Big Data Analytics Training in Bangalore

Big Data Analytics Course Syllabus

Big Data Analytics Training in Bangalore

Best Big Data Analytics Course in Bangalore & Top Big Data Analytics Training Institute

Module 1-Introduction to course

Introduction to corporate training

Software life cycle

Methodologies

Soft skills

Professional Ethics

Make you strength

Where I Stands?

Module 2-Data science introduction

What is data science

Need for data science?

Data science vs Business Intelligence

Prerequisite for learning data science

What does a data scientist do?

Data science life cycle with example

Demand for data science

Module 3-Installations

• Installations required for data science

 

Module 4 – R Programming

R Basics, background

Comprehensive R Archive Network

Demo of Installing R On windows from CRAN Website

Installing R Studios on Windows OS

Setting Up R Workspace.

Getting Help for R-How to use help system

Installing Packages – Loading And Unloading Packages

Getting familiar with basics

Operators in R – Arithmetic,Relational,Logical and Assignment Operators

Variables,Types Of Variables,Using variables Conditional statements,ifelse(),switch

Loops: For Loops,While Loops,Using Break statement,Switch

The R Programming Language- Data Types

Creating data objects from the keyword.

How to make different type of data objects.

Types of data structures in R

Arrays And Lists- Create Access the elements

Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations Matrices-

Building the first matrices,Matrix Operations,Subsetting,visualising subset

Data Frames- create and filter data frames,Building And Merging data frames.

Functions And Importing data into R

Function Overview – Naming Guidelines

Arguments Matching,Function with Multiple Arguments

Additional Arguments using Ellipsis,Lazy Evaluation Multiple Return Values Function as

Objects,Anonymous Functions

Importing and exporting Data into R- importing from files like excel,csv and minitab.

Import from URL and excel Files

Import from database.

Data Descriptive

Statistics, Tabulation, Distribution

Summary Statistics for Matrix Objects. apply() Command. Converting an Object into a Table

Histograms, Stem and Leaf Plot, Density

Normal Distribution

Graphics in R – Types of graphics

Bar Chart, Pie Chart, Histograms- Create and edit.

Box Plots- Basics of Boxplots- Create and Edit Visualisation in R using ggplot2.

More About Graphs: Adding Legends to Graphs,Adding Text to Graphs, Orienting the AxisLabel.

Module 5 -Python programming

Introduction to python

Operators

Data Types

Control Statements

Functions

Data Structures – Lists, Sets, Tuples, Strings, Dictionaries,

OOPS Concept

Module 6 -Libraries

• Numpy

• Pandas

• Scipy

• Scikit-Learn • Keras

 

• Matplotlib • Seaborn

• Cufflinks • NLTK

 

Note: Installations and work end to end. As per requirements going to work with different libraries.

 

Module 7 – Data Exploration

Collecting data from different sources

Analyzing data

Data preprocessing

Data munging

Data mining

Data manipulation

Data visualization

Feature Selection

Feature Scaling

Dimensionality reduction

Module 8 -Statistics

Basics of Statistics

Descriptive Statistics

Inferential Statistics

Qualitative vs. Quantitative Analysis

Hypothesis Testing

Data Distribution

Module 9 – Other Mathematics Concepts

Probability

Calculus

Linear algebra

Module 10 -Machine Learning

Introducing Machine Learning models

Supervised learning

Regression and Classification models

Unsupervised learning

Clustering and Aggregation models

Semi supervised learning

Over fitting and under fitting(Linear, Logistic, Navi Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest …..)Note: Deals with Mathematics and Programming part

Module 11 -Deep Learning and other algorithms

Introduction to Deep learning

OpenCV

Artificial Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Tensor flow

Over fitting and under fitting

Module 12 -Natural Language Processing

Introduction to NLP

Text Preprocessing

Lemmatization

Stemming

Text to features

Important Tasks in NLP

Big Data Analytics Trainer Profile

4V ( Volume, Velocity, Variety and Veracity) characteristics

Structured and Unstructured Data

Application and use cases of Big Data

Limitations of traditional large Scale systems

How a distributed way of computing is superior (cost and scale)

Opportunities and challenges with Big Data

Introduction to Linux and Big Data Virtual Machine (VM)

Introduction to Linux - Why Linux? -

Windows and the Linux equivalents

Different flavors of Linux

Unity Shell (Ubuntu UI)

Basic Linux

Commands (enough to get started with Hadoop)

HDFS Overview and Architecture

Deployment Architecture

Name Node, Data Node and Checkpoint Node ( aka Secondary Name Node)

Safe mode

Configuration files

HDFS Data Flows ( Read v/s Write)

Load Balancer

Dist Cp

HDFS Federation

HDFS High Availability

Hadoop Archives

CRC Checksum

Data replication

Rack awareness and Block placement policy

Small files problem

Command Line Interface

File System

Web Interface

Legacy MR v/s Next Generation MapReduce ( aka YARN/ MRv2)

Slots v/s Containers

Schedulers

Shuffling, Sorting

Hadoop Data Types

Input and Output Formats

Input Splits - Partitioning ( Hash Partitioner v/s Customer Partitioner)

Speculative execution

Combiners

JVM Reuse

Compression

Word Count

Term Frequency

Inverse Document Frequency

Log Data Analysis

Different ways of joining data

Purchases Data Analysis

Max Temperatur

PageRank

Inverted Index

Introduction and Architecture

Different Modes of executing Pig constructs

Data Types

Dynamic invokers Pig streaming Macros

Pig Latin language Constructs (LOAD, STORE, DUMP, SPLIT, etc)

User Defined Functions

Use Cases

Introduction and Architecture

Different Modes of executing Hive queries

Metastore Implementations

HiveQL (DDL & DML Operations)

External v/s Managed Tables Views

Partitions & Buckets

Joins, Group by, Order by

User Defined Functions

Review of RDBMS

Need for NoSQL

Brewers CAP Theorem

ACID v/s BASE

Schema on Read vs. Schema on Write

Different levels of consistency

Key Value

Columnar

Document

Graph

HBase Architecture

Master and Region Server

Catalog Tables (Root and Meta)

HBase Data Modeling

Loading data in HBase

Introduction to RDD

Installation and Configuration of Spark

Spark Architecture

Different interfaces to Spark

Data frames and Datasets

Querying massive data using SparkSql

Sample Python programs in Spark

Data Visualization using Apache Zeppelin

Cloudera Hadoop cluster on the Amazon Using EMR (Elastic Map Reduce)

Using EC2 (Elastic Compute Cloud)

Importing/ exporting data across RDBMS and HDFS using Sqoop

Getting real- time events into HDFS using Flume

Creating workflows in Oozie

Introduction to Graph processing

Graph processing with Neo4J

Processing data in real time using Storm

Interactive Adhoc querying with Impala

Popular Interview Questions for Big Data Analytics

Why choose BTA for Big Data Analytics Training in Bangalore?

• Bangalore Training Academy has excellent qualified Big Data Analytics Trainers
• 100% student satisfaction rate, we will continue that in future.
• Excellent Lab Facilities and Infrastructure
• There are no hard & fast rules for timings.

Big Data Analytics Training Batches in Bangalore

• Big Data Analytics Training Regular Batch ( Morning, Day time & Evening)
• Big Data Analytics Training Weekend Training Batch( Saturday, Sunday & Holidays)
• Big Data Analytics Training Fast Track batch

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