ABOUT THE COURSE: 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 desirableINDUSTRY SUPPORT: Analytics and data science industry, IT services industry, Manufacturing and services operations and marketing
Business Intelligence & Analytics
Indian Institute of Technology Madras and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
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,
https://www.gartner.com/en/digital-markets/insights/what-is-customer-lifetime-value
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, ISLb. 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?:
https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp
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:
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bcbb8ca9d6a6ce6017710ebf6143da76b6edf98b
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:
www.tidytextmining.com/index.html
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,
https://www.gartner.com/en/digital-markets/insights/what-is-customer-lifetime-value
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, ISLb. 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?:
https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp
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:
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bcbb8ca9d6a6ce6017710ebf6143da76b6edf98b
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:
www.tidytextmining.com/index.html
Taught by
Prof. Saji K Mathew
