Data Science Training

At Magento Guru, we believe in providing a full-fledged Data science Training  course in delhi of your desire where our industry experts have designed a top-notch curriculum just for you.

Data science Training by Expert. Data science it is a software here distributing and processing the large set of data into the cluster of computers. This Course is designed to Master yourself in the Data Science Techniques and Upgrade your skill set to the next level to sustain your career in ever changing the software Industry.This Course covers from the basics of Data Science to Big Data Hadoop, Python, Apache Spark etc

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis, Data Mining, and Machine Learning
  • Analytics vs Data Science
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • 45 Working days, daily 1.30 hours
  • Basis of  Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data and Sources
  • Data Quality, Changes and Data Quality Issues, Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?
  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture, Technologies, Challenge and Big Data Requirements
  • Big Data Distributed Computing and Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem
  • What is Data Science?
  • Why are Data Scientists 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 and Data Science Project Life Cycle & Stages
  • Map-Reduce Framework
  • Hadoop Ecosystem
  • Data Acquisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats, Quantity and Data Quality
  • Resolution Techniques
  • Data Transformation
  • File Format Conversions
  • Anonymization
  • 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
  • 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
  • Various phases of Data Cleaning
  • Functions used in Inspection
  • Data Cleaning Techniques
  • Uses of functions involved
  • Use-cases for Data Cleaning using 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
  • 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
  • Storytelling 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
  • 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
  • Hadoop Cluster and 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
  • What is Map Reduce?
  • Why Map Reduce?
  • Map Reduce in real world  and Map Reduce Flow
  • What is Mapper,  Reducer, and Shuffling?
  • Word Count Problem
  • Hands-On Exercise
  • Distributed Word Count Flow and Solution
  • Log Processing and Map Reduce
  • Hands-On Exercise
  • 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 and Software?
  • Hadoop Log Files?
  • Hadoop 1.0 Challenges
  • NN Scalability, SPOF, and 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
  • 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
  • Joining Data Sets in Pig
  • Hands-On Exercise
  • Splitting Data Sets
  • Hands-On Exercise
  • Hive Fundamentals and 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
  • 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
  •  
  • CAP Theorem
  • HBase Architecture and concepts
  • Introduction to HBase
  • Client API’s and their features
  • HBase tables The ZooKeeper Service
  • Data Model, Operations
  • Programming and Hands on Exercises
  • 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
  • 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
  • Social Media Final Project
  • Hadoop Project
  • Objective
  • Problem Definition
  • Solution
  • Discuss datasets and specifications of the project