Introduction to Big Data
3.2 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
26 students enrolled

Introduction to Big Data

ICT
3.2 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
26 students enrolled
Last updated 6/2017
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
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This course includes
  • 37 mins on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Big Data, its characteristics and advantages
  • Big Data: Importance and associated risk
  • Hadoop: Features and Ecosystem
  • Hadoop: Architecture and Basic building blocks
  • MapReduce, and its process using an example
  • Various Open-source Technologies
  • Using Big Data for Analytics and Customer Experience Management
Requirements
  • Basic understanding IT terminologies related to data
Description

Big Data is not always about the volume of data, it is about the value that lies in that View of Data. Hence, big data is the amount of data which cannot be processed by the existing computational infrastructure. In much simpler terms, the amount of data which cannot fit into the RAM of your existing hardware for computational purposes, is referred to as big data for that hardware.
The course gives basic understanding and jargons of the data science. Some of the advantages of big data are:

  • Better Insights from Data
  • Better view of User behaviours
  • Helps in more accurate predictions
  • Helps in personalization at Scale
  • Saves a lot of time required for information extraction
  • Integrates both structured and unstructured information
  • Helps in better decision making
  • Helps in becoming more customer-centric

The course will help you understand the basic concepts on identifying the right data and make sense of Big Data.
Apache Hadoop is a framework designed to perform computations in a distributed fashion. It works on large clusters made up of commodity hardware connected by network. It is an Apache open source project under G N U Licenses. The framework is designed to process Big Data at a much higher speed than the existing Computational Setup.

The following features make Hadoop so lovable:

  • Open Source
  • Runs on Commodity Hardware
  • Fault Tolerant
  • Scalable
  • Distributed Processing

Participants will learn about Hadoop Architecture and some other open source technologies used for BIG Data processing. The course also touches topic like analytics through descriptive analytics, prescriptive analytics and predictive analytics.

Welcome to learn more on BIG DATA…

Who this course is for:
  • People interested to learn more about ICT Operations.
Course content
Expand all 8 lectures 37:10
+ Introduction
1 lecture 04:37

Part-1: The module covers the following:

  • What is not Big Data?
  • Why are we really talking about Big Data?
  • Key Trends for Data Explosion
  • Characteristics of Big Data
    • Volume
    • Velocity
    • Variety
    • Veracity
    • Value
Preview 04:37
+ Data
1 lecture 03:34

Part-2: The module covers the following:

  • Data
  • Types of Data
    • Structured
    • Numerical
    • Categorical
    • Semi-structured
    • Un-structured
  • Advantages of Big Data
  • Risk of Big Data
Preview 03:34
+ Hadoop
1 lecture 03:48

Part-3: The module covers the following:

  • What is Hadoop?
  • Hadoop Cluster
  • Features of Hadoop
  • Hadoop Ecosystem
  • HDFS
Preview 03:48
+ Hadoop (Part-2)
1 lecture 06:01

Part-4: The module covers the following:

  • HBase
  • MapReduce
  • YARN ( Yet Another Resource Negotiator
  • Pig
  • Hive
  • Sqoop
  • Flume
  • Zookeeper
  • Ambari
  • Oozie
  • Mahout
  • Spark
Hadoop (Cont.)
06:01
+ Hadoop (Part-3)
1 lecture 05:05

Part-5: The module covers the following:

  • Hadoop Architecture – Basic Building Blocks
  • YARN Infrastructure
  • Resource Manager
  • Node Manager
  • MapReduce
Hadoop Architecture
05:05
+ Open Source Technologies
1 lecture 08:18

Part-6: The module covers the following:

  • Open Source Technologies
    • R
    • Python
    • Apache Lucene
    • Elastic Search
    • Apache Solr
    • Cassandra
    • MongoDB
    • Neo4j
    • Apache Strom
    • Cloudera Impala
    • Pentaho
    • RapidMiner
  • Using Big Data for Analytics and Customer Experience Management
  • Descriptive Analytics
  • Prescriptive Analytics 
  • Predictive Analytics
Open Source Technologies and Analytics
08:18
+ Big Data and BI
1 lecture 03:11

Part-7: The module covers the following:

  • What is Machine Learning?
  • Difference between Big Data and BI
  • What Big Data adds to Analytics? 
  • Example Use Cases
Big Data, BI and machine learning
03:11
+ Introduction to Big Data: Summary
1 lecture 02:36

Part-8: The module covers the following: Summary

Course Summary
02:36
+ Assessment Quiz
0 lectures 00:00
Assessment Quiz: Introduction to Big Data
15 questions