Identifying Data 2019/20
Subject (*) Data Analytics with HPC Code 614973108
Study programme
Mestrado Universitario en Computación de Altas Prestacións / High Performance Computing (Mod. Virtual)
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Optional 6
Language
English
Teaching method Face-to-face
Prerequisites
Department Departamento profesorado máster
Enxeñaría de Computadores
Coordinador
López Taboada, Guillermo
E-mail
guillermo.lopez.taboada@udc.es
Lecturers
López Taboada, Guillermo
Rodríguez Álvarez, Gabriel
E-mail
guillermo.lopez.taboada@udc.es
gabriel.rodriguez@udc.es
Web http://aula.cesga.es
General description A cantidade cada vez maior de información accesible a través de Internet fai que o procesamento eficiente de grandes cantidades de datos sexa cada vez de maior interese. Isto levou ao desenvolvemento de novas técnicas de almacenamento e procesamento de inxentes cantidades de información, denominadas técnicas Big Data, que se adaptan de forma natural aos sistemas distribuídos.
Contingency plan

Study programme competencies
Code Study programme competences
A1 CE1 - Define, evaluate and select the most appropriate architecture and software to solve a problem
A2 CE2 - Analyze and improve the performance of a given architecture or software
B1 CB6 - Possess and understand the knowledge that give a baseline or opportunity to be original in the development and/or application of ideas, often in a research environment
B2 CB7 - The students have to know how to apply the acquired knowledge and their capacity to solve problems in new or hardly explored environment inside wider contexts (or multidiscipinary) related to its area of development
B6 CG1 - Be able to search and select useful information to solve complex problems, using the bibliographic sources of the field
B8 CG3 - Be able to maintain and extend properly funded theoretical hypothesis to allow the introduction and exploitation of novel and advanced technologies in the field
B10 CG5 - Be able to work in teams, specially multidisciplinary, and do a proper time and people management and decision taking
C1 CT1 - Use the basic technologies of the information and computing technology field required for the professional development and the long-life learning
C4 CT4 - Value the importance of research, innovation and the technological development in the socioeconomical and cultural advance of the society

Learning aims
Learning outcomes Study programme competences
The student will be capable of installing, configuring, and managing the basic software for massive data processing. AJ1
AJ2
BJ2
BJ6
BJ8
BJ10
CJ1
The student will be capable of coding massive data processing applications using domain-specific languages. AJ2
BJ1
BJ2
BJ10
CJ1
The student will learn about Data Engineering tools (for Intake/Storage/Processing/Visualization). AJ1
AJ2
BJ1
BJ2
CJ1
CJ4
The student will learn the skills to search, select and manage Big data-related resources (bibliography, software, etc.). AJ1
AJ2
BJ1
BJ6
CJ1
CJ4

Contents
Topic Sub-topic
1. Introduction to Data Engineering 1.1 HPC vs Big Data: similarities and differences in data management.
1.2 Hardware and Software Technologies for High Performance Data Engineering
1.3 Data Engineering in HPC infrastructures vs. Cloud environments
2. Data Engineering phases 2.1 Modeling (Formats, Compression, Designing Schemas)
2.2 Intake (Periodicity, Transformations, Tools)
2.3 Storage (HDFS and NoSQL DBs, HBase, MongoDB, Cassandra)
2.4 Processing (Batch, Real-Time)
2.5 Orchestration
2.6 Analysis (SQL, Machine Learning, Graphs, UI)
2.7 Governance
2.8 Integration with BI (Visualization)
3. Introduccion to Data Analytics 3.1 Exploratory Data Analytics
3.2 Introduction to Machine Learning
4 Use cases 4.1 Applications to Internet of Things (Smart environments and Industry 4.0)
4.2 Applications to sciences and engineering

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Workbook A1 A2 B1 B6 C4 0 18 18
Laboratory practice B1 B8 B10 0 80 80
Supervised projects A1 A2 B1 B2 B8 0 45 45
Directed discussion B6 C1 C4 4 2 6
 
Personalized attention 1 0 1
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Workbook Planned instruction through various teaching materials.
Laboratory practice Problem solving and practical cases.
Supervised projects Semi-autonomous work on larger practical cases, under the professors' guidance.
Directed discussion Guidance to solve individual / group assignments, problem solving and continuous evaluation activities.

Personalized attention
Methodologies
Laboratory practice
Supervised projects
Directed discussion
Description
During laboratory practice, supervised projects, and directed discussions, students will be able to ask questions, doubts, etc. The teacher, after listening to the students feedback, will go over difficult concepts, solve new problems, or use any appropriate methodology to answer the questions.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice B1 B8 B10 Grading the assignments submitted by students. 40
Supervised projects A1 A2 B1 B2 B8 Grading the supervised projects submitted by students. 50
Directed discussion B6 C1 C4 Continued, active, objectively measurable participation by the student. 10
 
Assessment comments

First evaluation (May): 

  • Practical exercises: 40% 
  • Guided projects: 50% 
  • Objective participation: 10% 

 Second evaluation (June/July):

  • Practical exercises: same grade as in the first evaluation, as there are no new activities planned for this evaluation. 40% of the final grade. 
  • Guided projects: projects not evaluated in may or deemed incomplete will be presented in july after performing the changes suggested by the professor. 50% of the final grade. 
  • Objective participation: same grade as in the first evaluation, as there are no new activities planned for this evaluation. 10% of the final grade. 

 Not graded: Students that do not present any practical exercise or guided project will not be graded.


Sources of information
Basic Tom White (2015). Hadoop: The Definitive Guide. O'Reilly (4ª ed.)
Wes McKinney (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly (2ª ed.)

Complementary Alex Holmes (2014). Hadoop in practice. Manning (2ª ed.)


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments

Recommendations

Due to the large practical component of the subject, it is advisable to be up-to-date with practices and guided projects during the semester. 

Observations 

The course makes intensive use of online communication tools: Video calls, chats, etc. In-person classes will be recorded for later perusing. An online learning management will be using for distributing notes, creating forums, etc.  

The software tools used in this course are generally open-source or have free license for students.



(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.