Data Science with R Training in Chennai

Data Science with R Training Course Content

Learn to use R as your Data Science tool of choice This course teaches you R as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with R

• 1.1 Data Science Course Content Overview
• 1.2 Introduction to Data Science
• 1.3 Data
• 1.4 Big Data
• 1.5 Data Science Deep Dive
• 1.6 Intro to R Programming
• 1.7 R Programming Concepts
• 1.8 Data Manipulation in R
• 1.9 Data Import Techniques in R
• 1.10 Exploratory Data Analysis (EDA) using R
• 1.11 Data Visualization in R

Introduction to Data Science

• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence
• What is Data Analysis
• What is Data Mining
• What is Machine Learning
• Analytics vs Data Science
• Value Chain
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle

Data

• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data & Sources
• Data Quality & Changes
• Data Quality Issues
• Data Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

Big Data

• What is Big Data?
• 5 Vs of Big Data
• Big Data Architecture
• Big Data Technologies
• Big Data Challenge
• Big Data Requirements
• Big Data Distributed Computing & Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

Data Science Deep Dive

• What Data Science is
• Why Data Scientists are in demand
• What is a Data Product
• The growing need for Data Science
• Large Scale Analysis Cost vs Storage
• Data Science Skills
• Data Science Use Cases
• Data Science Project Life Cycle & Stages
• Map Reduce Framework
• Hadoop Ecosystem
• Data Acuqisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File format Conversions
• Annonymization

Intro to R Programming

• Introduction to R
• Business Analytics
• Analytics concepts
• The importance of R in analytics
• R Language community and eco-system
• Usage of R in industry
• Installing R and other packages
• Perform basic R operations using command line
• Usage of IDE R Studio and various GUI

R Programming Concepts

• The datatypes in R and its uses
• Built-in functions in R
• Subsetting methods
• Summarize data using functions
• Use of functions like head(), tail(), for inspecting data
• Use-cases for problem solving using R

Data Manipulation in R

• Various phases of Data Cleaning
• Functions used in Inspection
• Data Cleaning Techniques
• Uses of functions involved
• Use-cases for Data Cleaning using R

Data Import Techniques in R

• Import data from spreadsheets and text files into R
• Importing data from statistical formats
• Packages installation for database import
• Connecting to RDBMS from R using ODBC and basic SQL queries in R
• Web Scraping
• Other concepts on Data Import Techniques

Exploratory Data Analysis (EDA) using R

• What is EDA?
• Why do we need EDA?
• Goals of EDA
• Types of EDA
• Implementing of EDA
• Boxplots, cor() in R
• EDA functions
• Multiple packages in R for data analysis
• Some fancy plots
• Use-cases for EDA using R

Data Visualization in R

• Story telling with Data
• Principle tenets
• Elements of Data Visualization
• Infographics vs Data Visualization
• Data Visualization & Graphical functions in R
• Plotting Graphs
• Customizing Graphical Parameters to improvise the plots
• Various GUIs
• Spatial Analysis
• Other Visualization concepts

Big Data and Hadoop Introduction

• What is Big Data and Hadoop?
• Challenges of Big Data
• Traditional approach Vs Hadoop
• Hadoop Architecture
• Distributed Model
• Block structure File System
• Technologies supporting Big Data
• Replication
• Fault Tolerance
• Why Hadoop?
• Hadoop Eco-System
• Use cases of Hadoop
• Fundamental Design Principles of Hadoop
• Comparison of Hadoop Vs RDBMS

Understand Hadoop Cluster Architecture

• Hadoop Cluster & Architecture
• 5 Daemons
• Hands-On Exercise
• Typical Workflow
• Hands-On Exercise
• Writing Files to HDFS
• Hands-On Exercise
• Reading Files from HDFS
• Hands-On Exercise
• Rack Awareness
• Before Map Reduce

Map Reduce Concepts

• Map Reduce Concepts
• What is Map Reduce?
• Why Map Reduce?
• Map Reduce in real world.
• Map Reduce Flow
• What is Mapper?
• What is Reducer?
• What is Shuffling?
• Word Count Problem
• Hands-On Exercise
• Distributed Word Count Flow & Solution
• Log Processing and Map Reduce
• Hands-On Exercise

Advanced Map Reduce Concepts

• What is Combiner?
• Hands-On Exercise
• What is Partitioner?
• Hands-On Exercise
• What is Counter?
• Hands-On Exercise
• InputFormats/Output Formats
• Hands-On Exercise
• Map Join using MR
• Hands-On Exercise
• Reduce Join using MR
• Hands-On Exercise
• MR Distributed Cache
• Hands-On Exercise
• Using sequence files & images with MR
• Hands-On Exercise
• Planning for Cluster & Hadoop 2.0 Yarn
• Configuration of Hadoop
• Choosing Right Hadoop Hardware?
• Choosing Right Hadoop Software?
• Hadoop Log Files?

Hadoop 2.0 & YARN

• Hadoop 1.0 Challenges
• NN Scalability
• NN SPOF & HA
• Job Tracker Challenges
• Hadoop 2.0 New Features
• Hadoop 2.0 Cluster Architecture & Federation
• Hadoop 2.0 HA
• Yarn & Hadoop Ecosystem
• Yarn MR Application Flow

PIG

• Introduction to Pig
• What Is Pig?
• Pig’s Features & Pig Use Cases
• Interacting with Pig
• Basic Data Analysis with Pig
• Hands-On Exercise
• Pig Latin Syntax
• Loading Data
• Hands-On Exercise
• Simple Data Types
• Field Definitions
• Data Output
• Viewing the Schema
• Hands-On Exercise
• Filtering and Sorting Data
• Hands-On Exercise
• Commonly-Used Functions
• Hands-On Exercise: Pig for ETL Processing
• Processing Complex Data with Pig
• Hands-On Exercise
• Storage Formats
• Complex/Nested Data Types
• Hands-On Exercise
• Grouping
• Hands-On Exercise
• Built-in Functions for Complex Data
• Hands-On Exercise
• Iterating Grouped Data
• Hands-On Exercises
• Multi-Dataset Operations with Pig
• Hands-On Exercise
• Techniques for Combining Data Sets

Module 7

• Joining Data Sets in Pig
• Hands-On Exercise
• Splitting Data Sets
• Hands-On Exercise

HIVE

• Hive Fundamentals & Architecture
• Loading and Querying Data in Hive
• Hands-On Exercise
• Hive Architecture and Installation
• Comparison with Traditional Database
• HiveQL: Data Types, Operators and Functions,
• Hands-On Exercise
• Hive Tables ,Managed Tables and External Tables
• Hands-On Exercise
• Partitions and Buckets
• Hands-On Exercise
• Storage Formats, Importing Data, Altering Tables, Dropping Tables
• Hands-On Exercise
• Querying Data, Sorting and Aggregating, Map Reduce Scripts,
• Hands-On Exercise

Module-9

• Joins & Sub queries, Views
• Hands-On Exercise
• Integration, Data manipulation with Hive
• Hands-On Exercise
• User Defined Functions,
• Hands-On Exercise
• Appending Data into existing Hive Table
• Hands-On Exercise
• Static partitioning vs dynamic partitioning
• Hands-On Exercise

HBASE

• CAP Theorem
• HBase Architecture and concepts
• Introduction to HBase
• Client API’s and their features
• HBase tables The ZooKeeper Service
• Data Model, Operations

Module-11

• Programming and Hands on Exercises

SQOOP

• Introduction to Sqoop
• MySQL Client & server
• Connecting to relational data base using Sqoop
• Importing data using Sqoop from Mysql
• Exporting data using Sqoop to MySql
• Incremental append
• Importing data using Sqoop from Mysql to hive
• Exporting data using Sqoop to MySql from hive
• Importing data using Sqoop from Mysql to hbase
• Using queries and sqoop

Flume & Oozie

• What is Flume?
• Why use Flume, Architecture, configurations
• Master, collector, Agent
• Twitter Data Sentimental Analysis project
• Oozie
• What is Oozie, Architecture, configurations?
• Oozie Job Submission
• Oozie properties
• Hands on exercises

Projects

• Social Media Final Project
• Hadoop Project
• Objective
• Problem Definition
• Solution
• Discuss data sets and specifications of the project.

Project in Healthcare Domain

• Hadoop Project in Healthcare
• Objective
• Problem Definition
• Solution
• Discuss data sets and specifications of the project.

Project in Finance/Banking Domain

• Hadoop Project in Banking Domain
• Objective
• Problem Definition
• Solution
• Discuss data sets and specifications of the project.

Spark

Apache Spark

• Introduction to Apache Spark
• Why Spark
• Batch Vs. Real Time Big Data Analytics
• Batch Analytics – Hadoop Ecosystem Overview,
• Real Time Analytics Options,
• Streaming Data – Storm,
• In Memory Data – Spark, What is Spark?,
• Spark benefits to Professionals
• Limitations of MR in Hadoop
• Components of Spark
• Spark Execution Architecture
• Benefits of Apache Spark
• Hadoop vs Spark

Introduction to Scala

• Features of Scala
• Basic Data Types of Scala
• Val vs Var
• Type Inference
• REPL
• Objects & Classes in Scala
• Functions as Objects in Scala
• Anonymous Functions in Scala
• Higher Order Functions
• Lists in Scala
• Maps
• Pattern Matching
• Traits in Scala
• Collections in Scala

Spark Core Architecture

• Spark & Distributed Systems
• Spark for Scalable Systems
• Spark Execution Context
• What is RDD
• RDD Deep Dive
• RDD Dependencies
• RDD Lineage
• Spark Application In Depth
• Spark Deployment
• Parallelism in Spark
• Caching in Spark

Spark Internals

• Spark Transformations
• Spark Actions
• Spark Cluster
• Spark SQL Introduction
• Spark Data Frames
• Spark SQL with CSV
• Spark SQL with JSON
• Spark SQL with Database

Spark Streaming

• Features of Spark Streaming
• Micro Batch
• Dstreams
• Transformations on Dstreams
• Spark Streaming Use Case

Statistics + Machine Learning

Statistics

Whats is Statistics

• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

Machine Learning

Machine Learning Introduction

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques
• Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study
• Implementing Association rule mining
• Case study
• Understanding Process flow of Supervised Learning Techniques
• Decision Tree Classifier
• How to build Decision trees
• Case study
• Random Forest Classifier
• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study
• Naive Bayes Classifier.
• Case study
• Project Discussion
• Problem Statement and Analysis
• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.
• Linear Regression
• Case study
• Logistic Regression
• Case study
• Text Mining
• Case study
• Sentimental Analysis
• Case study

Python

Getting Started with Python

• Python Overview
• About Interpreted Languages
• Advantages/Disadvantages of Python pydoc.
• Starting Python
• Interpreter PATH
• Using the Interpreter
• Running a Python Script
• Python Scripts on UNIX/Windows
• Python Editors and IDEs.
• Using Variables
• Keywords
• Built-in Functions
• StringsDifferent Literals
• Math Operators and Expressions
• Writing to the Screen
• String Formatting
• Command Line Parameters and Flow Control.

Sequences and File Operations

• Lists
• Tuples
• Indexing and Slicing
• Iterating through a Sequence
• Functions for all Sequences
• Using Enumerate()
• Operators and Keywords for Sequences
• The xrange() function
• List Comprehensions
• Generator Expressions
• Dictionaries and Sets.

Deep Dive – Functions Sorting Errors and Exception Handling

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections
• Sorting Dictionaries
• Sorting Lists in Place
• Errors and Exception Handling
• Handling Multiple Exceptions
• The Standard Exception Hierarchy
• Using Modules
• The Import Statement
• Module Search Path
• Package Installation Ways.

Regular Expressionsit’s Packages and Object Oriented Programming in Python

• The Sys Module
• Interpreter Information
• STDIO
• Launching External Programs
• PathsDirectories and Filenames
• Walking Directory Trees
• Math Function
• Random Numbers
• Dates and Times
• Zipped Archives
• Introduction to Python Classes
• Defining Classes
• Initializers
• Instance Methods
• Properties
• Class Methods and DataStatic Methods
• Private Methods and Inheritance
• Module Aliases and Regular Expressions.

Debugging, Databases and Project Skeletons

• Debugging
• Dealing with Errors
• Using Unit Tests
• Project Skeleton
• Required Packages
• Creating the Skeleton
• Project Directory
• Final Directory Structure
• Testing your Setup
• Using the Skeleton
• Creating a Database with SQLite 3
• CRUD Operations
• Creating a Database Object.

Machine Learning Using Python

• Introduction to Machine Learning
• Areas of Implementation of Machine Learning
• Why Python
• Major Classes of Learning Algorithms
• Supervised vs Unsupervised Learning
• Learning NumPy
• Learning Scipy
• Basic plotting using Matplotlib
• Machine Learning application

Supervised and Unsupervised learning

• Classification Problem
• Classifying with k-Nearest Neighbours (kNN)

Algorithm

• General Approach to kNN
• Building the Classifier from Scratch
• Testing the Classifier
• Measuring the Performance of the Classifier.
• Clustering Problem
• What is K-Means Clustering
• Clustering with k-Means in Python and an

Application Example.

• Introduction to Pandas
• Creating Data Frames
• GroupingSorting
• Plotting Data
• Creating Functions
• Converting Different Formats
• Combining Data from Various Formats
• Slicing/Dicing Operations.

Scikit and Introduction to Hadoop

• Introduction to Scikit-Learn
• Inbuilt Algorithms for Use
• What is Hadoop and why it is popular
• Distributed Computation and Functional Programming
• Understanding MapReduce Framework Sample MapReduce Job Run.

Hadoop and Python

• PIG and HIVE Basics
• Streaming Feature in Hadoop
• Map Reduce Job Run using Python
• Writing a PIG UDF in Python
• Writing a HIVE UDF in Python
• Pydoop and MRjob Basics.

Python Project Work

• Real world project

Course advisor

Named by Onalytica as one of the three most influential people in Big Data, Also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. She also regularly speaks at renowned events.

What is a Data Scientist?

Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

The biggest benefits of getting R certified is how it opens doors to employment. R certification demonstrates that you can learn your job more quickly.

Typical job duties for data scientists

There's not a definitive job description when it comes to a data scientist role. But here are a few things you'll likely be doing:

• Collecting large amounts of unruly data and transforming it into a more usable format.
• Solving business-related problems using data-driven techniques.
• Working with a variety of programming languages, including SAS, R and R.
• Having a solid grasp of statistics, including statistical tests and distributions.
• Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
• Communicating and collaborating with both IT and business.
• Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.

What’s in a data scientist’s toolbox?

These terms and technologies are commonly used by data scientists:

• Data visualization: the presentation of data in a pictorial or graphical format so it can be easily analyzed.
• Machine learning: a branch of artificial intelligence based on mathematical algorithms and automation.
• Deep learning: an area of machine learning research that uses data to model complex abstractions.
• Pattern recognition: technology that recognizes patterns in data (often used interchangeably with machine learning).
• Data preparation: the process of converting raw data into another format so it can be more easily consumed.
• Text analytics: the process of examining unstructured data to glean key business insights.

How can you become a data scientist?

Positioning yourself for a career in data science could be a smart move. You’ll have plenty of job opportunities, plus it’s a chance to work in the technology field with room for experimentation and creativity. So what’s your strategy?

If you’re a student

Choosing Greens Technologies, Best R training institute in Chennai that offers data science and analytics training.

If you’re a professional who wants to shift careers

While most data scientists have backgrounds as data analysts or statisticians, others come from non-technical fields such as business or economics. How can professionals from such diverse backgrounds end up in the same field? It’s important to look at what they have in common: a knack for solving problems, the ability to communicate well and an insatiable curiosity about how things work. Learn how the R training institute in Chennai for Data Science gives you the tools to become a certified data scientist.

you’ll also need a solid understanding of:

• Statistics and machine learning.
• Coding languages such as SAS, R or Python.
• Databases such as Oracle and Postgres.
• Data visualization and reporting technologies.
• Hadoop and MapReduce.



If you don’t want to learn these skills on your own, take an online course or R class room training in Greens Technologys . And then, of course, you should network. Connect with other data scientists in your company, or find an online community. They’ll give you insider information into what data scientists do – and where you’ll find the best jobs.

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