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Big Data Analytics

CourseClass-Room TrainingOnline-Virtual Training What is Big Data Analytics? Big data analytics refers to the process of collecting, organizing and analyzing [...]
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    What is Big Data Analytics?
    Big data analytics refers to the process of collecting, organizing and analyzing large sets of data (“big data”) to discover patterns and other useful information. Not only will big data analytics help you to understand the information contained within the data, but it will also help identify the data that is most important to the business and future business decisions. Big Data analysts basically want the knowledge that comes from analyzing the data.

    Big Data Analytics and Apache Hadoop open source project are rapidly emerging as the preffered solution to address business and technology trends that are disrupting traditional data management and processing.
    Enterprises can gain a competitive advantage by being early adopters of big data analytics.

    Course Overview :
    The course provides a hands-on practitioner’s approach to the techniques and tools required for analyzing Big Data.The course is designed to enable students to: Become an immediate contributor on a data science team

    • Assist reframing a business challenge as an analytics challenge.
    • Deploy a structured lifecycle approach to data analytics problems.
    • Apply appropriate analytic techniques and tools to analyze big data
    • Tell a compelling story with the data to drive business action
    • Use open source tools such as R, Hadoop, and Postgres
    • Prepare for EMC ProvenTM Professional Data Scientist certification.

     

    • What is Big Data and Analytics
    • Challenges in handling today’s Big Data Analytics
    • Current industry trends and needs of Big Data Analytics

    • What is Hadoop – Brief history of Hadoop
    • Features of Hadoop, Hadoop v/s RDBMS
    • Hadoop Architecture and components:
    • Understanding Hadoop features
    • Learning the HDFS and MapReduce architecture

    • Installing R
    • Installing RStudio
    • Installing Hadoop
      • Understanding different Hadoop modes
      • Understanding Hadoop installation steps Hands-on:Basic HDFS Shell commands

    • Introducing RHIPE

    Installing RHIPE Understanding the architecture of RHIPE Understanding RHIPE samples

    • Introducing RHadoop

    Understanding the architecture of RHadoop Installing RHadoop Understanding RHadoopand RHiveexamples

    • Using Hadoop Streaming with R

    Understanding the basics of Hadoop streaming Understanding how to run Hadoop streaming with R Exploring the

    • HadoopStreaming R package Hands-on: Setup, configuration and testing of above components

    Simple manipulations; numbers and vectors Objects, their modes and attributes Arrays and matrices Lists and data frames Reading data from files Hands-on: Practice above topics

    Probability distributions Grouping, loops and conditional execution Writing your own functions Statistical models in R Graphical procedures R CRAN Packages Reading in Raw Data Sub-setting Data Factor Variables Using “Dummy” Coding for Categorical Variables in Regression Models Probabilities and Distributions Hands-on: Practice some of the above topics

    Importing data into R Exporting the data to Excel Understanding RHive operations Hands-on:Import sample data from Excel and Hive

    Understanding the data analytics project life cycle Identifying the problem Designing data requirement Preprocessing data Performing analytics over data Visualizing data Hands-on: Solving data analytics problems Exploring web pages categorization Computing the frequency of stock market change Predicting the sale price of blue book for bulldozers – case stud

    (Complete previous left over hands-on exercises, if any) Inferential Statistics Social Network Analysis Search and Text Analysis Gaussian Distributions, Other Distributions and The Central Limit Theorem Bayesian vs. Classical Statistics Probabilistic Interpretation of Linear Regression, and Maximum Likelihood Classification, Clustering and Dimensionality Reduction Collaborative Filtering and Recommendation Data Sciences with Text and Language Data Sciences with Location

    Trainer support – Over Phone at fixed duration Conference call – Twice in a week conference call with all the participants

    What is Machine learning? What is Data Mining and Predictive Analytic’s? Applications: Business, marketing and web problems solved with predictive analytics. Commonly used Data mining techniques Supervised and Unsupervised machine learning Recommendation technique and its application in real life Clustering technique and its application in real life Classification technique and its application in real life

    Complete remaining model development Discuss and resolve problems with the trainer Refine the design and code Deploy the project Design an alternate approach of solving the same assignment

    Course Assessment / Exam [2 hours] Present the solution to class and get feedback [4 hours] Interview preparation – Mock interviews [2 hours] Certificate distribution


    Class Room Training : Learn how we can shape your career by equipping yourself with Analytics and Big Data know how. Empower your talent pool to uncover powerful insights from your organization’s data.


    Online – Virtual Training : Learn how we can shape your career by equipping yourself with Analytics and Big Data know how. Empower your talent pool to uncover powerful insights from your organization’s data.

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