Degree in Business Intelligence and Data Analytics

Lead the transformation of companies through the use and analysis of data.

Advanced data processing and analysis

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Description
There is a direct link between how well an organization manages its data resources and its financial performance. The goal of "Advanced data processing and analysis" is to help you to take good decisions with data. This subject divides its syllabus into two general blocks: (a) data governance, management and security along with (b) algorithms to analyze structured and non-structured data. In the first block, the subject aims to give principles, definitions and models to use when evaluating, directing and monitoring the handling and the use of data in their organizations. In the second block, the subject aims to detail and practice three main Machine Learning methods for structured and non-structured data (SVM, Knn, Logistic Regression and PCA).
Type Subject
Tercer - Obligatoria
Semester
Second
Course
2
Credits
6.00

Titular Professors

Didier Grimaldi
Professor

Professors

Esther González Benítez
Previous Knowledge
Objectives

Learning Outcomes of this subject are:
R1. Understand data governance concept
R2. Define a correct data strategy aligned with business strategy
R3. Define a correct data governance strategy
R4. Understand how to secure data
R5. Manipulate and codify Machine Learning Supervised and Non-supervised algorithms

Contents

Advanced data processing and analysis involves actions and methods performed on data that help describe facts, detect patterns, develop explanations and test hypotheses. This includes:
- Data governance
- Statistical data analysis
- Modeling
- Interpretation of results

Methodology

The subject has two teaching sessions every week. Each Session is divided into Two parts: a first part is masterful in which the teacher explains the new Contents and one second in which the students work in new Exercises to consolidate the subject. Every two or three sessions, individual or group evaluation activities are carried out by means of written tests, collection of exercises carried out at home, etc.
The following table relates the learning outcomes to the areas and the content taught to achieve them.

R1. Understand data governance concept
- Recognizing data assets from Information Systems
- Data governance definitions
- ISO Guidance principles for the Management
- Benefits of good governance of data
- Do you think data governance and business results are correlated?

R2. Define a correct data strategy aligned with business strategy
- Data governance components
- Data management plan components
- Data life cycle
- Examining the relationship between data management and data governance

R3. Define a correct data governance strategy
- Data Governance Strategy
- Data strategy in Big Data epoch
- Data Governance Template

R4. Understand how to secure data
- Data security, compliance and backup and MIS2
- Disaster Recovery and Business Continuity

R5. Manipulate and codify Machine Learning Supervised and Non-supervised algorithms: Basic code, examples, script

Evaluation

In order to evaluate if the student has achieved in an adequate degree the objectives pursued in the subject, different evaluation activities are used (with a frequency of approximately weekly).
The following table shows the percentage of evaluation of each activity on the final grade:

CONTINUOUS EVALUATION SYSTEM:
R2 - 15% - 20% - DAILY TASKS IN CLASS
R4 - 35% - 20% - GROUP PROJECT 1
R5 - 15% - 20% - GROUP PROJECT 2
R6 - 35%
- 20% - GROUP PROJECT 3
- 20% - FINAL EXAM (ORDINARY CALL)

Students who do not pass the regular call will have an Extraordinary Call in July. Students who do not take any of the rest exams will have a final grade of the subject NP (Not Presented) in the extraordinary call.
Objectives of the continuous evaluation:
- The main objective is to help students to update the subject and get a good method of work, so that it helps them to assimilate the subject, taught progressively, and in obtaining good academic results.
- It also allows to value the work that the student does day by day, without his note depends only of the examinations realized during the semesters of the academic course.
- As a teacher, it helps to have more information about the work done by students and a better knowledge of them, both academically and personally.

Retake policy: Should you fail the course overall, you will have the opportunity to sit a re-take exam, as long as assignments and projects have been presented.
The re-take grade will then be: 40% the re-take exam and 60% the continuous assessment obtained during the course.

Evaluation Criteria
Basic Bibliography

- Data Strategy - how to profit from a World of big data, analytics and artificial intelligence (2022). Bernard Marr. 2nd Edition. KoganPage
- Data Governance. How to design, deploy and sustain and effective data governance program (2020). John Ladley. 2nd Edition. Elsevier.
- DAMA-DMBOK (Data Management Body of Knowledge) 2017. 2nd Edition. Technics Publications. Basking Ridge, New Jersey.
- ISO/IEC 38505-1:2017. International Standard. Information Technology - Governance of IT - Governance of data. Part1: Application of ISO/IEC 38500 to the governance of data
- James et al. - 2021 - An Introduction to Statistical Learning
- Easy Steps To Managing Cyber Security Risk Edited By Jonathan Reuvid

Additional Material