Modern Statistical Computing

Universidad Pompeu Fabra

Course Description

  • Course Name

    Modern Statistical Computing

  • Host University

    Universidad Pompeu Fabra

  • Location

    Barcelona, Spain

  • Area of Study

    Computer Science

  • Language Level

    Taught In English

  • Prerequisites

    An introductory course on probability and statistics is basic for enrolment to this course. For UPF students, the compulsory requirement is the Probability and Statistics of the second year in the studies of ECO/ADE/IBE.

    Hours & Credits

  • Contact Hours

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    Course Description
    Statistical computing is a highly sought-after analytical data analysis skill both in professional and research environments. This course presents modern graphical displays and data manipulation methods, interactive and reproducible reporting, emphasizing statistical methods and computation related to regression and classification methods such as linear, generalized linear and non-linear models. The concepts are introduced in R ( one of the leading programming languages for statistical computing. Knowledge of R is highly-valued by companies in many sectors for positions related to data science, quantitative analysis or finance. R is also an indispensable tool in most research fields, including Economics, Finance, Marketing, Biomedicine, etc. R provides a rich set of off-theshelf data analysis tools, and the possibility to design our own data processing and analyses. R runs in all operating systems (Windows, Mac, Unix-like) and is a free open-source language that is enhanced by an extensive list of user-contributed packages. The purpose of this course is to introduce students to statistical computing, including flexible regression data analysis methods, and to advanced R skills. The idea is that students learn by doing. Therefore, there is a strong applied emphasis, all concepts are driven by examples discussed in class, where students are given the code to reproduce them. Students will become skilled in applications of elementary statistical methods, with an emphasis on data exploration, graphics and programming. Focus will also be placed on opportunities to enhance the learning experience in other statistical courses.
    Learning Objectives
    At the end of the course, students wll have learned
    -to use a fundamental data analysis tool for quatitative analytical methods.
    -programming, data handling, exploratory data analysis, linear / generalised linear /non -linear regression, summarising data, effective graphics, model-free computational methods (bootstrap, permutation tests, cross-validation)
    -Preparing notebooks to automatically perform quantitative analyses and create reports in formats such as pdf and html, with interactive elements.
    Course Workload
    The course is constituted by lectures and practice with laptop computers.The teaching philosophy is that students learn by doing.
    Classroom sessions are normally split into a lecture and a practice part.
    Students are required to attend classes with their own laptops.
    Method of Assessment
    20% Homework + Class contribution
    40% Controls in-class exercises
    40% Final Project
    The Final Project (in groups of 2 students) requires a report to be submitted of up to 10 typed pages (not counting appendices). Students will select their projects from topics of their own interest (accepted by the course instructors) and will make a brief oral presentation at the end of the course.
    Absence Policy
    Up to two (2) absences -  No penalty
    Three (3) absences - 1 point subtracted from the final grade (on a 10 point scale)
    Four (4) absences - 2 points subtracted from the final grade (on a 10 point scale)
    Five (5) absences - The student receives an INCOMPLETE grade for the course
    The BISS attendance policy does not distinguish between justified or unjustified absences. The student is deemed responsible to manage his/her absences.
    Emergency situations (hospitalization, family emergency, etc.) will be analysed on a case-by-case basis by the Academic Director of the UPF Summer School.
    Course Contents
    Week 1
    Introduction to R
    Graphic Displays
    Data Manipulation 
    Week 2
    Programming basics 
    Week 3
    Computational inference methods: bootstrap, permutation tests, cross-validation
    Model comparison techniques
    Week 4
    Advanced reports: interactive plots, dashboards
    Methods of flexible data analysis 
    Required Readings
    The instructor will assemble a coursepack/ or indicate mandatory textbooks. 
    Recommended Bibliography
    Students are encourage to consult the following sources on their own. 
    Wickam, H., Grolemund, G. R for the Data Science. O’Reilly,
    Lander, Jared P. R for Everyone: Advanced Analysis and Graphics. Boston etc.: Pearson Education, Inc, 2017.

Course Disclaimer

Please note that there are no beginning level Spanish courses offered in this program.

Courses and course hours of instruction are subject to change.


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