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ISB Hyderabad - ISB - Applied Business Analytics 

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ISB - Applied Business Analytics
 at 
Emeritus 
Overview

Gain a hands-on approach to understand different types of analytics and their uses to make informed data-driven business decisions

Duration

12 weeks

Start from

3 days to go

Total fee

1.12 Lakh

Mode of learning

Online

Official Website

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Credential

Certificate

ISB - Applied Business Analytics
 at 
Emeritus 
Highlights

  • Earn a certificate after completion of course from ISB
  • 108 Pre-recorded Videos for Self-paced Learning from Top ISB Faculty
  • Live Masterclasses on Generative AI, Big Data, Cloud Computing, and More
  • Fee payment can be done in installments
Read more
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ISB - Applied Business Analytics
 at 
Emeritus 
Course details

Who should do this course?

Candidates interested in leveraging business analytics to outpace the competition and develop data-driven growth strategies

Consultants seeking to sharpen their strategy skillset by using the latest data-backed frameworks so that they can provide their clients with data-driven solutions to their problems

Individuals looking to learn more about data analytics to manage data science and analytics teams, and improve functional performance through analytics

What are the course deliverables?

Leverage data driven growth strategies

Describe how to collect and prepare data for analyses

Analyse data using analytics tools and gather business insights

Use data-driven decision-making to make more informed business decisions

Test whether your analyses confirm your hypothesis

Describe ways to apply ML techniques in your work to solve business problems

More about this course

The Applied Business Analytics programme from ISB Executive Education is a must for anyone looking for a high-growth career in data analytics

This high-impact programme is designed to provide a hands-on experience of key machine learning models and techniques to gather insights or predict outcomes

The program is designed not only to explain what each model does or functions but also explores how businesses use them, whether it is to gather insights, solve problems, or predict outcomes

Participants will gain a hands-on approach to understanding different types of analytics and their uses to make informed data-driven business decisions

ISB - Applied Business Analytics
 at 
Emeritus 
Curriculum

Introduction to Business Analytics

What Is Business Analytics

Problem Formulation for Data Analytics

Evolution of Machines

What Is Artificial Intelligence

Machine Learning Techniques

What Is Supervised Learning

What Is Unsupervised Learning

 

Data Preliminaries for Analytics

Data and Measurement Basics

Introduction to Data Dichotomies

Data Dichotomies

Introduction to Data Types

Debrief for the Scales of Measurement Activity

Data Preprocessing for Analytics Using the Data-Preproc App

 

Leveraging Generative AI for Business Analytics
What are Large Language Models (LLMs)

The Power of Generative AI in Data Analysis

Practical Applications of Generative AI in Business

Definitional Preliminaries

Anatomy of a [Simple] Prompt

Prompting Text Summarisation

Prompting Reasoning

RTFC Framework for Prompt Design

Zero-Shot Prompting: Types & Examples

One-Shot Prompting: Types & Examples

Image-Analytics with Generative AI Assist

Graph-Analysis with Generative AI

 

Introduction to Statistics and Hypothesis Testing

Introduction to Statistics and Hypothesis Testing

Quick Introduction to Probability

Introduction to Normal Distribution

Practical Example of Use of Normal Distribution

Sample Vs Population

Statistical Inference

Unknown Population Standard Deviation

Two Types of Errors

Hypothesis Testing

One Sided Test

Summary

 

Decision Trees - Descriptive and Predictive Analytics

Predictive Analytics: Decision Trees

What is Decision Tree

Decision Trees Example for Prediction

Decision Trees: Demo?

Depth of the Tree

Prediction Using Decision Tree

Decision Trees for Metric or Numerical Outcome

 

Digonostic Analytics - Regression Analysis

Regression Analysis

ML Approaches in Business Analytics

Regression: Motivating Example

Input Data and Summary Stats

Select Dependent (Y) and Independent (X) Variables?

Regression Demo: Interpretation

Regression Demo: Evaluating the Model

Zero Code Learning Demo

Regression Variant

Interaction Effects?

Regression Analysis: Summary


Diagnostic Analytics - Logistic Regression and Classification Analysis

Classification Analysis

Logistic Regression Overview

Why Not Use Regression

Logistic Regression Example: Predicting Heart Disease

Interpreting beta

Classification/Confusion Matrix

Multiple Classes

Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC)

Multiple Classes??


Unsupervised Learning - Factor Analysis

Prelims: The WHAT of Factorising Data

Factor-An: Toothpaste Dataset

Factor Analysis of a Real Dataset: mtcars

Factor Analysis on OTT Genres

Generative AI Assist for the Toothpaste Example


Unsupervised Learning - Cluster Analysis

Cluster Analysis Overview

Grouping Objects

Clustering Algorithms

Clustering and Principal Component Analysis (PCA)

Clustering Visualisation


Visualisation - Perceptual Mapping for Business Analytics

Positioning Statements

Elements of a Positioning Statement

Example: Positioning Statement for SouthwestPerceptual Maps and JSMs

Guide to Read JSM Plots

Generative AI Assist on Pmaps and JSMs

Cities: Perception and Preference Analysis


Unstructured Data - Text Analytics

The WHY and HOW of Text-An

Text-Analysing a Review Corpus ?

Interpreting App Output with Generative AI Assist?

Keyword-Based Corpus Filtering?

The Need for a Keyword Filtering App?

Elementary Sentiment Mining? ???

Four Tidytext Sentiment? Dictionaries

Text Analysis with PDF and Generative AI Assistance


Network Analysis

Network Properties

Social Network Analysis (SNA) Examples

SNA with Real Data

SNA Demo: Network-Prep and Basic-Network App

Social Networks in Affiliation Space

Text Network and Recommendation Analysis Application

 

Prescriptive Analytics - Causal Inference and Experiments

Introduction

Understanding Causal Inference

Gold Standards to Establish Causality (Experiments)

Casual Interference

Analysing Experimental Data and Drawing Inferences

Analysing A/B Tests: Electricity Pricing

Experimentation Using ML: Ad Targeting Example


Capstone
Capstone assignment
Faculty Icon

ISB - Applied Business Analytics
 at 
Emeritus 
Faculty details

Prof. Manish Gangwar
Prof. Gangwar is the Executive Director of the Institute of Data Science and Business Analytics programme at ISB. He holds a PhD in Management Science from the University of Texas at Dallas. His research interests include exploring marketing, product, and technology issues using quantitative models. His research articles have been published in leading academic journals, books chapters, and popular media. He was also recognised as one of the most prominent data science academicians in India.
Prof. Sudhir Voleti
Prof. Voleti has worked in the industry in different capacities, from a management consultant to a software analyst. He holds a PhD in Marketing from the University of Rochester.

ISB - Applied Business Analytics
 at 
Emeritus 
Entry Requirements

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  • N/A
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ISB - Applied Business Analytics
 at 
Emeritus 
Admission Process

    Important Dates

    Dec 24, 2024
    Course Commencement Date

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    ISB - Applied Business Analytics
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