Big Data Analytics

Course Overview:

Big Data Analytics Online Course provided is mainly aimed at enhancing the subject skills of the aspirants to such a high extent that they can effectively handle any sort of complex challenges that might face during their professional life as a  Big Data Analytics professional. You can learn how to get acquaintance with the functioning model of Big Data Analytic

Course Content:

Introduction

♦ What is Data Analytics- an Overview?

♦ Importance of Statistics in the field of Data Analytics

♦ What is Big Data and why is so important?

♦ A couple of concepts a Data Analyst should know: (A)

The 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

♦ Analytics and scopes;

♦ Overview of Text/Web analytics;

♦ Hypothesis framing & Testing;

♦ A couple of concepts a Data Analyst should know: (B)

The 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 a practice session in “R”)

Building a Marketing Mix Model - 1

♦ Deliver the concept of Linear and Multiple Regression analysis;

♦ End to end the concept of how to build a marketing mix model using regression;

♦ Model Validation technique

Building a Marketing Mix Model – 2

♦ Hands-on experience on regression analysis and prediction techniques using “R”

♦ Deliver the Concept and application of association technique Market Basket Analysis

Classification Technique - 1

♦ Classification and Segmentation;

♦ Rule-based classification;

♦ K-mean

♦ Principle Component Analysis

♦ Hierarchical Cluster

Classification Technique – 2

♦ 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 the concept of Logistic Regression and the application

♦ Credit risk modeling (PD/EAD/LGD)

Detail level Concepts You Should Know

♦ Outlier checking and treatment;

♦ Concept of Best fit regression line;

♦ Concept of CEM and CEM touchpoints;

♦ Concept of NPS metrics;

♦ Concept of Survey design and best practices;

♦ Concept of Customer lifetime value;

♦ Calculation for scorecard preparation in Logistic regression etc.

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