# Category: DATA SCIENCE

## Data Science Training

Proerp Academy is a leading E-Learning Platform both Online and Instructor-led training(Offline).We have trained the students and professionals across the globe in different technologies like  ,Data Science,SAP, Big Data Hadoop,Amazon Webservices(AWS), Business Intelligence and Analytics,Big Data Analytics, Data Science Analytics and Digital Maketing.

We have efficient and affordable learning solutions that is accessible to the millions of interested students and professionals across the globe.

Proerp Academy’s Data Science Online and Offline training will train the Students and Professionals  how to use  Statistical Analysis System(SAS),R-Programming,Regression Analysis,Decision Trees and Model Selection,Predictive Modeling ,Neural Network,Machine Learning,Python Basic Statistics .

This Data Science training also helps the Students and Professionals to upgrade the skills to handle R-Programming,Machine Learning and Python Statistics for reporting Activities .

This Course provides the skills and knowledge of experienced Data Science Programmers Experts.

This Course will provide you the necessary skills to implement Data Science Programming in the Industry.

The Course provides you the overview of Data Science Programming.

Training Objectives:

• Statistical Analysis System(SAS)
• Statistical Theorey
• R-Programming,Data Handling and Basic Statistics
• Basic Descriptive Statistics
• Reporting and Data Validation
• Regression Analysis and Logistics Regression Model Building
• Decision Trees and Model Selection
• Predictive Modeling
• Neural Network,SVM and Random Forest
• SVM Algorithm
• Machine Learning
• Introduction to Python
• Data Handling in Python
• Python Basic Statistics
• Python Data Handling
• Logistics Regression
• Decision Trees
• Model Selection and Cross Validation
• Python Machine Learning

Module 1

Statistical Analysis System(SAS)

Goal:

In this module,You will learn about Statistical Analysis System(SAS).

Objective:

After completing this module 1,you should be able to:

STATISTICAL ANALYSIS SYSTTEM (SAS)

• Understand the introduction to SAS
• Understand the Types of Libraries and Variables
• Understand how to Read,Write,Import and Export Data
• Understand Functions and Options
• Understand Conditional Statements and Logical Operators
• Understand how to Append,Merge and Sort Datasets
• Understand Report Generation ,Data set Manipulation
• Understand Databases,RDBMS Concepts
• Understand Structured Query Language(SQL)

Topics:

• Introduction to SAS
• Types of Libraries and Variables
• Functions and Options
• Conditional Statements and Logical Operators
• Appending,Merging and Sorting Datasets
• Report Generation ,Data set Manipulation
• Databases,RDBMS Concepts
• Structured Query Language(SQL)

Hands on:

• Introduction to SAS
• Different Types of Libraries and Variables
• How to Read,Write,Import and Export Data
• How to use Functions and Options
• How to work with Conditional Statements and Logical Operators
• How to Append,Merge and Sort Datasets
• Report Generation ,Data set Manipulation
• Databases,RDBMS Concepts
• Structured Query Language(SQL) Concept

Module 2

Statistics Theorey

Goal:

In this module,You will learn about Statistics Theorey.

Objective:

After completing this module 2,you should be able to:

• Understand the introduction to Statistics
• Understand Graphical and Tabular Descriptive Statistics
• Understand Probability
• Understand Probability Distribution
• Understand Hypothesis Testing
• Understand different Statistical Tests like Z-Test,Chi-Square Test,T-Tests

Topics:

• Introduction to Statistics
• Graphical and Tabular Descriptive Statistics
• Probability
• Probability Distribution
• Hypothesis Testing
• Different Statistical Tests like Z-Test,Chi-Square Test,T-Tests

Hands on:

• Graphical and Tabular Descriptive Statistics
• Probability Concept
• Probability Distribution Concepts
• Hypothesis Testing Concept
• Different Statistical Tests like Z-Test,Chi-Square Test,T-Tests

Module 3

R Programming

Goal:

In this module,you will learn about R Programming.

Objective:

After completing this module 3,you should be able to:

• Understand the introduction to Data Analysis
• Understand the introduction to R Programming
• Understand R Environment and Basic Commands

Topics:

• Introduction to Data Analysis
• Introduction to R Programming
• R Environment and Basic Commands

Hands on:

• Introduction to Data Analysis
• Introduction to R Programming
• R Environment and Basic Commands

Module 4

Data Handling in R

Goal:

In this module,you will learn about Data Handling in R.

Objective:

After completing this module 4,you should be able to:

• Understand how to Import Data
• Understand Sampling
• Understand Data Exploration
• Understand how to Create Calculated Fields
• Understand how to Sort and Remove Duplicates

Topics:

• Importing Data
• Sampling
• Data Exploration
• Creating Calculated Fields
• Sorting and Removing Duplicates

Hands on:

• How to Import Data
• Sampling
• Data Exploration
• Creation of Calculated Fields
• How to Sort and Remove Duplicates

Module 5

Basic Descriptive Statistics

Goal:

In this module,You will learn about the Basic Descriptive Statistics.

Objective:

After completing this module 5,you should be able to:

• Understand Population and Sample
• Understand Measures of Central Tendency
• Understand Measures of Dispersion

Topics:

• Population and Sample
• Measures of Central Tendency
• Measures of Dispersion

Hands on:

• Population and Sample Concepts
• Measures of Central Tendency Concept
• Measures of Dispersion Concept

Module 6

Reporting and Data Validation

Goal:

In this module,You will learn about Reporting and Data Validation .

Objective:

After completing this module 6,you should be able to:

• Understand Percentiles and Quartiles
• Understand Box Plots and Outlier Detection
• Understand how to Create Graphs and Reporting

Topics:

• Percentiles and Quartiles
• Box Plots and Outlier Detection
• Creating Graphs and Reporting

Hands on:

• Percentiles and Quartiles Concepts
• Box Plots and Outlier Detection Concepts
• Creation of Graphs and Reporting

Module 7

Regression Analysis

Goal:

In this module,You will learn about Regression Analysis.

Objective:

After completing this module 7,you should be able to:

• Understand Correlation
• Understand Simple Regression Models
• Understand R-Square
• Understand Multiple Regression
• Understand Multicollinearity
• Understand Individual Variable Impact

Topics:

• Correlation
• Simple Regression Models
• R-Square
• Multiple Regression
• Multicollinearity
• Individual Variable Impact

Hands on:

• Correlation Concept
• Simple Regression Models Concept
• R-Square Concept
• Multiple Regression Concept
• Multicollinearity Concept
• Individual Variable Impact

Module 8

Logistic Regression

Goal:

In this module,You will learn about Logistic Regression.

Objective:

After completing this module 8,you should be able to:

• Understand the need of Logistic Regression
• Understand Logistic Regression Models
• Understand the validation of Logistic Regression Models
• Understand Multicollinearity in Logistic Regression
• Understand the individual impact of Variables
• Understand Confusion Matrix

Topics:

• Need of Logistic Regression
• Logistic Regression Models
• Validation of Logistic Regression Models
• Multicollinearity in Logistic Regression
• Individual impact of Variables
• Confusion Matrix

Hands on:

• Different Logistic Regression Models
• How to Validate Logistic Regression Models
• Multicollinearity in Logistic Regression
• Individual impact of Variables
• Confusion Matrix Concept

Module 9

Decision Trees

Goal:

In this module,You will learn about Decision Trees.

Objective:

After completing this module 9,you should be able to:

• Understand Segmentation
• Understand Entropy
• Understand how to Build Decision Trees
• Understand how to Validate Trees
• Understand how to Fine Tune and Predict using Trees

Topics:

• Segmentation
• Entropy
• Building Decision Trees
• Validating Trees
• Fine Tuning and Prediction using Trees

Hands on:

• Segmentation Concept
• Entropy Concept
• How to Build Decision Trees
• How to Validate Trees
• Fine Tuning and Prediction using Trees

Module 10

Model Selection and Cross Validation

Goal:

In this module,You will learn about Model Selection and Cross Validation.

Objective:

After completing this module 10,you should be able to:

• Understand how to Validate a Model
• Understand what is a Best Model
• Understand different types of Data
• Understand different types of Errors
• Understand the problem of Over Fitting
• Understand the problem of Under Fitting
• Understand Cross Validation
• Understand Boot Strapping

Topics:

• Validating a Model
• Best Model
• Types of Data
• Types of Errors
• Over Fitting
• Under Fitting
• Cross Validation
• Boot Strapping

Hands on:

• How to Validate a Model
• Best Model Selection
• Different Types of Data
• Different Types of Errors
• Over Fitting Problem
• Under Fitting Problem
• Cross Validation Concept
• Boot Strapping Concept

Module 11

Neural Network

Goal:

In this module,You will learn about Neural Network.

Objective:

After completing this module 11,you should be able to:

• Understand Neural Networks
• Understand Neural Network Intuition
• Understand Neural Network and Vocabulary
• Understand Neural Network Algorithm
• Understand the Math behind Neural Network Algorithm
• Understand how to Build the Neural Networks
• Understand how to Validate the neural network Model
• Understand Neural Network Applications
• Understand Image Recognition using neural networks

Topics:

• Neural Networks
• Neural Network Intuition
• Neural Network and Vocabulary
• Neural Network Algorithm
• Math behind Neural Network Algorithm
• Building the Neural Networks
• Validating the neural network Model
• Neural Network Applications
• Image Recognition using neural networks

Hands on:

• Neural Networks Concept
• Neural Network Intuition
• Neural Network and Vocabulary Concepts
• Neural Network Algorithm
• Math behind Neural Network Algorithm Concept
• How to Build the Neural Networks
• How to Validate the neural network Model
• Neural Network Applications
• Image Recognition using neural networks

Module 12

SVM

Goal:

In this module,You will learn about SVM.

Objective:

After completing this module 12,you should be able to:

• Understand the introduction to SVM
• Understand the Decision Boundary with Largest Margin
• Understand SVM-The Large Margin Classifier
• Understand SVM Algorithm
• Understand the Kernel Trick
• Understand how to Build SVM Model

Topics:

• Introduction to SVM
• Decision Boundary with Largest Margin
• SVM-The Large Margin Classifier
• SVM Algorithm
• Kernel Trick
• Building SVM Model

Hands on:

• Decision Boundary with Largest Margin Concepts
• SVM-The Large Margin Classifier
• Usage of SVM Algorithm
• Kernel Trick
• How to Build SVM Model

Module 13

Random Forest and Boosting

Goal:

In this module,You will learn about Random Forest and Boosting.

Objective:

After completing this module 13,you should be able to:

• Understand the introduction to Random Forest and Boosting
• Understand the Decision Boundary with Largest Margin
• Understand SVM-The Large Margin Classifier
• Understand SVM Algorithm
• Understand the Kernel Trick
• Understand how to Build SVM Model

Topics:

• Introduction to Random Forest and Boosting
• Decision Boundary with Largest Margin
• SVM-The Large Margin Classifier
• SVM Algorithm
• Kernel Trick
• Building SVM Model

Hands on:

• Decision Boundary with Largest Margin Concepts
• SVM-The Large Margin Classifier
• Usage of SVM Algorithm
• Kernel Trick
• How to Build SVM Model

Module 14

Introduction to Python

Goal:

In this module,You will learn about the introduction to Python.

Objective:

After completing this module 14,you should be able to:

• Understand the history of Python
• Understand how to install Python
• Understand the basic commands in Python
• Understand Data Types and Operations
• Understand Python Packages
• Understand Loops
• Understand how to write a Python Program
• Understand how to use If-then-Else Statement

Topics:

• History of Python
• Installing Python
• Basic commands in Python
• Data Types and Operations
• Python Packages
• Loops
• Writing a Python Program
• If-then-Else Statement

Hands on:

• History of Python Concept
• How to Install Python
• How to use Basic commands in Python
• Different Data Types and Operations
• Python Packages
• How to use Loops
• How to Write a Python Program
• How to use If-then-Else Statement

Module 15

Data Handling in Python

Goal:

In this module,You will learn about Data Handling in Python.

Objective:

After completing this module 15,you should be able to:

• Understand how to Import Data
• Understand how to Work with Datasets
• Understand how to Manipulate Datasets
• Understand how to Create new Variables
• Understand how to Export the Datasets into External Files
• Understand how to Perform Data Merging

Topics:

• Data Importing
• Working with Datasets
• Manipulating Datasets
• Creating new Variables
• Exporting the Datasets into External Files
• Data Merging

Hands on:

• How to Import Data
• How to Work with Datasets
• How to Manipulate Datasets
• Creation of new Variables
• How to Export the Datasets into External Files
• How to Perform Data Merging

Module 16

Python Basic Statistics

Goal:

In this module,You will learn about Python Basic Statistics.

Objective:

After completing this module 16,you should be able to:

• Understand how to take a random sample from data
• Understand Descriptive Statistics
• Understand Central Tendency
• Understand Variance,Quartiles ,Percentiles
• Understand Box Plots
• Understand Graphs

Topics:

• Taking a random sample from data
• Descriptive Statistics
• Central Tendency
• Variance,Quartiles ,Percentiles
• Box Plots
• Graphs

Hands on:

• Taking a random sample from data
• Descriptive Statistics Concept
• Central Tendency Concept
• Usage of Variance,Quartiles ,Percentiles
• Usage of Box Plots
• Usage of Graph