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Professional Data Science Training

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and 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, classification, cluster analysis, data mining, databases, and visualization

Curriculum
Our Courses prepared by Industry experts and Data Science Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Trainers

Mr Santhosh
Data Science
10+ Years of Experience as Data Science Programmer.

Data Science and ADVANCED Certified Professional Programmer.

Trained from Data Science Institute Australia.

Worked for IBM, Accenture, SunCorp &Commonwealth Bank. Corporate trainer for Accenture, Capgemini.

What do our Students say ?

Their Data Science courses are well structured and taught by recognised professionals which helps one to learn Data Science fast. I have found the videos to be of excellent quality. Thanks
Consultant
Swaroopa Nour
I had taken the data science master program which is a combo of . Since there are so many technologies involved in the Data Science course, getting your query resolved at the right time becomes the most important aspect.
Working in US
Anwar Pasha
I think this Data Science certification training is a very good way of starting to learn Data Science and make a career in it. The instructors are reasonably good. The projects was also very interesting and relevant to current industry trends.
IT Consultant
Pruthvi Krishna
The classes were highly interactive and also practical oriented. The office staff was cordial and co-operative. Every teaching session was recorded each day and was put on-line by the institute which was really helpful.
Asst Consultant
Shiva Kumar

Course Content

Introduction: What is Data Science?

– Big Data and Data Science hype { and getting past the hype

– Why now? { Datafication }

– Current landscape of perspectives

– Skill sets needed

Statistical Inference

– Populations and samples

– Statistical modeling, probability distributions, tting a model

– Intro to R

Exploratory Data Analysis and the Data Science Process

– Basic tools (plots, graphs and summary statistics) of EDA

– Philosophy of EDA

– The Data Science Process

– Case Study: Real Direct (online real estate rm)

Three Basic Machine Learning Algorithms

-Linear Regression

– k-Nearest Neighbors (k-NN)

– k-means

One More Machine Learning Algorithm and Usage in Applications

– Motivating application: Filtering Spam

– Why Linear Regression and k-NN are poor choices for Filtering Spam

– Naive Bayes and why it works for Filtering Spam

– Data Wrangling: APIs and other tools for scrapping the Web

Feature Generation and Feature Selection (Extracting Meaning From Data)

– Motivating application: user (customer) retention

– Feature Generation (brainstorming, role of domain expertise, and place for

imagination)

– Feature Selection algorithms

{Filters; Wrappers; Decision Trees; Random Forests

Recommendation Systems: Building a User-Facing Data Product

– Algorithmic ingredients of a Recommendation Engine

– Dimensionality Reduction

– Singular Value Decomposition

– Principal Component Analysis

– Exercise: build your own recommendation system

Mining Social-Network Graphs

– Social networks as graphs

– Clustering of graphs

– Direct discovery of communities in graphs

– Partitioning of graphs

– Neighborhood properties in graphs

Data Visualization

– Basic principles, ideas and tools for data visualization

– Examples of inspiring (industry) projects

– Exercise: create your own visualization of a complex dataset

MACHNIE LEARNING

SUPERVISED LEARNING

Introduction to supervised learning

Regression and classification

R-square, RMSE for regression

Confusion Matrix

Accuracy, Precision and Recall

F-1 score

ROC and AUC

(i) Linear Regression

Simple Liner Regression

Model Development

Model Validation

(ii) Logistic Regression

Model Development

Model Validation

Interpretation and Implementation

(iii) Decision Trees

Introduction

Decision Nodes Vs Leaf Nodes

Entropy, Gini Index and Information gain

Over fitting ad Pruning

(iv) Bagging and Random Forest

(v) Boosting

UNSUPERVISED LEARNING

Clustering

Hierarchical clustering

K-Means Clustering

Cluster profiling

Principal Component Analysis

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INTRODUCTION TO R

R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years.

R is a GNU package. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available.

  • Data types and Data structures
  • Mathematical operations in R
  • Conditional statements
  • Loops
  • Packages and Functions in R
  • Data Manipulation
  • Data Pre-Processing
  • Exercises

INTRODUCTION TO PYTHON

Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991. An interpreted language, Python has a design philosophy that emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly brackets or keywords), and a syntax that allows programmers
to express concepts in fewer lines of code than might be used in languages such as C++ or Java. It provides constructs that enable clear programming on both small and large scales Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library. Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is open source software ] and has a community-based development
model, as do nearly all of its variant implementations. CPython is managed by the non-profit Python Software Foundation.

  • Python programming Introduction
  • Data types and structures
  • Control statements
  • Functions
  • User defined functions
  • Python Packages
  • Exercises.Note: Practical training of all models in Python and R programming along with reference code

Professional Hadoop Administration Training

Spoorthy's Hadoop Administration Certification Training will guide you to gain expertise in maintaining large and complex Hadoop Clusters. You will learn exclusive Hadoop Admin activities like Planning, Installation, Configuration, Monitoring & Tuning. Furthermore, you will be mastering the security implementation through Kerberos and Hadoop v2 through industry-level cases studies.

Curriculum
Our Courses prepared by Industry experts and Hadoop Administration Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Trainers

Mr Charan Tej
Hadoop Administration Faculty
10+ Years of Experience as Hadoop Administration Expert.
Worked for IBM, Accenture, SunCorp &Commonwealth Bank. Corporate trainer for Accenture, Capgemini.

What do our Students say ?

NO DOUBTS!! Spoorthy is the best self-paced course providing institute. Everything is so well explained. cheers up!!
Student Placed in an It firm as a Analyst
Rajini
I really thank you for the training and support, your real-time scenarios are very helpful on the job, Thank you Swathi mam for your support
IT Consultant working in US
Rakesh Kumar
As a beginner, I found the Hadoop Administration training curriculum and explanation well defined. It is simple and easy to learn new and complex technologies.
IT Consultant , T** Noida
Shanmitha
from the day one, I just practiced all your scenarios, and now I got a job from MNC Thankyou spoorthy Solutions, for the best preparation assistance
Asst Consultant , IB* Noida
Kumar Varma

Course Content

INTRODUCTION

Big Data Introduction
What is Big Data?
Big Data – Why
Big Data – Journey
Big Data Statistics
Big Data Analytics
Big Data Challenges
Technologies Supported By Big Data

Hadoop Introduction
What Is Hadoop?
History Of Hadoop
Breakthroughs Of Hadoop
Future of Hadoop
Who Is Using?

Basic Concepts
The Hadoop Distributed File System – At a Glance
Hadoop Daemon Processes
Anatomy Of A Hadoop Cluster
Hadoop Distributions

HADOOP DISTRIBUTED FILE SYSTEM (HDFS)

What is HDFS?
Distributed File System (DFS)
Hadoop Distributed File System (HDFS)

HDFS Cluster Architecture and Block Placement
NameNode
DataNode
JobTracker
TaskTracker
Secondary NameNode

HDFS Concepts
Typical Workflow
Data Replication
Replica Placement
Replication Policy
Hadoop Rack Awareness
Anatomy of a File Read
Anatomy of a File Write

MAPREDUCE

STAGES OF MAPREDUCE
DAEMONS
Job Tracker
Task Tracker

TASK FAILURES
Child
Task Tracker Failures
Job Tracker Failures
HDFS Failures

YARN
HOW TO PLAN A CLUSTER
VERSIONS AND FEATURES
HARDWARE SELECTION
Master Hardware
Slave Hardware
Cluster sizing

OPERATING SYSTEM SELECTION
Deployment Layout
Software Packages
Hostname, DNS
Users, Groups, Privileges

DISK CONFIGURATION
Choose a FileSystem

INSTALLATION AND CONFIGURATION

APACHE HADOOP
Tarball Installation
Package Installation

CONFIGURATION
XML Configuration
Environment Variables
Logging Configuration
HDFS
Optimization and Tuning

MAPREDUCE
Optimization and Tuning

AUTHENTICATION

KERBEROS AND HADOOP
Kerberos
Configuring Hadoop Security

RESOURCE MANAGEMENT

WHAT IS RESOURCE MANAGEMENT?

MAPREDUCE SCHEDULER

Capacity Scheduler

Fair Scheduler

CLUSTER MAINTENANCE

MANAGING HADOOP PROCESS

Starting and stopping processes with Init scripts

Starting and stopping processes manually

 

HDFS MAINTENANCE

Adding and Decommissioning DataNode

Balancing HDFS Block Data

Dealing with a Failed disk

 

MAPREDUCE MAINTENANCE

Adding and Decommissioning TaskTracker

Kill MapReduce Job and Task

Dealing Blacklisted Tasktracker

TROUBLESHOOTING

COMMON FAILUERS AND PROBLEMS
HDFS AND MAPREDUCE CHECKS

BACKUP AND RECOVERY

DATA BACKUP

Distributed copy

Parallel data ingestion

 

NAMENODE METADATA

COURSE DELIVERABLES

 Workshop style coaching

 Interactive approach

Course material

Hands on practice exercises

Quiz at the end of each major topic

Tips and techniques on Cloudera Certification Examination

Mock interviews for each individual will be conducted on need basis

 Resume preparation and guidance

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Hadaoop training in hyderabad

Professional Hadoop Training

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, technqiues and frameworks.

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Curriculum
Our Courses prepared by Industry experts and Hadoop Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Trainers

Mr Sudheer Varma
Hadoop Faculty

7+ Years of Experience as Hadoop.

Trained from Hadoop Institute US.

Worked for Microsoft, TCS.

What do our Students say ?

Full marks for the Spoorthy support team for providing excellent support services. Since Hadoop was new to me and I used to have many queries but the support team was very qualified.
Hadoop Expert, Pune
Sumakanth
I am completely satisfied with the Spoorthy big data hadoop training. The trainer came with over a decade of industry experience.
IT Consultant
Rakshitha Jain
I wanted to learn big data since it had a huge scope. My career changed positively upon completion of Spoorthy Big Data Hadoop Online Training. Go with Spoorthy for a Bright Career !!! Thanks.
IT Consultant , Delhi
Vishal Miyank
I am fully satisfied with your services. Thank you for your guidance. I want to make a reference about the quiz at the end of the course that was perfectly designed to gauge my proficiency. Thanks a lot
Consultant , IB* Noida
Sai Kumar

Course Content

Introduction

1. Big Data Introduction

What is Big Data

  •  Data Analytics
  •  Bigdata Challenges
  •  Technologies supported by big data

2. Hadoop Introduction

  •  What is Hadoop?
  •  History of Hadoop
  •  Basic Concepts
  •  Future of Hadoop
  •  The Hadoop Distributed File System
  •  Anatomy of a Hadoop Cluster
  •  Breakthroughs of Hadoop
  •  Hadoop Distributions:
    •  Apache Hadoop
    •  Cloudera Hadoop
    •  Horton Networks Hadoop
    •  MapR Hadoop

Hadoop Daemon Processes

  •  Name Node
  •  DataNode
  •  Secondary Name Node/High Availability
  •  Job Tracker/Resource Manager
  •  Task Tracker/Node Manager

HDFS (Hadoop Distributed File System)

  •  Blocks and Input Splits
  •  Data Replication
  •  Hadoop Rack Awareness
  •  Cluster Architecture and Block Placement
  •  Accessing HDFS
  • JAVA Approach
  • CLI Approach

Hadoop Installation Modes and HDFS

  •  Local Mode
  •  Pseudo-distributed Mode
  •  Fully distributed mode
  •  Pseudo Mode installation and configurations
  •  HDFS basic file operations

Hadoop Developer Tasks

1. Writing a MapReduce Program

  •  Basic API Concepts
  •  The Driver Class
  •  The Mapper Class
  •  The Reducer Class
  •  The Combiner Class
  •  The Partitioner Class
  •  Examining a Sample MapReduce Program with several examples
  •  Hadoop’s Streaming API
  •  Examining a Sample MapReduce Program with several examples
  •  Running your MapReduce program on Hadoop 1.0
  •  Running your MapReduce Program on Hadoop 2.0

2. Performing several hadoop jobs

  •  Sequence Files
  •  Record Reader
  •  Record Writer
  •  Role of Reporter
  •  Output Collector
  •  Processing XML files
  •  Counters
  •  Directly Accessing HDFS
  •  ToolRunner
  •  Using The Distributed Cache

3. Advanced MapReduce Programming

  •  A Recap of the MapReduce Flow
  •  The Secondary Sort
  •  Customized Input Formats and Output Formats
  •  Map-Side Joins
  •  Reduce-Side Joins

4. Practical Development Tips and Techniques

  •  Strategies for Debugging MapReduce Code
  •  Testing MapReduce Code Locally by Using LocalJobRunner
  •  Testing with MRUnit
  •  Writing and Viewing Log Files
  •  Retrieving Job Information with Counters
  •  Reusing Objects

5. Data Input and Output

  •  Creating Custom Writable and Writable-Comparable Implementations
  •  Saving Binary Data Using SequenceFile and Avro Data Files
  •  Issues to Consider When Using File Compression

6. Tuning for Performance in MapReduce

  •  Reducing network traffic with Combiner, Partitioner classes
  •  Reducing the amount of input data using compression
  •  Reusing the JVM
  •  Running with speculative execution
  •  Input Formatters
  •  Output Formatters
  •  Schedulers
  •  FIFO schedulers
  •  FAIR Schedulers
  •  CAPACITY Schedulers

7. YARN

  •  What is YARN
  •  How YARN Works
  •  Advantages of YARN

Hadoop Ecosystems

 

 

1. PIG

  •  PIG concepts
  •  Install and configure PIG on a cluster
  •  PIG Vs MapReduce and SQL
  •  PIG Vs HIVE
  •  Write sample PIG Latin scripts
  •  Modes of running PIG
  •  Programming in Eclipse
  •  Running as Java program
  •  PIG UDFs
  •  PIG Macros
  •  Accessing Hive from PIG

2. HIVE

  •  Hive concepts
  •  Hive architecture
  •  Installing and configuring HIVE
  •  Managed tables and external tables
  •  Partitioned tables
  •  Bucketed tables
  •  Complex data types
  •  Joins in HIVE
  •  Multiple ways of inserting data in HIVE tables
  •  CTAS, views, alter tables
  •  User defined functions in HIVE
  •  Hive UDF
  •  Hive UDAF
  •  Hive UDTF
  •  SQOOP
  •  SQOOP concepts
  •  SQOOP architecture
  •  Install and configure SQOOP
  •  Connecting to RDBMS
  •  Internal mechanism of import/export
  •  Import data from Oracle/Mysql to HIVE
  •  Export data to Oracle/Mysql
  •  Other SQOOP commands

3. HBASE

  •  HBASE concepts
  •  ZOOKEEPER concepts
  •  HBASE and Region server architecture
  •  File storage architecture
  •  NoSQL vs SQL
  •  Defining Schema and basic operations
  •  DDLs
  •  DMLs
  •  HBASE use cases
  •  Access data stored in HBASE using clients like CLI, and Java
  •  Map Reduce client to access the HBASE data
  •  HBASE admin tasks
  •  OOZIE
  •  OOZIE concepts
  •  OOZIE architecture
  •  Workflow engine
  •  Job coordinator
  •  Install and configuring OOZIE
  •  HPDL and XML for creating Workflows
  •  Nodes in OOZIE
  •  Action nodes
  •  Control nodes
  •  Accessing OOZIE jobs through CLI, and web console
  •  Develop sample workflows in OOZIE on various Hadoop distributions
  •  Run HDFS file operations
  •  Run MapReduce programs
  •  Run PIG scripts
  •  Run HIVE jobs
  •  Run SQOOP Imports/Exports

4. FLUME

  •  FLUME Concepts
  •  FLUME architecture
  •  Installation and configurations
  •  Executing FLUME jobs
  •  IMPALA
  •  What is Impala
  •  How Impala Works
  •  Imapla Vs Hive
  •  Impala’s shortcomings
  •  Impala Hands on
  •  ZOOKEEPER
  •  ZOOKEEPER Concepts
  •  Zookeeper as a service
  •  Zookeeper in production

Integrations

  •  Mapreduce and HIVE integration
  •  Mapreduce and HBASE integration
  •  Java and HIVE integration
  •  HIVE – HBASE Integration
  •   – HADOOP

Spark

  •  Introduction to Scala
  •  Functional Programming in Scala
  •  Working with Spark RDDs

Hadoop Administrative Tasks:

1. Setup Hadoop cluster: Apache, Cloudera and VMware

  •  Install and configure Apache Hadoop on a multi node cluster
  •  Install and configure Cloudera Hadoop distribution in fully distributed mode
  •  Install and configure different ecosystems
  •  Basic Administrative tasks

Course Deliverables

  • Workshop style coaching
  • Interactive approach
  • Course material
  • Hands on practice exercises for each topic
  • Quiz at the end of each major topic
  • Tips and techniques on Cloudera Certification Examination
  • Linux concepts and basic commands
  • On Demand Services
  • Mock interviews for each individual will be conducted on need basis
  • SQL basics on need basi
  • Core Java concepts on need basis
  • Resume preparation and guidance
  • Interview questions

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Professional RPA Training

Spoorthy’s Robotic Process Automation training will make you an expert in UiPath RPA tool, so that you can drive RPA initiatives in your organisation. You’ll master the concepts of key considerations while designing a RPA solution using UiPath, perform Image and text automation, create RPA Bots, perform Data Manipulation and debug and handle the exceptions, using real-life case studies.

Curriculum
Our Courses prepared by Industry experts and RPA Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Trainers

Mr Aravind Sing
RPA Faculty

10+ Years of Experience as RPA Programmer.

ADVANCED Certified Professional Programmer.

Trained from RPA Institute Australia.

Worked for IBM, Accenture, SunCorp &Commonwealth Bank. Corporate trainer for Accenture, Capgemini.

What do our Students say ?

The training was structured well,which explains all concepts very holistically followed by the quizzes,that helped me to internalize the knowledge very well
Project Manager
Prahlad Patidar
My learning journey was excellent at Spoorthy! The classes were detailed and in – depth. Our trainer was amazing as he explained all the concepts very well.
IT Consultant
Devender
I would definitely like to give more than 5 stars to Spoorthy, i have been associated with more than two years from now, and i have never had any issue or any complains.
IT Consultant
Rupali Sharma
The course was very well structured. The sessions were in good pace and the instructor was knowledgeable too. Overall, A very good learning experience learning RPA.
Asst RPA Consultant
Sharath

Course Content

ROBOTIC PROCESS AUTOMATION CONCEPTS :

  • What is Robotic Process Automation
  •  Natural language processing and RPA
  •  How Robotic Process Automation works!
  •  Why to automate repetitive tasks/process
  •  RPA Solution Architecture Patterns – Key Considerations
  •  Input Data Handling Solution Pattern
  •  Exception Handling
  •  Transaction Logging
  •  Credential Management
  •  Secure Execution
  •  Monitoring and Reporting
  •  List of Robotic Process Automation Tools
  •  Robotic Process Automation Tool selection Checklist

BLUE PRISM:

  •  Introduction
  •  Process Studio
  •  Process Flow
  •  Inputs and Outputs
  •  Business Objects
  •  Object Studio
  •  Overview of Error and Case Management
  •  Error Management
  •  Case Management
  •  Additional Features
  •  Advanced Features
  •  Application Types

AUTOMATION ANYWHERE:

 Introduction to Automation Anywhere

UI PATH TOOL:

  • Flowchart
  •  Sequence
  •  Modular
  •  Variables
  •  Data Manipulation
  •  Recording
  •  Documentation
  •  Tool Activities
  •  Advanced UI Interaction
  •  About UI Elements
  •  UI Activities Properties
  •  Input Methods
  •  Example of Using Input Methods
  •  Output or Screen Scraping Methods
  •  Examples of Using Output or Screen Scraping Methods
  •  About Web Scraping
  •  Example of Using Web Scraping
  •  About Data Scraping
  •  Example of Using Data Scraping
  •  Selectors
  •  Image and Text Automation
  •  Mouse and Keyboard Activities
  •  Text Activities
  •  OCR Activities
  •  Image Activities
  •  Mouse and Keyboard Automation
  •  Text Automation
  •  OCR and Image Automation

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With to impart new energy in the IT training by endowing first rate and industry oriented courses to churn out the next generation IT experts.

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