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Business Analytics Course

Home Course Business Analytics Course
Business Analytics Course

Business Analytics Course

Course Duration: 4 Months & 1 Months Intern

Weekly: 2 Days ( 8.00 PM)


Basic knowledge of programming

Laptop/Desktop with Internet Connection

8-10 hours of commitment to learning per week

Introduction to Business Analytics


The program is designed to enable the participants to diagnose business problems captured in different types of data, conduct an in-depth analysis of the data using statistical and visualization tools, and understand how to generate business insights by leveraging Machine Learning. The course consists of Theory (40%) and Practical (60%), comprising of Industry-basedUse Cases.

Delivery Mode: Virtual Instructor-led / Classroom-based

Prerequisites: High-school mathematics | Laptop/Desktop with internet connection

Tools: Jupyter Notebook / R Studio, Tableau

Python / R packages: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn / dplyr, data.table, ggplot2, janitor, caret, mlr


1.     Introduction to Business Analytics

a.      Organizational role of Business Analytics

b.     Knowledge Areas, Planning and Monitoring

c.      Business Understanding and Strategy Analysis

d.     Analyzing Requirements and Evaluating Solution

e.      Solution Performance Measurement and Enterprise Limitations Assessment

2.     Statistical learning in decision-making 

a.      Fundamentals of applied probabilities

b.     Sampling and inferring data

c.      Decision-making under uncertainty

d.     Regression techniques: Capturing relationships among variables of interest

3.     Introduction to Data Analysis tool(Elective) 

                      i.     Python

a.      Installation of Python working environment

b.     Datatypes and data structures

c.      Python Programming Basics

d.     Classes, objects, attributes & methods

e.      ML ecosystem: NumPy, Pandas, Matplotlib, Scikit-Learn

                    ii.     R

a.      Getting started with R Studio

b.    Basic database data structures in R

c.      R programming basics

d.     Working with R data frames

e.      Visualization in R

f.      ML ecosystem

4.     Understanding data and descriptive analytics 

a.      Data extraction and cleaning

b.     Interpretation of a statistical summary of data

c.      Dimensionality and outlier analysis


5.     Exploratory Data Analysis using Tableau

a.      Introducing Tableau

b.     Loading different types of data in Tableau

c.      Different types of charts in Tableau

d.     Working with Time Series

e.      Filters in Tableau

f.      Working with Geographical Locations and Maps

g.     Dashboard


6.     Predictive Analysis for decision-making

a.      Role of Artificial Intelligence and Machine Learning in BA

b.     Supervised and unsupervised learning in decision-making

c.      Combining human expertise with data-driven intelligence for decision-making


7.     Case study Analysis

a.      Retail case study

b.     Finance case study