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Data-Science Training in Chennai

Data Science Overview Module #1 – Introduction to Data Science-Part1

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining

Module #1 – Introduction to Data Science-Part2

  • What is Machine Learning
  • Analytics vs Data Science
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle

Module #2 – Data-Part1

  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes

Module #2 – Data-Part2

  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?

Module #3 - Big Data-Part1

  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge

Module #3 - Big Data-Part2

  • Big Data Requirements
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem

Module #4 - Data Science Deep Dive-Part1

  • 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

Module #4 - Data Science Deep Dive-Part2

  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Map Reduce Framework
  • Hadoop Ecosystem

Module #4 - Data Science Deep Dive-Part3

  • Data Acuqisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats

Module #4 - Data Science Deep Dive-Part4

  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions
  • Annonymization

Module #5 - Intro to R Programming-Part1

  • Introduction to R
  • Business Analytics
  • Analytics concepts
  • The importance of R in analytics
  • R Language community and eco-system

Module #5 - Intro to R Programming-Part2

  • 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

Module #6- 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

Module #7 - 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

Module #8 - 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

Module #9 - Exploratory Data Analysis (EDA) using R-Part1

  • What is EDA?
  • Why do we need EDA?
  • Goals of EDA
  • Types of EDA
  • Implementing of EDA

Module #9 - Exploratory Data Analysis (EDA) using R-Part2

  • Boxplots, cor() in R
  • EDA functions
  • Multiple packages in R for data analysis
  • Some fancy plots
  • Use-cases for EDA using R

Module #10 - Data Visualization in R-Part1

  • Story telling with Data
  • Principle tenets
  • Elements of Data Visualization
  • Infographics vs Data Visualization
  • Data Visualization & Graphical functions in R

Module #10 - Data Visualization in R-Part2

  • Plotting Graphs
  • Customizing Graphical Parameters to improvise the plots
  • Various GUIs
  • Spatial Analysis
  • Other Visualization concepts

Hadoop Module #1 - Big Data and Hadoop Introduction-Part1

  • What is Big Data and Hadoop?
  • Challenges of Big Data
  • Traditional approach Vs Hadoop
  • Hadoop Architecture
  • Distributed Model

Module #1 - Big Data and Hadoop Introduction-Part2

  • Block structure File System
  • Technologies supporting Big Data
  • Replication
  • Fault Tolerance
  • Why Hadoop?

Module #1 - Big Data and Hadoop Introduction-Part3

  • Hadoop Eco-System
  • Use cases of Hadoop
  • Fundamental Design Principles of Hadoop
  • Comparison of Hadoop Vs RDBMS

Module #2 - Understand Hadoop Cluster Architecture-Part1

  • Hadoop Cluster & Architecture
  • 5 Daemons
  • Hands-On Exercise
  • Typical Workflow
  • Hands-On Exercise

Module #2 - Understand Hadoop Cluster Architecture-Part2

  • Writing Files to HDFS
  • Hands-On Exercise
  • Reading Files from HDFS
  • Hands-On Exercise
  • Rack Awareness
  • Before Map Reduce

Module #3 - Map Reduce Concepts-Part1

  • Map Reduce Concepts
  • What is Map Reduce?
  • Why Map Reduce?
  • Map Reduce in real world.
  • Map Reduce Flow

Module #3 - Map Reduce Concepts-Part2

  • What is Mapper?
  • What is Reducer?
  • What is Shuffling?
  • Word Count Problem

Module #3 - Map Reduce Concepts-Part3

  • Hands-On Exercise
  • Distributed Word Count Flow & Solution
  • Log Processing and Map Reduce
  • Hands-On Exercise

Module #4 - Advanced Map Reduce Concepts-Part1

  • What is Combiner?
  • Hands-On Exercise
  • What is Partitioner?
  • Hands-On Exercise
  • What is Counter?

Module #4 - Advanced Map Reduce Concepts-Part2

  • Hands-On Exercise
  • InputFormats/Output Formats
  • Hands-On Exercise
  • Map Join using MR
  • Hands-On Exercise

Module #4 - Advanced Map Reduce Concepts-Part3

  • Reduce Join using MR
  • Hands-On Exercise
  • MR Distributed Cache
  • Hands-On Exercise
  • Using sequence files & images with MR

Module #4 - Advanced Map Reduce Concepts-Part4

  • Hands-On Exercise
  • Planning for Cluster & Hadoop 2.0 Yarn
  • Configuration of Hadoop
  • Choosing Right Hadoop Hardware?
  • Choosing Right Hadoop Software?
  • Hadoop Log Files?

Module #5 - Hadoop 2.0 & YARN-Part1

  • Hadoop 1.0 Challenges
  • NN Scalability
  • NN SPOF & HA
  • Job Tracker Challenges
  • Hadoop 2.0 New Features

Module #5 - Hadoop 2.0 & YARN-Part2

  • Hadoop 2.0 Cluster Architecture & Federation
  • Hadoop 2.0 HA
  • Yarn & Hadoop Ecosystem
  • Yarn MR Application Flow

Module #6 – PIG-Part1

  • Introduction to Pig
  • What Is Pig?
  • Pig’s Features & Pig Use Cases
  • Interacting with Pig
  • Basic Data Analysis with Pig

Module #6 – PIG-Part2

  • Hands-On Exercise
  • Pig Latin Syntax
  • Loading Data
  • Hands-On Exercise
  • Simple Data Types

Module #6 – PIG-Part3

  • Field Definitions
  • Data Output
  • Viewing the Schema
  • Hands-On Exercise
  • Filtering and Sorting Data

Module #6 – PIG-Part4

  • Hands-On Exercise
  • Commonly-Used Functions
  • Hands-On Exercise: Pig for ETL Processing
  • Processing Complex Data with Pig
  • Hands-On Exercise

Module #6 – PIG-Part5

  • Storage Formats
  • Complex/Nested Data Types
  • Hands-On Exercise
  • Grouping
  • Hands-On Exercise

Module #6 – PIG-Part6

  • Built-in Functions for Complex Data
  • Hands-On Exercise
  • Iterating Grouped Data
  • Hands-On Exercises
  • Multi-Dataset Operations with Pig

Module #6 – PIG-Part7

  • Hands-On Exercise
  • Techniques for Combining Data Sets
  • Joining Data Sets in Pig
  • Hands-On Exercise
  • Splitting Data Sets
  • Hands-On Exercise

Module #7 – HIVE-Part1

  • Hive Fundamentals & Architecture
  • Loading and Querying Data in Hive
  • Hands-On Exercise
  • Hive Architecture and Installation
  • Comparison with Traditional Database

Module #7 – HIVE-Part2

  • HiveQL: Data Types, Operators and Functions,
  • Hands-On Exercise
  • Hive Tables ,Managed Tables and External Tables
  • Hands-On Exercise
  • Partitions and Buckets

Module #7 – HIVE-Part3

  • 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 #8 - 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 #9 – SQOOP-Part1

  • 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

Module #9 – SQOOP-Part2

  • 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

Module #10 - Flume & Oozie-Part1

  • What is Flume?
  • Why use Flume, Architecture, configurations
  • Master, collector, Agent
  • Twitter Data Sentimental Analysis project
  • Oozie

Module #10 - Flume & Oozie-Part2

  • What is Oozie, Architecture, configurations?
  • Oozie Job Submission
  • Oozie properties
  • Hands on exercises

Spark Module #1 - Apache Spark-Part1

  • Introduction to Apache Spark
  • Why Spark
  • Batch Vs. Real Time Big Data Analytics
  • Batch Analytics – Hadoop Ecosystem Overview,
  • Real Time Analytics Options.

Module #1 - Apache Spark-Part2

  • Streaming Data – Storm,
  • In Memory Data – Spark, What is Spark?,
  • Spark benefits to Professionals
  • Limitations of MR in Hadoop.

Module #1 - Apache Spark-Part3

  • Components of Spark
  • Spark Execution Architecture
  • Benefits of Apache Spark
  • Hadoop vs Spark

Module #2 - Introduction to Scala-Part1

  • Features of Scala
  • Basic Data Types of Scala
  • Val vs Var
  • Type Inference
  • REPL

Module #2 - Introduction to Scala-Part2

  • Objects & Classes in Scala
  • Functions as Objects in Scala
  • Anonymous Functions in Scala
  • Higher Order Functions
  • Lists in Scala

Module #2 - Introduction to Scala-Part3

  • Maps
  • Pattern Matching
  • Traits in Scala
  • Collections in Scala

Module #3 - Spark Core Architecture-Part1

  • Spark & Distributed Systems
  • Spark for Scalable Systems
  • Spark Execution Context
  • What is RDD
  • RDD Deep Dive

Module #3 - Spark Core Architecture-Part2

  • RDD Dependencies
  • RDD Lineage
  • Spark Application In Depth
  • Spark Deployment
  • Parallelism in Spark
  • Caching in Spark

Module #4 - Spark Internals-Part1

  • Spark Transformations
  • Spark Actions
  • Spark Cluster
  • Spark SQL Introduction

Module #4 - Spark Internals-Part2

  • Spark Data Frames
  • Spark SQL with CSV
  • Spark SQL with JSON
  • Spark SQL with Database

Module #5 - Spark Streaming

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

Statistics-Part1 What is Statistics?

  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions

Statistics-Part2 What is Statistics?

  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics

Statistics-Part3 What is Statistics?

  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test

Statistics-Part4 What is Statistics?

  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

Machine Learning Introduction-Part1

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Clustering
  • Similarity Metrics

Machine Learning Introduction-Part2

  • 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

Machine Learning Introduction-Part3

  • Implementing Association rule mining
  • Case study
  • Understanding Process flow of Supervised Learning Techniques
  • Decision Tree Classifier
  • How to build Decision trees

Machine Learning Introduction-Part4

  • Case study
  • Random Forest Classifier
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance

Machine Learning Introduction-Part5

  • Case study
  • Naive Bayes Classifier.
  • Case study
  • Project Discussion
  • Problem Statement and Analysis

Machine Learning Introduction-Part6

  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.
  • Linear Regression
  • Case study
  • Logistic Regression

Machine Learning Introduction-Part7

  • Case study
  • Text Mining
  • Case study
  • Sentimental Analysis
  • Case study

Python Module #1 - Getting Started with Python-Part1

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python
  • Interpreter PATH

Module #1 - Getting Started with Python-Part2

  • Using the Interpreter
  • Running a Python Script
  • Python Scripts on UNIX/Windows
  • Python Editors and IDEs.
  • Using Variables

Module #1 - Getting Started with Python-Part3

  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control

Module #2 - Sequences and File Operations-Part1

  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences

Module #2 - Sequences and File Operations-Part2

  • Using Enumerate()
  • Operators and Keywords for Sequences
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets

Module #3 - Deep Dive – Functions Sorting Errors and Exception Handling-Part1

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys

Module #3 - Deep Dive – Functions Sorting Errors and Exception Handling-Part2

  • Lambda Functions
  • Sorting Collections of Collections
  • Sorting Dictionaries
  • Sorting Lists in Place
  • Errors and Exception Handling

Module #3 - Deep Dive – Functions Sorting Errors and Exception Handling-Part3

  • Handling Multiple Exceptions
  • The Standard Exception Hierarchy
  • Using Modules
  • The Import Statement
  • Module Search Path
  • Package Installation Ways

Module #4 - Regular Expressionsit’s Packages and Object Oriented Programming in Python-Part1

  • The Sys Module
  • Interpreter Information
  • STDIO
  • Launching External Programs
  • PathsDirectories and Filenames

Module #4 - Regular Expressionsit’s Packages and Object Oriented Programming in Python-Part2

  • Walking Directory Trees
  • Math Function
  • Random Numbers
  • Dates and Times
  • Zipped Archives
  • Introduction to Python Classes

Module #4 - Regular Expressionsit’s Packages and Object Oriented Programming in Python-Part3

  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and DataStatic Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressions

Module #5 - Debugging, Databases and Project Skeletons-Part1

  • Debugging
  • Dealing with Errors
  • Using Unit Tests
  • Project Skeleton
  • Required Packages
  • Creating the Skeleton

Module #5 - Debugging, Databases and Project Skeletons-Part2

  • Project Directory
  • Final Directory Structure
  • Testing your Setup
  • Using the Skeleton
  • Creating a Database with SQLite 3
  • CRUD Operations
  • Creating a Database Object

Module #6 - Machine Learning Using Python-Part1

  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning

Module #6 - Machine Learning Using Python-Part2

  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • Machine Learning application

Module #7 - Supervised and Unsupervised learning

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

Module #8 – Algorithm-Part1

  • General Approach to kNN
  • Building the Classifier from Scratch
  • Testing the Classifier
  • Measuring the Performance of the Classifier.
  • Clustering Problem

Module #8 – Algorithm-Part2

  • What is K-Means Clustering
  • Clustering with k-Means in Python and an

Module #9 - Application Example-Part1

  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions

Module #9 - Application Example-Part2

  • Converting Different Formats
  • Combining Data from Various Formats
  • Slicing/Dicing Operations

Module #10 - 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

Module #11 - 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.
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  • We in Kyros Academy have a team of professionally trained exclusive personnel from a various IT Companies to train in trending technologies, who have worked in MNCs and other reputed institutions, who deliver the training with loads of enthusiasm.
    Get your technology with enriched IT Professionals who train you on your skill needs & your career growth

    • Have been working in the Data Science space for many years starting with the dev team of Amazon Web Services S3 team and later at Microsoft for their Big Data platform
    • Experience in delivering workshops / webinar(s) and consultancy on Data Science technology
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    Join Data-Science Training in Chennai

  • Certification

    Following this course, Data-Science recommends that attendees have at least two to three months of hands‑on experiences with Data-Science before attempting the Certification Exam.

    Further details regarding this exam, including an exam blueprint, can be found on the Successful candidates will be awarded a “Datascience Certified System” certificate.

    Why Learn Data Science?
    • Data Science certification training lets you master data analysis, deploying R Statistical computing, machine learning algorithms, K-Means Clustering, NaïveBayes, connecting R with Hadoop framework, work on real world projects and case studies You will learn time series analysis, business analytics and more in this popular course
    Who Should Take This Training Course?

    Big Data Specialists, Business Analysts and Business Intelligence professionals
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    Developers wanting to learn Machine Learning (ML) Techniques
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    Those looking to take up the roles of Data Scientist and Machine Learning Expert

    What are the prerequisites for learning Data Science?

    There are no particular prerequisites for this Training Course. If you love mathematics, it is helpful.

    What is the Duration of the Course?

    40 Hours

    Certification

    Data Science Certification Exam

    Join Data-Science Training in Chennai

    We in Kyros Academy have a team of professionally trained exclusive personnel from a various IT Companies to train in trending technologies, who have worked in MNCs and other reputed institutions, who deliver the training with loads of enthusiasm.
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    “Kyros Academy is an Intellectual bridge between the highly sophisticated dreamy industries and abundant talent filled students. The Data Science course taught here are well planned and build one’s interest to explore more further. “I would
    recommend anyone who is looking for a good career in Data Science to join this
    course and get pampered in Industry.”

    Sudha

    “The Data Science course, which I have done at Kyros Academy, helped me so much to face the technical interview confidently and get the job. Actually, speaking the hands-on lab sessions and the projects that I have done increased my confidence
    level. I am glad to say that this advanced Data Science course shaped my career.”

    Deenanath

    “This is what I felt while undergoing the Data-Science Training in Chennai at Kyros Academy. The trainer is excellent. I always wanted a trainer like him to train me in something new. His abundant knowledge about the subject and vast industry
    experience is the right mix for somebody who is responsible to impart knowledge.”

    Vinoth

    “I did my Data-Science Training in Chennai. I found the whole experience at Kyros Academy a pleasant one. Hence, I have no reservations in recommending this course to any of my friends or colleagues.”

    Harshita

    “Before joining the Data Science course, it was really unbelievable that anyone can make me clear about the Data Science concept in just few days as it’s a vast language with lots of complexities. But, Kyros Academy was really helpful to me for getting the clear idea. Thank You.”

    Vimala

    “Here at Kyros Academy, I found the course and labs are arranged in such a manner that were sufficient for me to get the complete picture of Data Science. I appreciate the efforts made by the Kyros Academy team to make this Data Science
    learning easy for us and wish a great future for Kyros Academy(Data-Science Training in Chennai).”

    Jyotiranjan

    “I am happy to be a part of Data Science course in Kyros Academy. According to me, the unique features are excellent-teaching professionals with vast industry experience. Well designed course content to meet the real time industry needs.
    More concentration on concepts and practical implementation.”

    Kirtikumar

    “I did the Data-Science Training in Chennai. Here not just the project assignment to implement the concepts learnt but also the evaluation by the faculty. Offline help of the faculty even after finishing the course. I personally felt that this course will pave the way to great future.”

    Ramesh

    “Hi, I thank Kyros Academy for teaching me Data-Science Training in Chennai. I got placed in a company before completing the course. The teaching is excellent and I like the way he explain the topic with example now-a- days. Once again, I thank the trainer for teaching me Data Science and placing me.”

    Samividhan

    “Hi Friends, I have completed Data-Science Training in Chennai. The Trainer’s explanation of each and every topic is excellent so that anyone can understand easily and clearly. He used to give thousands of real time examples. He also give the
    assignments regularly to improve our practical knowledge.”

    Jadeza

    “I joined Kyros Academy for Data-Science Training in Chennai. During the training assignments are really helped me to gain the practical knowledge and as well as to crack the interview. I really want to thank trainer, who has given me lot of
    encouragement, support as well as concern. So, friends i suggest u all to join Kyros
    Academy to make your life fruitful.”

    Rooban

    “Hi friends. First of all, I want to thank my trainer for training and guiding me in a right way to crack the interview. I have completed my Data Science Training now I am having an offer letter in my hand. Feeling very happy to get job within a short time. I did not even complete my course but I have job. So, I suggest you to join for
    your bright future in IT. It is the place, which can take you to a successful carrier.”

    Mihirkiran

    “Hello Everyone, I being the student of Kyros Academy would like to share few words. It is one of the BEST Institutes in Chennai. The technical knowledge that is been taught is very good and better than any of the Institutes. The Trainer takes care
    of each and every student, asks questions every day and ensures that the students
    are overwhelmed technically and up to date in studies. I am very much thankful to
    Kyros Academy-Data-Science Training in Chennai.”

    Padman

    “I joined Data-Science Training in Chennai. Overall care is been taken that the students are practically trained. Each and every concept will be taught in depth. I, myself have advised few of my friends to join Kyros Academy. Presently advising and
    will further keep advising. So, as usual I advice all who are wishing to join the Data
    Science course that Kyros Academy is the BEST Institute where people are technically
    empowered. All The Best………!!”

    Nagendra

    “Best Data-Science Training in Chennai. I was looking for best Data Science institution in Chennai where i can get quality training and good placement
    opportunities. I was searching online for Data Science training institutions and i was
    attending demo classes but none of them were satisfying. Then I came to Kyros
    Academy, I attended demo classes here also and it was very nice. So joined here for
    Data Science course.”

    Shailesh

    “I have joined Data-Science Training in Chennai. The training was in depth and intense. I learnt so many things in data science in depth and also i got placed in a good company from Kyros Academy itself with a good package, even before finishing
    my course. I am really thankful to the trainer for the way he taught us. Credit goes to
    him for making me expert in Data Science.”

    Murugan

    “Hello everyone, I would like to thank Kyros Academy for giving me placement. After joining this institute my knowledge has improved a lot. Here teaching is very good. The teaches Data Science in depth. Quality of teaching here is just too good.
    No other institute care their students as they does. Students here not only get
    quality education but also so many interview opportunity through this institute.
    Thank you.”

    Saumya

    “I Completed Data Science Training in Chennai. When i joined here they covered every topic with best examples in depth. They teach for every point an example that help to remember and also helped me to clear the interview, with that they gave
    assignment help me lot. They cover most of the examples belonging to the
    interviews. When i completed the course at that time I am very much confident on
    me that i can clear any interview. Now I got placed.”

    Kaviraj

    “I had completed my Data-Science Training in Chennai. I am very happy to say that I got placed through Kyros Academy-Data-Science Training in Chennai. From one of my senior I got to know about Kyros Academy(Data-Science Training in Chennai). After completing Data Science course I have positive reviews on
    them. I am really thankful to the trainer for his excellent teaching. I think this is the
    right place to learn Data Science. I am thankful to the trainer, who trained me and
    support I got and provided me opportunity to attend the interview.”

    Govindhan

    Join Data-Science Training in Chennai

    We in Kyros Academy have a team of professionally trained exclusive personnel from a various IT Companies to train in trending technologies, who have worked in MNCs and other reputed institutions, who deliver the training with loads of enthusiasm.
    Get your technology with enriched IT Professionals who train you on your skill needs & your career growth

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