Checking date: 19/05/2022


Course: 2022/2023

Big data and business analitics
(17637)
Study: Bachelor in Management and Technology (351)


Coordinating teacher: SAEZ ACHAERANDIO, YAGO

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Have previous knowledge about statistics and/or have passed any of the Fundamentals of Statistics subject, basic programming skills
Objectives
1. Acquire the basic knowledge necessary to carry out an exploratory analysis of objective and conclusive data 2. Get a deep knowledge about the full data analytics cycle 3. Get in touch and use some of the technology tools in the industry for data analysis 4. Being able to tackle "big data" analysis
Skills and learning outcomes
Description of contents: programme
1. Introduction to Big Data Data and Business Analytics 2. Models and Technologies for Decision Making 3. Descriptive Analytics 3.1. Exploratory Data Analysis 3.2 Business Reports and Visual Analytics 3.3 Data Warehouses 4. Predictive Analytics and Data Mining 4.1 Basic Concepts in Supervised Learning 4.2 Linear Regression 4.3 Decision Trees 4.4 Evaluation of Classifiers 4.5 Other Classification Techniques 4.6 Ensemble-based Methods 5. Neural Networks and Deep Learning 6. Big Data Specific Technologies 7. Emerging Trends and Impact of Business Analytics
Learning activities and methodology
AF1. THEORETICAL-PRACTICAL LECTURES. These lectures will present the knowledge that students should acquire. They will receive the lecture notes and will have basic texts of reference to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems on the part of the student will be solved, and workshops and evaluation tests will be carried out to acquire the required skills. AF2. TUTORIES. Individualized assistance (individual tutorials) or group (collective tutorials) to students given by the teacher. AF3. INDIVIDUAL OR GROUP STUDENT WORK. MD1 THEORY LECTURE. Talks with support of computer and audiovisual media, in which the main concepts of the subject are developed and the materials and bibliography are provided to complement the students' learning process. MD2. PRACTICES. Resolution of practical cases, problems, etc. organized by the teacher individually and/or in groups. MD3. TUTORIES. Individualized assistance (individual tutorials) or group (collective tutorials) to students given by the teacher. For 6 credits subjects, 4 hours will be dedicated with 100% of attendance required.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Steve Williams. Business Intelligence Strategy and Big Data Analytics: A General Management Perspective. Morgan Kaufmann. 2016
Additional Bibliography
  • Stepanek, Hannah. Thinking in Pandas. 1st ed. Berkeley, CA: Apress . 2020

The course syllabus may change due academic events or other reasons.