Business Analytics

Frameworks

Analytics and visualization software

Machine learning algorithms

Upon completion of the CU, the student is expected to demonstrate abilities to:

- Understand the concept of Business Analytics;

- Know the main implementation frameworks for Business Analytics and a framework to identify an organization’s Business Analytics capabilities;

- Become familiar with various data visualization tools and analytics software;

- Know key machine learning algorithms;

- Identify and understand the applicability of different types of machine learning algorithms;

- Apply the knowledge acquired.

I. Business Intelligence vs. Business Analytics, and the role of Business Analytics in decision-making;

II. Core concepts and frameworks in Business Analytics, including descriptive, predictive, and prescriptive analytics;

III. Frameworks used in organizations to implement Business Analytics, including the CRISP-DM model and agile methodologies;

IV. Business Analytics technologies and tools: introduction to data visualization tools, analytics software, and machine learning algorithms;

V. Future trends.

Optional:

Vidgen, R., Kirshner, S. and Tan, F. (2019), "Business Analytics: a management approach”, Red Globe Press.

Mandatory:

Resources made available for each topic (mandatory reading/viewing) – please consult the schedule and topics in the class.

E-learning.

Continuous assessment is privileged: 2 digital written documents (e-folios) during the semester (40%) and a final digital test, Global e-folio (e-folio G) at the end of the semester (60%). In due time, students can alternatively choose to perform one final exam (100%).