Business Intelligence & Analytics

By Prof. Saji K Mathew   |   IIT Madras
Learners enrolled: 11228   |  Exam registration: 2948
This course equips students with necessary knowledge and skills on the thought process, modelling approaches and tools required to use data from the enterprise databases and other sources for business decisions. In turn, the course prepares participants for a career in data science, business analytics and market research. This course will introduce the context of data mining, and cover important modelling techniques such as regression, decision trees, clustering, ANN and text mining.

PREREQUISITES: A core course on Business statistics desirable

INDUSTRY SUPPORT: Analytics and data science industry, IT services industry, Manufacturing and services operations and marketing
Course Status : Ongoing
Course Type : Elective
Duration : 12 weeks
Category :
  • Computer Science and Engineering
  • Data Science
Credit Points : 3
Level : Postgraduate
Start Date : 22 Jan 2024
End Date : 12 Apr 2024
Enrollment Ends : 05 Feb 2024
Exam Registration Ends : 16 Feb 2024
Exam Date : 27 Apr 2024 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction to Business Intelligence & Analytics (BIA), drivers of BIA, types of analytics: descriptive to prescriptive, vocabulary of business analytics, course plan and resources

Books to refer : Text 1: Han et al. (2023) Chapter 1, Introduction

Week 2: 
Technical architecture of BIA, case analysis of AT&T Long distance, fundamentals of data management, OnLine Transaction Processing (OLTP), design process of databases

Books to refer : Text 1: Han et al. (2023) Chapter 4, Data Warehouse and Online Analytical Processing (pp. 85-108)

Week 3: 
Relational databases, normalisation, SQL queries, ShopSense case of management questions, data warehousing, OnLine Analytical Processing (OLAP), data cube

Books to refer : Tutorial: SQL tutorial on MySQL (https://www.mysqltutorial.org)

Week 4: 
Descriptive analytics, and visualization, customer analytics, survival analysis, customer lifetime value, case study

Books to refer :
a. Knowing When to Worry: Using Survival Analysis to Understand Customers: https://learning.oreilly.com/library/view/data-mining-techniques/9780470650936/9780470650936c 10.xhtml#c10_level1_1
b. Customer Lifetime Value (CLV): A Critical Metric for Building Strong Customer Relationships,

Week 5: 
Data mining process, introduction to statistical learning, data pre-processing, data quality, overview of data mining techniques, case study using regression analysis

Books to refer : 
a. Text 2: James et al. (2013) Chapter 1, Statistical learning, ISL
b. Text 2: James et al. (2013) Chapter 2, Linear regression, ISL

Week 6: 
Introduction to classification, classification techniques, scoring models, classifier performance, ROC and PR curves

Books to refer : Text 1: Han et al. (2023) Chapter 6, Classification: Basic concepts and methods

Week 7: 
Introduction to decision trees, tree induction, measures of purity, tree algorithms, pruning, ensemble methods

Books to refer : Text 2: James et al. (2013) Chapter 8, Tree- based models

Week 8: 
Tree implementation in Python: problem of targeted mailing

Books to refer :  
a. https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics
b. https://scikit-learn.org/stable/visualizations.html

Week 9: 
Cluster analysis, measures of distance, clustering algorithms, K-means and other techniques,
cluster quality

Books to refer :  Text 2: James et al. (2013) Chapter 10, Unsupervised learning (pp. 385-400)

Week 10: 
A store segmentation case study using clustering, implementation in Python, profiling clusters, cluster interpretation and actionable insights, RFM sub- segmentation for customer loyalty

Books to refer : What Is Recency, Frequency, Monetary Value (RFM) in Marketing?:

Week 11: 
Machine learning, Artificial Neural Networks (ANN), topology and training algorithms, back propagation, financial time series modelling using ANN, implementation in Python

Books to refer : Kaastra & Boyd (1996) Designing a neural network for forecasting financial and economic time series, JNC:

Week 12: 
Text mining, process, key concepts, sentiment scoring, text mining using R-the case of a movie discussion forum, summary

Books to refer : Silge and Robinson, Text Mining with R, A Tidy Approach: O’reilly:

Books and references

Text 1: Han, J., Pei, J. & Tong H. (2023). Data Mining Concepts and Techniques, 4th ed,
New Delhi: Elsevier.

Text 2: James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to
Statistical Learning with Applications in R, Springer: NY

Data sources
▪ “Adventure Works Cycles”, SQL Server sample database
▪ “Retail Sense transaction data”, real life data of a fashion retailer
▪ UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
▪ Financial/capital market data: Yahoo! Finance
▪ Text data: www.twitter.com
▪ ISL resources: http://www-bcf.usc.edu/~gareth/isl/
▪ Kaggle: www.kaggle.com

Resources for Learning Python & R

Participants need to develop proficiency in statistical programming while doing this course. This
course gives the flexibility to learn either one of them or both. Scripts required to solve class work
will be provided in both the languages. I give below guidelines to install and get started with both
the languages.

I. Installing and Using Python and Jupyter Notebook
You may install anaconda distribution of Python 3.7 for your machine and OS
(Windows/MacOS/Linux) from Anaconda website:

After installation, click on the “Anaconda Navigator” to launch Jupyter Notebook.
In Jupyter Notebook you may browse “New”, “Python 3” to start a new window for writing codes.

You may separately install the following packages required for the course:
a. Graphviz: Follow the guidelines here to install Graphviz for your OS:

b. Yellowbrick: Follow guidelines here to install yellow brick in your OS:

c. Category encoders: https://contrib.scikit-learn.org/category_encoders/

II. Python learning resources
I suggest the following resources to get started with Python programming and then data analysis
using Python:

▪ Full Stack Python (An aggregator site for Python learning resources): https://www.fullstackpython.com/best-python-resources.html
▪ Best Python videos (An aggregator site for Python video learning resources):
▪ Machine learning in Python: https://scikit-learn.org/stable/index.html
▪ Anaconda resources: https://www.anaconda.com/library
▪ Coursera: https://www.coursera.org/courses?query=python

III. Installing and Using R
latest version of R for your machine and OS.
Visit https://rstudio.com/products/rstudio/download/ for guidelines to install the latest free
version of RStudio for your machine and OS.
Now open RStudio and run the following command to install the packages required for the course:
If you encounter errors, try install each package separately using install.packages() command.

IV. R learning resources
I suggest the following resources to get started with R programming and then data analysis using R : 

1. W. N. Venables, D. M. Smith, An Introduction to R:
2. Julia Silge and David Robinson, Text Mining with R, A Tidy Approach, O’reilly:
3. David Romney, Online resources for learning R:
6. Coursera: https://www.coursera.org/learn/r-programming

V. Installing and Using MySQL with Workbench
https://www.mysqltutorial.org (SQL tutorial for MySQL database)

Instructor bio

Prof. Saji K Mathew

IIT Madras
Prof.Saji K Mathew is currently a Professor at the Department of Management Studies, Indian Institute of Technology Madras, India. As a Fulbright Scholar, he did his post-doctoral research on offshore IT outsourcing at the Goizueta Business School of Emory University, Atlanta (USA). His current research focuses on behavioral cyber security, information privacy, misinformation and digital nudging. He has published research in leading IS journals while also making editorial contributions to some of them. He is a founding member of the Association for Information Systems India Chapter (INAIS) and presently serves as its Vice President.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 27 April 2024 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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