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Data Science Course prepares you for the Data Science Certification exam and for the role of Data Scientist by making you an expert in Statistics, Analytics, Data Science, Big Data, AI, Machine Learning and Deep Learning. The Data science master program has been designed by the industry expert to help you master data mining, management, exploration, and carry out several industry-relevant projects. Enroll & Get Certified now!

- ✔ Course Duration : 56 hrs
- ✔ Training Options : Live Online / Self-Paced / Classroom
- ✔ Certification Pass : Guaranteed

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Data science is a “concept to unify statistics, data analysis, machine learning & their related methods” in order to “understand & analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization. Data Science Turing award winner Jim Gray imagined data science as a ” fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that ” everything about science is changing because of the impact of information technology” and the data deluge.

Data science course program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces. You will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems. These skills will help you prepare for the role of a Data Scientist.

Following are some prerequisites to learn Anaplan:

1. Fundamentals of Python programming

2. Basic knowledge of statistics

If you don’t know about python, statistics, machine learning etc. don’t worry we will provide training for that to you so, that you can analyze and statistical techniques, it will take hardly 15-20 hours.

Learning Objectives:
Analytics Overview
Introduction

Introduction to Business Analytics

Types of Analytics

Areas of Analytics

Analytical Tools

Analytical Techniques
Introduction to SAS
Introduction

What is SAS

Navigating in the SAS Console

SAS Language Input Files

DATA Step

PROC Step and DATA Step

DATA Step Processing

SAS Libraries

Importing Data

Exporting Data
Combining and Modifying Datasets
Introduction

Why Combine or Modify Data

Concatenating Datasets

Interleaving Method

One – to – one Reading

Data Manipulation

Modifying Variable Attributes
PROC SQL
Introduction

What is PROC SQL

Retrieving Data from a Table

Selecting Columns in a Table

Retrieving Data from Multiple Tables

Selecting Data from Multiple Tables

Concatenating Query Results

Activity
SAS Macros
Introduction

Need for SAS Macros

Macro Functions

Macro Functions Examples

SQL Clauses for Macros

The % Macro Statement

The Conditional Statement
Basics of Statistics
Introduction to Statistics

Statistical Terms

Procedures in SAS for Descriptive Statistics

Descriptive Statistics

Hypothesis Testing

Variable Types

Hypothesis Testing

Process

Parametric and Non – parametric Tests

Parametric Tests

Non – parametric Tests

Parametric Tests – Advantages and Disadvantages
Statistical Procedures
Introduction o Statistical Procedures

PROC Means

PROC FREQ

PROC UNIVARIATE

PROC CORR

PROC CORR Options

PROC REG

PROC REG Options

PROC ANOVA
Data Exploration
Introduction

Data Preparation

General Comments and Observations on Data Cleaning

Data Type Conversion

Character Functions

SCAN Function

Date/Time Functions

Missing Value Treatment

Various Functions to Handle Missing Value

Data Summarization
Advanced Statistics
Introduction

Introduction to Cluster

Clustering Methodologies

K Means Clustering

Decision Tree

Regression

Logistic Regression
Working with Time Series Data
Introduction

Need for Time Series Analysis

Time Series Analysis — Options

Reading Date and DDateTimeValues

White Noise Process

Stationarity of a Time Series

Stages of ARIMA Modelling

Transform Transpose and Interpolating Time Series Data
Designing Optimization Models
Introduction

Need for Optimization

Optimization Problems

PROC OPTMODEL

Learning Objectives:
Introduction to Business Analytics
Introduction

Objectives

Need of Business Analytics

Business Decisions

Introduction to Business Analytics

Features of Business Analytics

Types of Business Analytics

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Supply Chain Analytics

Health Care Analytics

Marketing Analytics

Human Resource Analytics

Web Analytics

Application of Business Analytics

Business Decisions

Business Intelligence (BI)

Data Science

Importance of Data Science

Data Science as a Strategic Asset

Big Data

Analytical Tools
Introduction to R
Introduction

Objectives

An Introduction to R

Comprehensive R Archive Network (CRAN)

Cons of R

Companies Using R

Understanding R

Installing R on Various Operating Systems

Installing R on Windows from CRAN Website

Install R

IDEs for R

Installing RStudio on Various Operating Systems

Install R-Studio

Steps in R Initiation

Benefits of R Workspace

Setting the Workplace

Functions and Help in R

Access the Help Document

R Packages o Installing an R Package

Install and Load a Package
R Data Structure
Introduction

Objectives

Types of Data Structures in R

Vectors

Create a Vector

Scalars

Colon Operator

Accessing Vector Elements

Matrices

Accessing Matrix Elements

Create a Matrix

Arrays

Accessing Array Elements

Create an Array

Data Frames

Elements of Data Frames

Create a Data Frame

Factors

Create a Factor

Lists

Create a List

Importing Files in R

Importing an Excel File

Importing a Minitab File

Importing a Table File

Importing a CSV File

Read Data from a File

Read Data from a File

Exporting Files from R
Apply Functions
Introduction

Objectives

Types of Apply Functions

Apply() Function

Lapply() Function

Sapply() Function

Tapply() Function

Vapply() Function

Mapply() Function

Dplyr Package

Installing the Dplyr Package

Functions of the Dplyr Package

Functions of the Dplyr Package – Select()

Use the Select() Function

Functions of Dplyr-Package – Filter()

Use the Filter() Function

Use Select Function

Functions of Dplyr Package – Arrange()

Use Arrange Function

Functions of Dplyr Package – Mutate()

Functions of Dply Package – Summarise()

Use Summarise Function
Data Visualization
Introduction

Objectives

Graphics in R

Types of Graphics

Bar Charts

Creating Simple Bar Charts

Editing a Simple Bar Chart

Create a Stacked Bar Plot and Grouped Bar Plot

Pie Charts

Editing a Pie Chart

Create a Pie Chart

Histograms

Creating a Histogram

Kernel Density Plots

Creating a Kernel Density Plot

Create Histograms and a Density Plot

Line Charts

Creating a Line Chart

Box Plots

Creating a Box Plot

Create Line Graphs and a Box Plot

Heat Maps o Creating a Heat Map

Create a Heatmap

Word Clouds

Creating a Word Cloud

File Formats for Graphics Outputs

Saving a Graphic Output as a File

Save Graphics to a File

Exporting Graphs in RStudio

Exporting Graphs as PDFs in RStudio

Save Graphics Using RStudio
Introduction to Statistics
Introduction

Objectives

Basics of Statistics

Types of Data

Qualitative vs. Quantitative Analysis

Types of Measurements in Order

Nominal Measurement

Ordinal Measurement

Interval Measurement

Ratio Measurement

Statistical Investigation

Normal Distribution

Example of Normal Distribution

Importance of Normal Distribution in Statistics

Use of the Symmetry Property of Normal Distribution

Standard Normal Distribution

Use Probability Distribution Functions

Distance Measures

Distance Measures – A Comparison

Euclidean Distance

Example of Euclidean Distance

Manhattan Distance

Minkowski Distance

Mahalanobis Distance

Cosine Similarity

Correlation

Correlation Measures Explained

Pearson Product Moment Correlation (PPMC)

Pearson Correlation

Dist() Function in R

Perform the Distance Matrix Computations
Hypothesis Testing I
Introduction

Objectives

Hypothesis

Need of Hypothesis Testing in Businesses

Null Hypothesis

Alternate Hypothesis

Null vs. Alternate Hypothesis

Chances of Errors in Sampling

Types of Errors

Contingency Table

Decision Making

Critical Region

Level of Significance

Confidence Coefficient

Bita Risk

Power of Test

Factors Affecting the Power of Test

Types of Statistical Hypothesis Tests

Upper Tail Test

Test Statistic

Factors Affecting Test Statistic

Critical Value Using Normal Probability Table
Hypothesis Testing II
Introduction

Objectives

Parametric Tests

Z-Test

Z-Test in R

T-Test

T-Test in R

Use Normal and Student Probability Distribution Functions

Testing Null Hypothesis

Objectives of Null Hypothesis Test

Three Types of Hypothesis Tests

Hypothesis Tests About Population Means

Decision Rules

Hypothesis Tests About Population Means

Hypothesis Tests About Population Proportions

Chi-Square Test

Steps of Chi-Square Test

Degree of Freedom

Chi-Square Test for Independence

Chi-Square Test for Goodness of Fit

Chi-Square Test for Independence

Chi-Square Test in R

Use Chi-Squared Test Statistics

Introduction to ANOVA Test

One-Way ANOVA Test

The F-Distribution and F-Ratio

F-Ratio Test

F-Ratio Test in R

One-Way ANOVA Test

One-Way ANOVA Test in R

Perform ANOVA
Regression Analysis
Introduction

Objectives

Introduction to Regression Analysis

Use of Regression Analysis

Types Regression Analysis

Simple Regression Analysis

Multiple Regression Models

Simple Linear Regression Model

Perform Simple Linear Regression

Correlation

Correlation Between X and Y

Find Correlation

Method of Least Squares Regression Model

Coefficient of Multiple Determination Regression Model

Standard Error of the Estimate Regression Model

Dummy Variable Regression Model

Interaction Regression Model

Non-Linear Regression

Non-Linear Regression Models

Perform Regression Analysis with Multiple Variables

Non-Linear Models to Linear Models

Algorithms for Complex Non-Linear Models
Classification
Objectives

Introduction to Classification

Examples of Classification

Classification vs. Prediction

Classification System

Classification Process

Classification Process – Model Construction

Classification Process – Model Usage in Prediction

Issues Regarding Classification and Prediction

Data Preparation Issues

Evaluating Classification Methods Issues

Decision Tree

Decision Tree – Dataset

Classification Rules of Trees

Overfitting in Classification

Tips to Find the Final Tree Size

Basic Algorithm for a Decision Tree

Statistical Measure – Information Gain

Calculating Information Gain for Continuous-Value Attributes

Enhancing a Basic Tree

Decision Trees in Data Mining

Model a Decision Tree

Naive Bayes Classifier Model

Features of Naive Bayes Classifier Model

Bayesian Theorem

Naive Bayes Classifier

Applying Naive Bayes Classifier

Naive Bayes Classifier – Advantages and Disadvantages

Perform Classification Using the Naive Bayes Method

Nearest Neighbor Classifiers

Computing Distance and Determining Class

Choosing the Value of K

Scaling Issues in Nearest Neighbor Classification

Support Vector Machines

Advantages of Support Vector Machines

Geometric Margin in SVMs

Linear SVMs

Non-Linear SVMs

Support a Vector Machine
Clustering
Introduction

Objectives

Introduction to Clustering

Clustering vs. Classification

Use Cases of Clustering

Clustering Models

K-means Clustering

K-means Clustering Algorithm

Pseudo Code of K-means

K-means Clustering Using R

K-means Clustering

Perform Clustering Using K-means

Hierarchical Clustering

Hierarchical Clustering Algorithms

Requirements of Hierarchical Clustering Algorithms

Agglomerative Clustering Process

Perform Hierarchical Clustering

DBSCAN Clustering

Concepts of DBSCAN

DBSCAN Clustering Algorithm

DBSCAN in R

DBSCAN Clustering
Association
Introduction

Objectives

Association Rule Mining

Application Areas of Association Rule Mining

Parameters of Interesting Relationships

Association Rules

Association Rule Strength Measures

Limitations of Support and Confidence

Apriori Algorithm

Applying Apriori Algorithm

Step 1 – Mine All Frequent Item Sets

Algorithm to Find Frequent Item Set

Ordering Items

Candidate Generation

Step 2 – Generate Rules from Frequent Item Sets

Perform Association Using the Apriori Algorithm

Perform Visualization on Associated Rules

Problems with Association Mining

Learning Objectives:
Introduction to Big data and Hadoop Ecosystem
Introduction

Overview to Big Data and Hadoop

Hadoop Ecosystem
HDFS and YARN
Introduction

HDFS Architecture and Components

Block Replication Architecture

YARN Introduction
MapReduce and Sqoop
Introduction

Why Mapreduce

Small Data and Big Data

Data Types in Hadoop

Joins in MapReduce

What is Sqoop
Basics of Hive and Impala
Introduction

Interacting with Hive and Impala

Working with Hive and Impala

Data Types in Hive

Validation of Data

What is Catalog and Its Uses
Types of Data Formats
Introduction

Types of File Format

Data Serialization

Importing MySql and Creating hive to

Parquet With Sqoop
Advanced Hive Concept and Data File Partitioning
Introduction

Overview of the Hive Query Language
Apache Flume and HBase
Introduction

Introduction to HBase
Pig
Introduction

Getting Datasets for Pig Development
Basics of Apache Spark
Introduction

Spark – Architecture, Execution, and Related Concepts

RDD Operations

Functional Programming in Spark
RDDs in Spark
Introduction

RDD Data Types and RDD Creation

Operations in RDDs

Implementation of Spark Applications
Introduction

Running Spark on YARN

Running a Spark Application

Dynamic Resource Allocation

Configuring Your Spark Application
Spark Parallel Processing
Introduction

Parallel Operations on Partitions
Spark RDD Optimization Techniques
Introduction

RDD Persistence
Spark RDD Optimization Techniques
Spark Algorithm

Introduction

Spark: An Iterative Algorithm

Introduction To Graph Parallel System

Introduction To Machine Learning

Introduction To Three C’s
Spark SQL
Introduction

Interoperating with RDDs
Apache Kafka
Core Java

Learning Objectives:
Data Science
Introduction to Data Science

Different Sectors Using Data Science

Purpose and Components of Python
Data Analytics
Data Analytics Process

Exploratory Data Analysis(EDA)

EDA-Quantitative Technique

EDA – Graphical Technique

Data Analytics Conclusion or Predictions

Data Analytics Communication

Data Types for Plotting

Data Types and Plotting
Statistical Analysis and Business Applications
Introduction to Statistics

Statistical and Non-statistical Analysis

Major Categories of Statistics

Statistical Analysis Considerations

Population and Sample

Statistical Analysis Process

Data Distribution

Dispersion o Histogram

Testing
Python Environment Setup and Essentials
Anaconda

Installation of Anaconda Python Distribution (contd.)

Data Types with Python

Basic Operators and Functions
Mathematical Computing with Python (NumPy)
Introduction to Numpy

Activity-Sequence it Right

Creating and Printing an array

Class and Attributes of array

Basic Operations

Activity-Slice It

Copy and Views

Mathematical Functions of Numpy
Scientific computing with Python (Scipy)
Introduction to SciPy

SciPy Sub Package – Integration and Optimization

SciPy sub package

Calculate Eigenvalues and Eigenvector

SciPy Sub Package – Statistics, Weave and IO
Data Manipulation with Pandas
Introduction to Pandas

Understanding DataFrame

View and Select Data

Missing Values

Data Operations

File Read and Write Support

Pandas Sql Operation
Machine Learning with Scikit–Learn
Machine Learning Approach

How it Works

Supervised Learning Model Considerations

Scikit-Learn

Supervised Learning Models – Linear Regression

Supervised Learning Models – Logistic Regression

Unsupervised Learning Models

Pipeline

Model Persistence and Evaluation
Natural Language Processing with Scikit Learn
NLP Overview

NLP Applications

NLP Libraries-Scikit

Extraction Considerations

Scikit Learn-Model Training and Grid Search
Data Visualization in Python using matplotlib
Introduction to Data Visualization

Line Properties

(x,y) Plot and Subplots

Types of Plots
Web Scraping with BeautifulSoup
Web Scraping and Parsing

Understanding and Searching the Tree

Navigating options

Navigating a Tree

Modifying the Tree

Parsing and Printing the Document
Python integration with Hadoop MapReduce and Spark
Why Big Data Solutions are Provided for Python

Hadoop Core Components

Python Integration with HDFS using Hadoop Streaming

Using Hadoop Streaming for Calculating Word Count

Python Integration with Spark using PySpark

Using PySpark to Determine Word Count

Python Basics

Learning Objectives:
Introduction to Business Analytics
Introduction

What Is in It for Me

Types of Analytics

Areas of Analytics
Formatting Conditional Formatting and Important Functions
Introduction

What Is in It for Me

Custom Formatting Introduction

Conditional Formatting Introduction

Logical Functions

Lookup and Reference Functions

VLOOKUP Function

HLOOKUP Function

MATCH Function

INDEX and OFFSET Function

Statistical Function

SUMIFS Function

COUNTIFS Function

PERCENTILE and QUARTILE

STDEV, MEDIAN and RANK Function
Analyzing Data with Pivot Tables
Introduction

What Is in It for Me

Pivot Table Introduction

Concept Video of Creating a Pivot Table

Grouping in Pivot Table Introduction

Custom Calculation

Calculated Field and Calculated Item

Slicer Intro

Creating a Slice

Dashboarding

Introduction

What Is in It for Me

What is a Dashboard

Principles of Great Dashboard Design

How to Create Chart in Excel

Chart Formatting

Thermometer Chart

Pareto Chart

Form Controls in Excel

Interactive Dashboard with Form Controls

Chart with Checkbox

Interactive Chart
Business Analytics With Excel
Introduction

What Is in It for Me

Concept Video Histogram

Concept Video Solver Addin

Concept Video Goal Seek

Concept Video Scenario Manager

Concept Video Data Table

Concept Video Descriptive Statistics
Data Analysis Using Statistics
Introduction

Moving Average

Hypothesis Testing

ANOVA

Covariance

Correlation

Regression

Normal Distribution
Power BI
Introduction

Power Pivot

Power View

Power Query

Power Map
Microsoft Power BI Desktop
Microsoft Power BI Recipes

Learning Objectives:
Machine Learning Introduction
Techniques of Machine Learning

Supervised Learning

Unsupervised Learning

Semi-supervised Learning and Reinforcement Learning

Some Important Considerations in Machine Learning

Data Preprocessing
Data Preparation

Feature engineering

Feature scaling

Datasets

Dimensionality reduction

Math Refresher
Eigenvalues, Eigenvectors, and Eigendecomposition

Concepts of Linear Algebra

Introduction to Calculus

Probability and Statistics

Regression
Regression and Its Types

Linear Regression: Equations and Algorithms

Classification
Logistic regression

K-nearest neighbours

Support Vector Machines

Kernel SVM

Naive Bayes

Decision tree classifier

Random forest classifier

Unsupervised learning – Clustering
K-Means Clustering

Clustering Algorithms

Introduction to Deep Learning
Meaning and importance of deep learning

Artificial Neural networks

TensorFlow

Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence

Machine Learning o Machine Learning algorithms o Applications of Machine Learning

Python Programming for Beginners
Python Django From Scratch

Learning Objectives:
Introduction to Deep Learning with TensorFlow
Introduction to TensorFlow

Intro to TensorFlow

Computational Graph

Key highlights

Creating a Graph

Regression example

Gradient Descent

TensorBoard

Modularity

Sharing Variables

Keras

Perceptrons
What is a Perceptron

XOR Gate

Activation Functions
Sigmoid

ReLU

Hyperbolic Fns

Softmax

Artificial Neural Networks
Introduction

Perceptron Training Rule

Gradient Descent Rule

Gradient Descent and Backpropagation
Gradient Descent

Stochastic Gradient Descent

Backpropagation

Some problems in ANN

Optimization and Regularization
Overfitting and Capacity

Cross-Validation

Feature Selection

Regularization

Hyperparameters

Intro to Convolutional Neural Networks
Intro to CNNs

Kernel filter

Principles behind CNNs

Multiple Filters

CNN applications

Intro to Recurrent Neural Networks
Intro to RNNs

Unfolded RNNs

Seq2Seq RNNs

LSTM

RNN

Deep Learning applications
Image Processing

Natural Language Processing

Speech Recognition

Video Analytics

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