Course

Module 1

Module 1: Able to recall the contents of the IBA course, regression models and how to check forthem (6 hours)

1. Introduction to the course, structure & evaluation components.
2. Review of topics covered in the IBA course.
3. Latest version of Gartner’s magic quadrant.
4. Review of Linear Regression& Multiple regression
5. Tackling the problem of Multi collinearity; Use of dummy variables in regression.
6. Use of interaction terms in regression
7. Logistic regression.

Module 2

Module 2: Able to understand Data Warehousing and analysis of DW designs and build various Data Warehousing models (6 hours)

1. OLTP vs OLAP, Data warehousing fundamentals, dimension tables and fact tables, schemas (star,snowflake, galaxy).
2. Multidimensional analysis, OLAP architectures (ROLAP,MOLAP, HOLAP).
3. Multidimensional analysis –pivoting, use of pivot tables ,data pre-processing, hands on practice.
4. Data warehouses – characteristics and goals, different approaches to build data warehouses, Data warehousing – Extraction, Transformation and Loading.

Module 3

Module 3: Able to understand basic concepts of Data Mining & install, open and do basic processes with Weka (6 hours)

1. Introduction to data mining, success stories.
2. Different learning schemes in data mining applications.
3. Introduction to Weka, arfffile format.
4. Introduction to classification using Weka.

Module 4

Module 4: Filtering Techniques in Data Mining (6 hours)

1. Filtering techniques.
2. Test options – difference between various options.

Module 5

Module 5: Able to understand various algorithms, hierarchy, trees in Data Mining (6 hours)

1. Classification algorithms –ZeroR and OneR, Discretization, over fitting.
2. Naive Bayes algorithm, zero frequency problems, tackling missing values.
3. Decision trees, constructing and visualizing decision trees, J48 algorithm, computing information gain, concept of entropy, Gini impurity index.
4. Ensemble methods – Bootstrap aggregation, random forest algorithm, up sampling, down sampling, SMOTE, boosting and stacking.
5. Mining association rules, concepts of support and confidence, the apriori algorithm.
6. Clustering techniques (hierarchical and non-hierarchical).

Course Description

The proliferation of Information Technology tools in organizations has not only created new opportunities, but also is posing newer challenges. For instance, a major challenge for managers is no longer the collection of data but analysis of the vast amounts of data that are available. The availability of low-cost massive data storage technologies and Internet connections have made available large amounts of data that have been collected and accumulated by the various organizations over the years. The challenge is how to make use of the available data to make better decisions. The organizations that can do this better stand to gain. The demand for managers with such skills is also high.

Business Analytics helps managers to leverage value from data. Business Analytics is an umbrella term that subsumes technologies, applications and practices for the collection, integration, storage, access, analysis, and presentation of business information to help users make better decisions. Business Analytics can give competitive advantage to organizations – but there are a lot of factors that contribute to successful implementation of analytics in an organization.

Course Outcomes& Learning levels

This course builds on the first year core course ‘Introduction to Business Analytics” and aims to impart learning that is required for a student to be a competent business professional in the business analytics field. The course seeks to develop knowledge and skills at the end of this course, the student will be able to:

1. Demonstrate understanding of the theoretical underpinnings of various business analytics techniques(L2)
2. Evaluate, process and analyze datasets by applying appropriate analytics techniques on them (L5)
3. Perform a literature review on the latest literature in one chosen analytics domain (L5)
4. Draw meaningful conclusions from the results of applying various analytics techniques (L6).

Evaluation Pattern

1. Hindle, G., Kunc, M., Mortensen, M., Oztekin, A., & Vidgen, R. (2020). “Business analytics: Defining the field and identifying a research agenda”. European Journal of Operational Research, 281, 483–490.