DATA SCIENCE MASTERS PROGRAMBoost your professional skills andshoot up your career to a new high with this professional certification course

Duration : 6 Months

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MODULE NAME SEGMENT

Python and its applications

Installation and configuration

Working with IDLE (Integrated Development & Learning Environment)

“Hello World” program

Data types

Variables

Introduction to Python

String manipulation

String concatenation

Indexing

Slicing of strings

Typecasting and its applications

Escape character

User inputs

PYTHON

Data structures

Lists

Tuples

Di�erences between lists and tuples

Dictionaries

Set

Practical applications of data structures

If-else loop

For loop

While loop

Problems based on control loops

Control loops

Functions

Inbuilt functions in Python

Function definition

Function calling

Problems based on functions

Creation of Python modules

Packages

Installation and usage of pip (package manager)

Importing modules

Object Oriented Programming

Modules and packages

Introduction to object oriented programming design paradigm

Classes and objects

Constructor function

Class variables

Class methods

Static methods

Practical implementation of object oriented programming

Introduction to exceptions

Try and except block

‘Finally’ keyword

File handling

Exception handling

Python for Data Science

Networking

Numpy

Scipy

Pandas

Matplotlib

Scikit learn

MODULE NAME SEGMENT

Overview of the course

Intro to Data Analysis

Intro to R - Basics

Introduction

Data Frames

Lists

Conditions

Loops

Functions

Vectors

Matrices

Factors

Apply functions

R ANALYTICS

Utility functions

Regular Expressions

Time and Date

Basic R Programming

Tidyverse

Types of visualizations

Data wrangling

Data visualization

Grouping and summarizing

Importing data from flat files with utils

readr & data.table

Importing Excel data

Reproducible Excel work with XLConnectImporting Data in R (Part 1)

Importing data from databases

Importing data from the web

Importing data from statistical so�ware packages

Ticket Sales Data

MBTA Ridership Data

World Food Facts

School Attendance Data

Introduction to dplyr and tbls

Select and mutate

Filter and arrange

Summarize and the pipe operator

Group_by and working with databases

Cleaning Data

Case Study and Assignment 1

dplyr: Data Manipulation

Introduction and exploring raw data

Tidying data

Preparing data for analysis

Putting it all together

Mutating joins

Filtering joins and set operations

Assembling data

Advanced joining

dplyr: Joining Data

Case study

qplot and wrap-up

Tweets across the United States

Shakespeare gets Sentimental

Analyzing TV News

Singing a Happy Song (or Sad?!)

Language of data

Study types and cautionary tales

Sampling strategies and experimental design

Case study

Exploring Categorical Data

Exploring Numerical Data

Numerical Summaries

Case Study

ggplot 2: Data Visualization

Case Study and Assignment 2: Sentiment Analysis

Data and R

Exploratory Data Analysis

Introduction

Data

Aesthetics

Geometries

Data cleaning and summarizing with dplyr

Data visualization with ggplot2

Tidy modeling with broom

Joining and tidying

Case Study and Assignment 3: Exploratory Data Analysis

Authoring R Markdown Reports

Embedding Code

Compiling Reports

Configuring R Markdown (optional)

Reporting with R Markdown

MODULE NAME SEGMENT

Multiple Linear Regression

Simple Linear Regression

Polynomial Regression

Logistic Regression

SVR

Regressions

Classification

Decision Tree

KNN

SVM

Kernel SVM

Naive Bayes

MACHINE LEARNING

Recommended System development

Natural Language Processing

Clustering

Decision Tree

Random Forest

Hierarchical

K means

Text Classification

Language Modeling and Sequence Tagging

Mini Assignments

Introduction to Deep Learning and Neural Networks

Neural Networks

PCA/LDA

PCA

LDA

Kernel PCA

Tensorflow installation

Introduction to Tensorflow

Sequence to sequence tasks

Vector space modeling

Tensorflow basics

Artificial Neural Network

Convolution Neural Network

MODULE NAME SEGMENT

Introduction to Big Data

Introduction to the Course

Need for Handling Big Data

Structure of Big Data

Storage Technique

Introduction to Big Data & Hadoop

Hadoop Ecosystem

Application of Big Data

Big Data - Impact on IT

Overview of Big data Solutions

HDFS

Architecture (HDFS)

BIG DATA ANALYTICS

Cloudera VM Installations

Hadoop Distributed File System

Hadoop Clusters

Hadoop Ecosystem

Cloudera VM Overview

Cloudera VM Installation

HDFS

HDFS Daemons

VM Player Installation

Single Node Cluster Installation and Setup

Multi Node Cluster Installation and Setup

Writing Files to HDFS

Re-replicating Missing replicas

Channel and Sink

Practice

Map Reduce Framework

Flow

Flow Example

Practice

Practice: Mapper

Practice: Reducer

Combiner and Practitioner

Job Cycle

Failures

Advanced Map Reduce

HDFS Shell Command

Data Ingestion Technique: Flume

Data Ingestion Technique: Map Reduce

Practice

Introduction

Architecture

Aggression Flow

Checkpoints and Journals

Data Node Startup

Data Node Heartbeats

HDFS Shell Command

Practice: Reducer

Combiner and Practitioner

Input and Output Format

YARN

Data Modelling

Custome Input Format

Apache HIVE

Apache HIVE - Deep Concepts

Customising Table Storage Formats

Case Study

Partition

HIVE Positioning and Bucketing

Bucketing

HIVE-Joints

User Defined Functions

HIVE Shell

PIG Running

Apache PIG

Features

Data Modelling

Diagnostic Tools

PIG-Joints

HIVE

PIG

Use Cases

Commands

NoSQL

HBase

Hadoop vs NoSQL

Case Study

Sqoop

Introducing Hadoop with Other Systems

Overview of Connectors

Case Study

Oozie

Zookeeper

YARN

Cloudera Manager

Overview of Spark, Impala, Ambari, HCatalog

Real life Case Scenarios & Discussion

Need for NoSQL Database

Data Integration in Hadoop

Data Administration in Hadoop

Recent Advancements