Big Data Analytics

Credits
4.5
Types
Optional
Requirements
This subject has not requirements , but it has got previous capacities
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

  • Antonio Moreno Ribas (antonio.moreno@urv.cat)

Others

  • Monir Yahya Ali Salmony (moniryahyaali.salmony@urv.cat)

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: CB6, CT6, CT7, CEA8, CEP1, CG3,
    2. Ability to analyze big data.
      Related competences: CB6, CT3, CT6, CT7, CEA8, CEP1, CG3,

    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


    Master classes.

    Blackboard explanations.
    • Theory: Theory

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

    Exam

    Exam
    Objectives: 1 2
    Week: 10
    Theory
    0h
    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%