Degree | Type | Year |
---|---|---|
4318290 Archival Studies and Information Governance | OB | 2 |
You can view this information at the end of this document.
Having completed the course "A04. Information Systems and Systems Architecture"
Having completed the course "A09. Information Description and Retrieval"
1. Know the data life cycle and its management.
2. Understand the context of data production.
3. Apply archival principles to data management.
4. Know and understand the main tools and systems for data management.
5. Know data management systems and databases.
6. Know data governance models, rules, and standards.
7. Know and understand the basic systems for data use, exploitation, and visualization.
Sure, here is the translation to English:
1.1. Data in organizations (introduction)
1.2. Where is data produced?
1.2.1. Forms of data capture and generation (transactions, sensors, etc.)
1.2.2. Models for structuring data (master, reference, etc.)
1.2.3. Architectures for storage (types of databases)
1.2.3.1. Use of relational databases (SQL)
1.2.3.2. Use of NoSQL databases (Hadoop and HDFS, MongoDB, etc.)
1.3. How is data used?
1.3.1. Data preparation
1.3.1.1. Data cleansing
1.3.1.2. Preparation for exploitation (cubes, BI, etc.)
1.3.1.3. Treatment consolidation (ETL, RPA, etc.)
1.3.1.4. Formats of data to be cleaned (CSV, JSON, XML, etc.)
1.3.1.5. Data cleaning and preparation with Python
1.3.2. Data exploitation and use
1.3.2.1. Data visualization
1.3.2.2. Advanced analytics: statistical, ML, and AI-based
1.3.2.3. Practical application of advanced analytics algorithms
1.4. Integrated data governance
1.4.1. Data identification and cataloging
1.4.2. Data lineage control
1.4.3. Data access virtualization
1.4.4. Legal and security aspects
1.4.5. Links with archival science
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Theoretical sessions | 45 | 1.8 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Type: Supervised | |||
Exercise 1: cleaning, debugging and preparation of a dataset. | 30 | 1.2 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Exercise 2: Creating a simple data visualization. | 30 | 1.2 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Exercise 3: running an advanced analysis on a data set (regression or cluster analysis). | 20 | 0.8 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Type: Autonomous | |||
Final test: test of general knowledge of the subject. | 10 | 0.4 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Reading Materials | 90 | 3.6 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
The autonomous learning activities will be reading materials and preparing for the final general knowledge test of the course.
The directed activities will be theoretical lecture sessions.
The supervised activities will be 3 practical exercises to be done at home with the explanations received in class.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exercise 1: cleaning, debugging and preparing a dataset. | 25% of the final grade | 0 | 0 | CA21, SA24 |
Exercise 2: creating a simple data visualization. | 25% of the final grade | 0 | 0 | KA31, SA23, SA24 |
Exercise 3: execution of an advanced analysis on a set of data (regression or cluster analysis). | 20% of the final grade | 0 | 0 | CA21, KA32, SA23 |
Final test: test of general knowledge of the subject. | 30% of the final grade | 0 | 0 | CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25 |
Both Exercise 1 and Exercise 2 will be worth 25% of the final grade. The 3rd exercise will be worth 20%, and the final exam will be worth 30%.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(TE) Theory | 1 | Catalan | first semester | afternoon |