Big Data Analytics

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This training gives you a clear understanding about how and where to use applications of data analytics who are new into the industry and aspiring their carrier in data analytics.

Code Titre Durée Prix HT
bigdata01 Big Data Analytics 5 jours Nous consulter


At end of this Big Data Analytics training you will be able to,

  • Learn the popular techniques of data analytics
  • Gain confidence to face interview for data analyst role
  • Hands on experience on "R" and other tool like SPSS and Mini Tab, Tableau etc.


Focused more on deliver the popular concepts of data analytics and make the participant confident enough to go and claim his deserving role in this field. IT Professionals Big Data Hadoop Professionals


Knowledge in computers and understanding about basic level statistical concepts are good to have



All the concepts are delivered in theoretical manner as well as hands on experience in statistical tool "R".


Introduction Data Analytics – I

  • What is Data Analytics- an Overview?
  • Importance of Statistics in the field of Data Analytics
  • What is Big Data and why is so important?

Introduction Data Analytics – II

  • Analytics and scopes
  • Over View of Text/Web analytics
  • Hypothesis framing & Testing

Building a Marketing Mix Model – I

  • Deliver the concept of Linear and Multiple Regression analysis
  • End to end concept of how to build a marketing mix model using regression
  • Model Validation technique

Building a Marketing Mix Model – II

  • Hands on experience on regression analysis and prediction techniques using "R"
  • Deliver the Concept and application of association technique Market Basket Analysis

Classification Technique - I

  • Classification and Segmentation
  • Rule based classification
  • K-mean
  • Principle Component Analysis
  • Hierarchical Cluster

Classification Technique – II

  • K-Mean cluster by using "R"
  • Hierarchical Cluster by using "R"
  • Text Mining for beginners with "R"

Credit Risk Modelling using Logistic Regression

  • End to end concept of Logistic Regression and the application
  • Credit risk modelling (PD/EAD/LGD)

Detail level Concepts You Should Know:

  • Measure of central tendency (Mean/Median/Mode)
  • Standard Deviation
  • Skewness and Kurtosis
  • Different types of Graph and their usage
  • Different types of data types
  • Co-relation etc.
  • Type I and Type II error
  • T-test (1 tail and Paired sample)
  • Z-test
  • F-Test
  • Anova
  • Outlier checking and treatment
  • Concept of Best fit regression line
  • Concept of CEM and CEM touch points
  • Concept of NPS metrics
  • Concept of Survey design and best practices
  • Concept of Customer life time value
  • IV calculation for score card preparation in Logistic regression etc.


Statistical tool "R"


"R", Regression, Hadoop, Stats