UP

Big Data Management, Technologies and Analytics

Master's degree. Face-to-face.

Content

2nd EDITION
UPC School

Subjects

Data Management
12 ECTS. 72 teaching hours.
1. Motivation.
- Context. The data society and the data-driven paradigm.
- Cases of use.
- Cloud computing and Services Engineering (XaaS).
 - The need for a paradigm shift: NoSQL.

2. Basic principles of non-relational databases (NoSQL).
- New architectures.
- New data models.

3. Foundations: new architectures.
- Basic concepts.
- One size does not fit all.
- Distributed data management and processing.
- Data management and processing in memory.
- Main reference architectures.

4. Foundations: new data models.
- Basic concepts.
- Unstructured and semi-structured data models.
- Main data models in the NoSQL world: Key-Value, Document-oriented, Graphs, Semantic Graphs and Streams.
- Advanced data modelling (for non-relational systems).

5. Main Families of NoSQL Managers.
- Key-Value Managers.
* Concept and principles.
* The Hadoop ecosystem: HDFS, HBase, MapReduce and Spark.
* Specific modelling considerations.

- Document-oriented managers.
* Concept and principles.
* Example: MongoDB and the Aggregation Framework.
* Specific modelling considerations.

- Column-oriented managers
* Concept and Principles
* Example: Arrow (database) and Parquet (files)
* Specific modelling considerations.

- Graph Managers.
* Concepts and principles.
* Types of graphs and operations.
* Example: Neo4J and Cypher.
* Specific modelling considerations.

- Semantic Graph Managers.
* Concept and Principles: the paradigm of Open Data / Linked Data.
* How to open data.
* Architectures based on graphs vs. relational technology.
* RDF and SPARQL.
* Specific modelling considerations.

6. Data integration.
- Intensive data processes and ETLs.
- Polystores and multilingual systems .
- Orchestrators: Muskeeter.

7. Visualisation.
- Visualisation processes.
- Visualisation techniques.
Data Analytics
12 ECTS. 72 teaching hours.
1. Introduction.
- What is knowledge discovery?
- Basic statistics.
- Introduction to R.

2. Pre-processing of data.
- Data cleansing and adjustment.
- Transformations.

3. Basic analysis techniques.
- Multiple regression.
- Profiling.

4. Multivariate Analysis.
- Principal component analysis.
- Clustering.
- Decision trees.

5. Machine Learning.
- Concept.
- Mathematical foundations.

6. Main machine learning techniques.
- Association rules.
- Supervised linear methods.
- Neuronal networks.
- Support vector machines.
- Random forests.

7. Text processing.
- Pre-processing and preparation of data.
- Main text mining techniques.
- Information retrieval.

8. Time series analysis.
- Pre-processing and preparation of data.
- Forecasting.
- Identifying outliers.

9. Advanced data analysis.
- R packages for parallel processing.
- R and relational databases.
- Data analysis in distributed environments using HDFS and Spark.
* Spark R.
Hands-on Experience: Data Maganement and Analytics
16 ECTS. 96 teaching hours.
1. Infrastructure.
- Introduction to Cloud environments.
- Virtualisation.
- Oracle services.

2. Distributed storage.
- The Hadoop Ecosystem.
- Key-Value Systems: HBase.

3. Distributed processing.
- MapReduce.
- Spark.
* SparkSQL.
* Spark Streaming.
* Spark Graphs.

- Data analysis in distributed environments.
* MLlib.
* SparkR.

4. Document Stores.
- MongoDB.
- ElasticSearch.

5. Graph databases.
- Neo4J.
- Semantic graphs: GraphDB and SPARQL.

6. Big Data systems architecture.
Business and Entrepreneurship in Big Data
5 ECTS. 33 teaching hours.
1. Introduction: The competitive environment of the company and Big Data.
- Big Data Landscape.

2. Success stories.

3. Business ideation techniques.
- Clients and users.
- Definition of products and services.

4. Business modelling tools: Business Model Canvas.
- Constituent parts.
- Practical cases.
- Resolution of cases: Twitter, Facebook, etc.

5. Financing process.
- Finance.
- Private funding.
* Business Angels.
* Venture Capital.

- Public funding.

6. Marketing.

7. Creating a business.
- Legal issues.
* Data regulation

- Financial considerations.

8. Ethical considerations of Big Data: Business and Privacy.

9. Presentations and pitch.
Project
15 ECTS. 45 teaching hours.
1. Project management.
- Agile methodologies.
- Specific considerations for Big Data.

2. Presentation of the project.

3. Project monitoring sessions.
The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the course objectives.
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(34) 93 114 68 05

Credits:
60 ECTS
(318 teaching hours)

Start date:
Classes start:08/10/2018 Classes end:12/07/2019Programme ends: 08/11/2019
Timetable:
Monday  18:00 to 21:00Wednesday  18:00 to 21:00Friday  18:00 to 21:00
Taught at:
Facultat d'Informàtica de Barcelona (FIB)
C/ Jordi Girona, 1-3
Barcelona

Registration fee:
8.500 €

Language of instruction:
Spanish

Registration open until the beginning of the course or until end of vacancies.

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