Advanced Statistics

Universidad de Deusto - Bilbao

Course Description

  • Course Name

    Advanced Statistics

  • Host University

    Universidad de Deusto - Bilbao

  • Location

    Bilbao, Spain

  • Area of Study

    Statistics

  • Language Level

    Taught In English

  • Prerequisites

    There is not any prerequirement, however, it is convenient to properly handle general mathematical concepts, such as derivatives and matrix operations, and to have passed the Statistics subject.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    Justification
    Graduates in Data Science and Artificial Intelligence should be able to carry out statistical analysis with a certain degree of complexity. In this sense, the main contribution of this subject to the Bachelor's Degree in Data Science and Artificial Intelligence is to provide a wide variety of statistical modeling approaches to deal with different real data problems.

    This subject develops the competence CE-CD-01: "Apply the concepts and the techniques of multiple variables and statistical analysis to the development of a data science project and interpret the obtained results from a useful point for the organization".


    Subject competencies
    This subject works the following specific competences:

    SC1. Identify, apply and interpret regression models. In addition, understand the estimation process behind each methodology.

    SC2. Handle dimensionality reduction models applied to correlated variables and identify different scenarios where each methodology should be applied.

    SC3. To know different clustering methodologies and to apply them to obtain individual profiles.


    Course content
    Chapter 1. Introduction to Multivariate Calculus.

    Chapter 2. Linear Regression.

    Chapter 3. Generalized Linear Models: Logistic Regression.

    Chapter 4. Factorial and Principal Component Analysis.

    Chapter 5. Correspondence Analysis.

    Chapter 6. Clustering.

    Chapter 7. Introduction to Bayesian Analysis.


    Evaluation System

    Observations:

    - The continuous assessment consists of a group work where each of the studied models is applied to several datasets. For the evaluation, both the provided report and oral communication are taken into account.

    - Students that have failed the continuous assessment, will have the opportunity to pass that part of the evaluation system the same day of the final exam. They will have to apply the studied methodologies to a new dataset and write a report with the results within the following 24 hours. The provided analysis and the report will be taken into account for the evaluation.

    - In order to pass the subject, it is not only necessary to obtain a minimum of 5 points in the final evaluation, but also to obtain a minimum of 4 points in the final test.

    - In the extraordinary assessment the evaluation will be carried out the same as in the ordinary one.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.

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