Big Data Analytics

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Credits
4.5
Types
Elective
Requirements
This subject has not requirements
Department
URV;CS
Massive data analysis is affecting many areas of science, engineering and industry; Discussing new challenges ranging from analyzing meteorological data to modeling traffic patterns to the processing of millions of online clients. To face these challenges, you need to have the training to store, manage, process and analyze data of this magnitude. The complexity of the data requires new powerful analytical techniques designed for this purpose.

Teachers

Person in charge

  • Alexandre Arenas ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0

Competences

Generic Technical Competences

Generic

  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Technical Competences of each Specialization

Academic

  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.

Transversal Competences

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.

Objectives

  1. To understand the problem of big data.
    Related competences: CEA8, CG3, CEP1, CT6, CT7, CB6,
  2. Ability to analyze big data.
    Related competences: CEA8, CG3, CEP1, CT3, CT6, CT7, CB6,

Contents

  1. Introduction
    Big data scenario.
  2. Data gathering
    The problem of big data gathering.
  3. Data storage.
    How to storage and access big data.
  4. Exploration data analysis
    How to make exploratori data analysis.
  5. Data preprocessing.
    How to pre-process big data.
  6. Data to models.
    How to model with data.

Activities

Activity Evaluation act


Theory
16h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Exam

Exam
Objectives: 1 2
Week: 10
Type: final exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Labs



Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

Explanations and related bibliography.

Evaluation methodology

Topic-based evaluation. For each topic, the student must show proof of understanding.

Topic 2: 20%
Topic 3: 20%
Topic 4: 20%
Topic 5: 40%