Big Data Analytics Training

Big Data Analytics Syllabus

Introduction Data Analytics – I (Day 1)

  • What is Data Analytics- an Overview?
  • Importance of Statistics in the field of Data Analytics
  • What is Big Data and why is so important?
  • Couple of concepts a Data Analyst should know: (A)
    • 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

Introduction Data Analytics – II (Day 2)

  • Analytics and scopes;
  • Over View of Text/Web analytics;
  • Hypothesis framing & Testing;
  • Couple of concepts a Data Analyst should know: (B)
    • T- test (1 tail and Paired sample);
    • Z test
    • F Test
    • Anova
    • (For all concepts comes under “Couple of concepts a Data Analyst should know: (B)” will be a combination of concepts as well as practice session in “R”)

Building a Marketing Mix Model – I (Day 3)

  • 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 (Day 4)

  • 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 (Day 5)

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

Classification Technique – II (Day 6)

  • K-Mean cluster by using “R”;
  • Hierarchical Cluster by using “R”
  • Text Mining for beginners with “R”

Credit Risk Modelling using Logistic Regression (Day 7)

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

Detail level Concepts You Should Know: (Day 8)

  • 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.
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