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Data-Science Training in Chennai provided by Certified Professionals. We are the Best Data-Science Training Institute in Chennai. 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.
-
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
- Working as an expert with cloud based companies
- Trained 100+ Students so far.
- Strong Theoretical & Practical Knowledge
- Data science certified Professionals
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
Statisticians looking to improve their Big Data statistics skills
Developers wanting to learn Machine Learning (ML) Techniques
Information Architects looking to learn Predictive Analytics
Those looking to take up the roles of Data Scientist and Machine Learning ExpertWhat 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.
Get your technology with enriched IT Professionals who train you on your skill needs & your career growth -
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“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
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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.
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