The following modules will be covered (but will not be limited to) in the programme:
A. Exploratory Data Analysis
In this module we examine how to draw insights from data using simple tools. We also examine how to identify outliers in data and ways to deal with them.
B. Data Visualisation
In this module we learn different techniques of visualising data that can aid in taking effective management decisions.
C. Supervised Learning
This module discusses several methods of supervised learning namely Regression Analysis (Linear and Logistic), Decision Trees, Random Forests and Support Vector Machines. The advantages and disadvantages of these widely used methods for management decision making are discussed with several case studies.
D. Unsupervised Learning
This module discusses unsupervised learning techniques such as Clustering, Dimensionality Reduction and Anomaly detection with applications to management domain through case studies.
E. Forecasting
This module discusses several techniques that can be used in different situations for forecasting the quantities of interest in management decision context using case studies.
F. Optimisation
In this module we discuss optimal decision making in the presence of constraints. The Linear programming method is discussed and its applications to management decision making is highlighted using case studies.
G. Bayesian Data Analysis
In many problem situations managers have some information about the problem under study. Effective use of such information helps in making better decisions. In this module we discuss how Bayesian techniques can lead to better management decision making use of the available prior information.
H. Unstructured Data Analysis
In many situations in real life we deal with data that is not structured. One area of prime interest where we encounter unstructured data is when we have to deal with text data such as when dealing with social media comments data. In this module we discuss text data analysis and its application for making better management decisions.
I. Data Analysis Project
The participants will have to work on a data analysis project as a group and will have to make a presentation on the last day of the programme.
07 September 2022 to 25 September 2022
Online
Online
120,000
