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Data Analytics with SAS+R+Python

inteligeneit@gmail.com Tiwary

Last Update September 6, 2022

0 already enrolled

About This Course

Course Overview

The course is designed in a manner that covers all parameters of basic and advanced learning in data analytics as well as enhances essential skills with respect to data analytics by using python.

The Data Analytic course includes analysis of data, visualization, R or python programming, machine learning, SQL, etc.

Data analytical experts use this skill for programming applications which would help in modifying or analyzing the different data which is stored.

The course would be facilitated by the best teachers of InteliGenes who have much experience in this field. The course involves high expertise in learning concepts in data alteration efficiently.

Key Highlights

This is the good time that makes your learning effective and enhance your essential learning aspects with respect to modernization.
The course is fully flexible having different time schedules like morning classes, afternoon classes, and evening classes that would be chosen by the students.
The course fee is very cheap which would grab by any class of students who keen to walk with future.
The city makes you much more energetic and marvelous in learning Data Analytics with Python programming as Delhi does.
The academy has a strong passion for providing you best and most effective classes in Data Analytics that would make your learning effective.

Course Advantage

The Data Analytic course includes all parts of learning which are required with respect to modern parameters where you learn and develop your skills.

Upon Completion, Of course, you will receive,

Course Completion Certificate from a top institute, InteliGenes Technologies.
Get a membership at InteliGenes Technologies.
Virtual Internship certificate in Data analytics as well as data analytics with python.

Course Details

The course includes various resources for learning Data Analytics with Python like ebooks and other programming books that would benefit your learning.

Along with this facility, the students will get online materials like video classes, advanced learning, practical learning, and other aspects of learning. Under the course, there is various assessment held which will check your progress and enhancement in data analytics.

The trainers of the academy will help you in providing the best and most effective classes in different programming parameters like MS office, machine learning, SQL, visualization, or analyzing the data.

Skills Covered

Data Analysis expert
Visualization skill
Python with Analytics expert
Machine learner
Statistical Programming
PPT, and other office skills.

Why Data Analytic course?

Enhancing skills for better future prospective

Having realistic and modern learning data analytics, the students will learn as well as enhance relevant skills throughout the course.

Learning from highly expertise Faculty

The students will have the best teachers who have enough experience in the field of data Analytics with Python and who would use different teaching parameters.

Better guidance never stops learning

With a dynamic mindset and different perceptive skills, the students will have the best instructions from them and would forward your learning in the future.

Proficiency in Data Analytic

Proficiency in the sense of having knowledge in Data Analytics which you would learn during the course and rapid your learning.

Program Benefits

Throughout the course, you will have various career options.

Complete this course while you are at work.
The course helps in improving your life standard.
Learning from highly expert teachers.
Masterclass and top tips in Data Analytics.

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 required 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 reference with filename statement

DATA LINES 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 informat

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

Printto 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 matrix

Actual 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-test & 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 visulizing 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 cant 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 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 odds ratio

Odds ratio vs probability

Log odds

log Vs natural Log

Complete seperation Vs Quasi Complete Seperation

Data Convergence

Fischers Technique

Creating and identifying the dependent variable

Data preparation for 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 non significant 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