telxperts telecom trainings

Big Data Analytics Certification Course (Overview)

£695.00

Location: Online

Duration: 2 Days

From-To: 2016-12-05 to 2016-12-06

Product Description

Course Overview

Services like web analytics, intelligent e-commerce and social networks often require managing big scale data compared to a traditional database. As the data scale and storage demand increase, the complexity of the database also increases. Big data systems use large number of machines that work in parallel for data storage and data processing. Big Data Analytics use a clustered hardware and advanced techniques to capture and analyze the large web-scale data efficiently. This approach describes a scalable and easy to technique to be built and run by a small team. Big data analytics process examines large data sets of various data types to uncover hidden patterns, market trends, correlations, customer preferences and other business information. The big data analytical findings allow more effective marketing, better customer services, new revenue opportunities, improved operational efficiency, and competitive business advantages and benefits.

This course provides deep understanding to Big Data dimensions and business effects utilizing big data analytics. This course provides the knowledge and training to use new Big Data tools and techniques, information storage for efficient processing and analysis of business decision-making, and analyzing massive amounts of unstructured data. The attendees will also learn about Hadoop data management for complex data processing.

Key Benefits for Participants

This course will provide the following key benefits to the attendees:

  • Understand the Analytics methodology, establishing business importance using Big data and integrating Big data with traditional Data
  • Understanding Basic Analytic techniques and Analyzing Data Characteristics
  • Deep insight to Big Data Storage and implementing polyglot data storage solutions
  • Learn about Online Business Performance Analysis and Predictive Modeling Techniques
  • Discuss Hadoop Data management for Complex processes
  • Understanding Hadoop Performance Analytics
  • Understanding Big Data System Implementation
  • Hands on training on end-to-end analytics using Hadoop and Tableau

Course Objectives

This course has the following major objectives:

  • Provide comprehensive knowledge about Big Data Fundamentals, Analytics tools and Techniques and Business benefits of Big Data Analytics
  • Deliver insights about Data Exploration, Preparation and Visualization using R Analytic Technique
  • Provide detailed insight to Analyzing and selecting data sources
  • Detailed insights to Modelling techniques for Linear and Logistic Regression and Model Validation
  • Comprehensive knowledge about Hadoop Distributed File System, Hypertable and Big data Table
  • Provide deep understanding of Hadoop Ecosystem, Cloudera Distribution and Online Business Analysis
  • Explain Complex data management and Processing and Performance Analysis using Hadoop in detail
  • Providing insight to implementation of Big Data Systems
  • Practical training on End-to-End Big Data Analytics using Hadoop and Tableau

Course Outline

In “Big Data Analytics Certification Course (Overview)”, we cover the fundamentals of the LTE, VoLTE and RCS technology, while in “Big Data Analytics Certification Course (Comprehensive)” we cover the following course contents:

Introduction to Analytics

  • What is Analytics?
  • Popular Tools
  • Role of Data Scientist
  • Analytics Methodology
  • Problem Definition

Statistical Concepts and their application in business

  • Descriptive Statistics
  • Probability Theory
  • Tests of significance
  • Non-parametric testing

Delivering business benefit from Big Data

  • Establishing the business importance of Big Data
  • Addressing the challenge of extracting useful data
  • Integrating Big Data with traditional data

Basic Analytic Techniques

  • Introduction to R
  • Data Exploration with R
  • Data Preparation with R
  • Data Visualization with R

Analyzing your data characteristics

  • Selecting data sources for analysis
  • Eliminating redundant data
  • Establishing the role of NoSQL

Overview of Big Data stores

  • Data models: key value, graph, document, column–family
  • Hadoop Distributed File System
  • HBase
  • Hive
  • Cassandra
  • Hypertable
  • Amazon S3
  • BigTable
  • DynamoDB
  • MongoDB
  • Redis
  • Riak
  • Neo4J

Selecting Big Data stores

  • Choosing the correct data stores based on your data characteristics
  • Moving code to data
  • Implementing polyglot data store solutions
  • Aligning business goals to the appropriate data store

Predictive Modeling Techniques

  • Linear Regression
  • Logistic Regression
  • Cluster Analysis
  • Decision Trees
  • Time Series Analysis

Model Validation

  • Model Validation
  • Creating Insights from Statistics
  • Online Resources
  • Connecting with the Analytics Community

Online Business Performance

  • What is Big Data?
  • Why Hadoop?
  • Hadoop and Map Reduce Overview
  • Hadoop Ecosystem
  • Cloudera Distributions

Hadoop Data Management

  • Hadoop Installation
  • Introduction to HDFS
  • Introduction to HBASE
  • Hive Data Management
  • Sqoop and Flume Overview

Processing Complex Data using Hadoop

  • Hadoop Streaming
  • Data Analysis using Pig
  • Data processing using Impala
  • Text Processing using Hive

Performing Analytics on Hadoop

  • R Streaming on Hadoop
  • Introduction to RHadoop
  • MapReduce Programming in R
  • Tableau for Hadoop

Implementing a Big Data Solution

  • Selecting suitable vendors and hosting options
  • Balancing costs against business value
  • Keeping ahead of the curve

Big Data Case Studies

  • Handling Unstructured Data
  • End-to-End Analytics using Hadoop
  • Case Studies with RHadoop
  • Visualizations with Tableau

Who Should Attend

  • Managers and team members of of business intelligence (BI), analytics, and big data professionals
  • Business and Data Analysts professionals who want to learn about big data analytics and add that to their skills.
  • Data and database professionals interested in exploitation of their analytic skills in a big data environment
  • Anyone with similar related discipline looking to move into the world of Data Science and big data.
  • Data analysts, program and project managers, executives, programmers, architects, system administrators and data analysts who want a sound technical and management overview of the key elements  needed to understand, manage and analyze Big Data

Delivery Options

  • Online
  • Onsite

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