- One sample- two sided, Upper tail, lower tail
How to apply T-test to the data
How will this help in making a decision
Graphical interpretation of T test
Puzzle
Assignment
Assumptions to a 1 sample T Test

Two sample T-tests & Sides of a test

Difference between a one-sample and a two-sample T-Test
Hypothesis for a T Test
Proc T-Test for 2 sample T Test
Understanding the results from the statistical point of view
Plotting the graph for visualizing the T-Test
Sides of 2 sample T Test-2 sided, Lower tail and upper tail
Application of 2 Sample T Test
Puzzle
Quiz
Assumptions to 2 sample T-Test

ANOVA-Analysis of Variance

Why do we need Anova
Why can’t we use a 3 Sample T Test
Hypothesis Testing for Anova
Assumptions to Anova
1 way Anova vs N Way Anova
Performing 2 sample T test using Anova
Coefficient of determination
Degree of Freedom
Levenes Test and F test
Interaction – Type 1 and Type III SS
Balanced Vs Unbalanced design
Proc Anova
Proc GLM
Puzzle
Interview Preparation

Anova-Post Hoc Analysis Test

Why apply Post-hoc Test when applying Anova
Controlled Design experiment
Experimental Error Rate
Multiple Comparison Test
Referential comparison
Tukey and Dunnett Test- Sides of a test
Diffogram and Control Plot
Means Vs LS Means
Puzzle
Mock Test

Correlation-Different types

What is Correlation
Why do need correlation analysis in any other analysis
How to measure correlation
Pearson and Spearman Correlation
Correlation-Hypothesis Test
Proc Corr
Correlation Matrix
Correlation-graphical representation and Interpretation
Puzzle
Case Study

Regression -Exploring the algorithm

Simple Linear Regression and Multiple Linear Regression
Regression-Hypothesis Testing
Degree of Freedom
Anova table for Regression
What is the Ordinary least square
Parameter Estimate and Intercept
Significant and Non Significant Variables
Removing Redundancy
Collinearity- VIF
Puzzle
Assignment-Discussion

Regression – Model Building

Regression- Model Building
R square and adjusted R square
2-way honest assessment
3-way honest assessment
How to split Training Validation Test
Oversampling Undersampling Overfitting Underfitting
Model Selection techniques
variable selection techniques
Model building Vs Model Fitting
Model fit statistics
Puzzle
Mock Test

Logistic Regression-Introduction

Logistic Regression-An Introduction
Logistic Regression-Need and the necessity
Why cant we apply regression everywhere
Algorithm to Logistic Regression
Asssumption to Logistic Regression
Checking the linearity amongst variables
Checking for collinearity
Removing Non Linearity
Removing Non Linearity
Logistic Regression- Hypothesis testing

Predictive modeling using Logistic Regression

What are the odds ratio
Odds ratio vs probability
Log odds
log Vs Natural Log
Complete separation Vs Quasi Complete Separation
Data Convergence
Fischer’s Technique
Creating and identifying the dependent variable
Data preparation for the model building process

Logistic Regression-Model Building

Sampling data for Training Validation Test
Fine-tuning Assessment and final assessment
Out-of-sample validation
Out-of-time validation
Variable transformation
Variable reduction techniques
Variable clusting techniques
Identifying Collinearity amongst variables
Interpretation of Results

Logistic Regression-Measure model performance

Cumulative Lift Chart
Cumulative Gain Chart
Relative operating characteristics
Area under the curve
Model fit statistics
validation statistics
Variable selection techniques
Significant and nonsignificant variables
Identifying the best variables for a model

Logistic Regression-Industry view and applications

Model selection techniques
Parameter estimate and Intercept
% Concordant, %Discordant, %ties pairs
Calculatig C value from the statistics
Puzzle
Interview Preparation
Case study
Comparing training and validation statistics

SAS SQL 1: Essentials

Introducing the Structured Query Language
Overview of the SQL procedure
Specifying columns
Specifying rows
Displaying Query Results
Presenting data
Summarizing data

SQL Joins

Introduction to SQL joins
Inner joins
Outer joins
Complex SQL joins

Subqueries

Noncorrelated subqueries
In-line views

Set Operators

Introduction to set operators
Union operator
Outer Union operator
Except operator
Intersect operator

Creating Tables and Views

Creating tables with the SQL procedure
Creating views with the SQL procedure

Advanced PROC SQL Features

Dictionary tables and views
Using SQL procedure options
Interfacing PROC SQL with the macro language

SAS Macro Language

Introduction
Getting Familiar to the macro facility

Macro Variables

Introduction to macro variables
Automatic macro variables
Macro variable references
User-defined macro variables
Delimiting macro variable references
Macro functions

Macro Definitions

Defining and calling a macro
Macro parameters

DATA Step and SQL Interfaces

Creating macro variables in the DATA step
Indirect references to macro variables
Creating macro variables in SQL

Macro Programs

Conditional processing
Parameter validation
Iterative processing
Global and local symbol tables

Advanced SAS Programming Techniques

Measuring Efficiencies

Controlling I/O Processing and Memory

SAS Data step processing
Controlling I/O
Using SAS views
Reducing the length of numeric variables
Compressing SAS data sets

Accessing Observations

Creating a sample data set
Creating an index
Using an index

Using DATA Step Arrays

Introduction to lookup techniques
Using one-dimensional arrays
Using multidimensional arrays
Loading a multidimensional array from a SAS data set

Using DATA Step Hash and Hiter Objects

Introduction
Using hash object methods
Loading a hash object with data from a SAS data set
Using the DATA step hiter object

Combining Data Horizontally

DATA step merges and SQL procedure joins

Using an index to combine data

Combining summary and detail data

Combining data conditionally
Expert Programmer Techniques

Creating user-defined functions

The experts’ FORMAT procedure
Understanding the R environment:

Setting up the machine and installing R

Setting up the R environment for the smooth usage of R

Understanding the various IDEs for R development

Installation/Removal of packages
Importing raw data

Reading csv files into R

Reading json files into R

Reading txt files into R

Reading sas7bdat files into R
Data structures

Understanding homogenous and hetrogenous form atomic vectors in R including Dataframes, List, Vectors, Factors and Matrices in R

Atomic vectors in R

Dataframes, List, Vectors

Factors and Matrices in R
Style Guide

Coding standards-Know How

Notation and Naming

Filenames

Object names

Syntax

Curly Braces

Spacing

Line length

Indentation

Assignment

Commenting Guidelines
Loops and vectorization

Writing For and while loops in R

Understanding if loops are really a necessary in R

Understand the apply family in R
Functions and conditionals

Writing functions in R

Understanding if…then…else in R

Undersanding pure functions in R and understanding the purrr package in R
summarization

Understand the meaning of clustering

Hierarchial clustering in R

K means clustering in R
GLM Family

Linear regression in R

Multicollinearity and calcualtion of VIF

Root mean squared error, t statistic, pvalue and confidence interval

Logistic regression in R

RoC, TPR, Lift, Gain and KS statistic in R

Interpreting the Linear and logistic Model

Understanding ridge and lasso in linear and logistic model
Decision Trees

Decision Trees in R

Understanding Ginni Index

Knowing the difference between CART and CHAID

Working with Random Forest
Optimization in R

What is optimization?

Working with opimiztion packages in R (e.g Optimx)

Constrained optimisation ( Lagrange multipliers )
Bonus Topic

Basics of GGPLOT2

Scatterplots in R

Histograms and density plots in R

Understanding basics of “Grammer of Graphics” concept
Introduction to ML

What is Learning

Components of learning

A simple learning model

Types of Learning

What is machine learning

Application of ML
Getting started with Python

What is Python

Origins and versions of Python

Ways to run a Python program

Setting up Python environment

Basic file operations with Python

Basic data operations with Python

Basic data visualization with Python

Hands on with Python
Process in any ML

Basic process flow of any machine learning

Terminologies used in ML

Evaluation metrics used in ML

Comparison of ML and Statistical learning
Regression

Simple Linear Regression

Multiple Linear Regression

Consideration in Regression

Accessing the accuracy of Coefficient estimates

Accessing the accuracy of Model

Fun example and workshop!
Classification

An overview of classification

Why not linear regression

Logistic Model

Linear discriminant Analysis

Comparing classification

Fun example and workshop!
Support Vector machines

Introduction

General concepts

Components of SVM

Relationship with Logistic regression

Fun example and workshop!
Decision Trees

Introduction

Basics of a tree

Classification with trees

Advantages / disadvantages of tree
Random Forest

Introduction

How Random forest works

Different parameters in RF

Fun example and workshop!
Gradient boosting machines

Introduction

How GBM works

Different parameters in GBM

Fun example and workshop!
Clustering

Introduction

K- Means clustering

Hierarchical Clustering

Practical issues in clustering
Natural Language processing

General text processing

Tokenizing

Stop word

Stemming

POS tagging

Chunking/chinking

Lemmatizing

Corpora

Building text only models

Fun example and workshop!
Neural Network

Introduction

Important concepts in NN

Building NN for classification

Fun example and workshop!
Solving live problems using what we have learned

Building a recommender System

Building a sales prediction model

Retrieving twitter feeds and sentiment analysis

Your Instructors

inteligeneit@gmail.com Tiwary

0/5

68 Courses

0 Reviews

0 Students

₹14,999.00~~₹19,999.00~~

25% off

Level

Intermediate

Subject

Development

-14%

Diploma In JAVA

₹18,999.00~~₹21,999.00~~

-17%

Android Development Course

₹14,999.00~~₹17,999.00~~

-14%

Angular JS Training

₹11,999.00~~₹13,999.00~~

Advance Java J2EE Training

About This Course

Course Outcome

Curriculum

Basic Concepts

Access Data

Starting With Raw Data(Basics)

Formats and Informatics

Starting With Raw Data( Beyond Basics)

Mixing Styles of Input

PDV: How the DATA Step Works

Manipulating SAS Datasets

Grouping Statements Using DO Groups

Combining SAS Data Sets

Transforming Data with SAS Functions

Relevant base SAS procedures

Generating Data with DO Loops

Processing Variables with Arrays

Analytics-An Introduction

Data and Variables-An introduction

Population and Sample-Sampling techniques

Normal Distribution & Central Limit Theorem

Exploratory data Analysis

Data Visualization

Hypothesis Testing

Campaign Management

One Sample T-Test

Two sample T-tests & Sides of a test

ANOVA-Analysis of Variance

Anova-Post Hoc Analysis Test

Correlation-Different types

Regression -Exploring the algorithm

Regression – Model Building

Logistic Regression-Introduction

Predictive modeling using Logistic Regression

Logistic Regression-Model Building

Logistic Regression-Measure model performance

Logistic Regression-Industry view and applications

SAS SQL 1: Essentials

SQL Joins

Subqueries

Set Operators

Creating Tables and Views

Advanced PROC SQL Features

SAS Macro Language

Macro Variables

Macro Definitions

DATA Step and SQL Interfaces

Macro Programs

Advanced SAS Programming Techniques

Controlling I/O Processing and Memory

Accessing Observations

Using DATA Step Arrays

Using DATA Step Hash and Hiter Objects

Your Instructors

inteligeneit@gmail.com Tiwary

Diploma In JAVA

Android Development Course

Angular JS Training

About

Courses

Support

Login