Business Intelligence and Analytics

Excel Fundamentals. 

The Business Intelligence and Analytics curricular unit enables students to learn about, manage and develop the ability to propose Data Analytics, Data Visualisation and, overall, Business Intelligence and Analytics solutions. To this end, and in a first phase, students will take theoretical and practical classes where they learn to manipulate Self Service Business Intelligence tools (such as Tableau or Power BI) and understand how data should be presented and communicated so that it can be understood by the public.
Few (2019) states that ‘we are not yet in the Information Age but in the Data Age’ and, given the multiplicity of data available to facilitate the decision-making process, students will benefit if they are able to propose more efficient and effective solutions for the presentation of data and information in their companies.

The Business Intelligence (BI) course unit plans to hold a number of lectures with external guests on current research topics (it is essential that all Master’s course units contribute to students feeling capable of conducting research) in the area of BI, Business Analytics and application cases, among others.

The teaching methodology is essentially problem-based learning and project-based learning, encouraging students to propose solutions to problems that are presented in class and to develop a project throughout the semester. Even when it comes to writing a scientific article, it is based on the project methodology, as students will have to submit several stages of their work and receive feedback on each one in order to move on to the next. The aim is to promote research and the publication of research articles with students and enable them to improve their skills in proposing dashboards, taking into account the defined objectives and questions that the data analysis should answer.
The teaching methodology allows students to develop research and practical skills in specific areas of BI that the student considers relevant to their training and/or their company.

Objectives:
OB1: to familiarise students with the potential of Business Intelligence processes and the operational systems that support them
OB2: learn about current information technologies and methodologies for developing Business Intelligence solutions for data-driven companies
OB3: learn about the main trends in tools for data visualisation and task automation
OB4: know how to use a data visualisation tool and implement the entire data analysis circuit, with a critical sense of the solutions to be used.

Competences:
C1: scientific and research: knowledge of the main authors and most recent studies and trends on the themes of this area in Business Intelligence and Analytics.
C2: of a practical nature, namely that students learn about, manage and develop the ability to propose Data Analytics, Data Visualisation and, overall, Business Intelligence and Analytics solutions.

1.- Data-driven Decisions
1.1 Importance of data-driven decisions
1.2 Frameworks for informed decision-making
1.3 Main challenges and pitfalls in using data
2 – Business Intelligence & Analytics
2.1 Fundamentals of BI and Analytics
2.2 Types of BI analyses
2.3 Components of a BI solution
2.4 Key indicators and metrics (KPIs)
3 – Data governance, culture and data curation
3.1 Fundamentals of data governance
3.2 Data-driven culture
3.3 Data curation and quality
4 – Process automation
4.1 Introduction to automation in BI
4.2 Robotic Process Automation (RPA) and BI
4.3 Machine Learning and Artificial Intelligence in BI
5 – Tools for analysing data
5.1 Overview of BI tools
5.2 Languages and technologies for analysing data
5.3 Comparison of tools and selection criteria
6- Business Intelligence and Analytics project
6.1 Project planning
6.2 Building and implementing the solution
6.3 Project evaluation and presentation

Assessment will be carried out in two ways: periodically (AP) or by exam (E).

AP. Periodic assessment

The assessment method is periodic and consists of 2 assignments.

In order to pass, students must complete two assignments (Trab1 and Trab2): - Trab1 corresponds to a scientific article on the application and/or state of the art of Business Intelligence and Analytics in a specific area.

- Trab2 is a practical data visualisation assignment.

The work can be done in groups or individually. The presentation component of the work will make it possible to distinguish the students' grades within each group.

The final grade for the course is obtained by applying the following formula, the result of which is rounded to the nearest integer:

- CF=1/2(Trab1+Trab2)

Trab1 and Trab2: individual marks obtained in the practical work, on a scale of 0 to 20, without rounding.

The minimum mark for Trab1 and Trab2 is 9.5. If the student fails to submit one of the elements required for assessment, an ‘F’ grade will be given and the grade for the work submitted will be kept for subsequent terms in the same academic year.

Each assignment will have a mark corresponding to the elements submitted - i.e. article in the case of Trab1 and Power BI files for Trab2 - and the presentation and discussion corresponding to:

- Final mark for the assignment = elements_delivered*75% + presentation_and_discussion*25%

E. Exam Assessment

Normal, Appeal and Special Assessment Periods

In the Normal, Appeal and Special Periods, 2 individual assignments will also be set, as described above for Trab1 and Trab2 and due by the day before the exam.

Students who choose this option will have to submit their work on the day of the exam.

The calculation of the final mark follows the rules set out above for periodic assessment.

Mark improvement: the work carried out in each term, provided it has been assessed, will be taken into account in the event of a grade improvement.

NAO