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.
Information form
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(34) 93 114 68 05

INFORMATION 2018-19 EDITION

Next course:
October 2019

Credits:
60 ECTS
(318 teaching hours)

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

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