Analítica Empresarial

Objectives

The learning objectives or outcomes consist of: (1) the correct understanding of the context, and how to identifying and constructing a business problem; (2) the development of the most suitable models to solve the problem; (3) applying the appropriate software for the model resolution; and (4) writing a report for presenting the description of the problem, the model it-self, the chosen BA solving technique, and provindig conclusions to support the decision making process.

Syllabus

1. Introduction a) What does it mean, Business Analyics? b) A “Systems Thinking” perspective in Business Analytics c) General overview of the different approaches in Business Analytics d) Fundamental concepts on algorithms and data structures for Business Analytics. ii) Data types: stacks, queues, priority queues, heaps, and trees iii) Search binary trees iv) Balanced search trees v) Dispersion tables 2) Practical uses of Business Analytics 3) Historical exploration of the business context of the Descriptive Analytics 4) Business forecasting with Predictive Analytics 5) Other tools for Analytics 6) The use of Analytics in concrete business contexts a) The prescriptive perspective of the Analytics b) The constructive perspective of the Analytics with examples c) Good practices about the interaction and communication of the results with managers, decision-makers and other stakeholders d) The frontiers between Business Analyics and other areas.

Prerequisites

Probability and Statistics.

Cross Competence Component

Grade assessment: 10% - Critical and innovative thinking (eg creativity, strategic thinking, approaches to problem solving). - Interpersonal Skills (eg oral presentations, communication and organizational skills, teamwork, etc.).

Laboratorial Component

On average, each week will have 3 hours of contact, making 42hs in total. Students will have 168h(6 ECTS)-42h = 126h in other (O) working hours.

Programming And Computing Component

(1.5 ECTS): 25% of the grade assessment. i) Fundamental concepts on algorithms and data structures for Business Analytics. ii) Data types: stacks, queues, priority queues, heaps, and trees iii) Search binary trees iv) Balanced search trees v) Dispersion tables

Ethical Principles

All members of a group are responsible for the group's work. In any assessment, every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.