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Syllabus:

# Introduction to data science, 3 Credits

Swedish name: Introduktion till data science

This syllabus is valid: 2023-10-30 and until further notice

Course code: 2ST043

Credit points: 3

Education level: First cycle

Main Field of Study and progress level: Statistics: First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Responsible department: Department of Statistics

Revised by: Dean of Umeå School of Business, Economics and Statistics, 2023-10-26

## Contents

The course gives an introduction to data science with emphasis on the essential part of data science that consists predictive modelling. Predictive modelling aims to generate predictions based on historical data. In addition to parametric predictive models, such as linear regression and logistic regression models already known from the course Statistik A, some non-parametric predictive models, such as K-nearest neighbors models, are introduced during the course.

Regardless of which kind of predictive models that is used, it is of key importance to evaluate the accuracy of the predictions. Ways to evaluate predictions are therefore also introduced during the course.

As predictive modelling, more and more regularly, are used in all parts of society and as a basis for decisions it is also necessary to be aware of that, similar to human decisions, algorithms can also be subject to bias and errors. Thus, there are crucial ethical considerations that must be reflected on when doing data science and predictive modelling. During the course this is problematized.

## Expected learning outcomes

After passing this course the student should be able to:

Knowledge and understanding
1.     describe fundamental types of analysis problems arising in data science;

Skills and abilities
2.     identify appropriate types of data science methods for practical applications;
3.     apply simple predictive modelling to solve practical problems;
4.     present, orally and in writing, the results from the data science application

Judgement and approach
5.     critically evaluate results from data science applications;
6.     critically reflect on data science applications with respect to ethics.

## Required Knowledge

Univ: 7.5 ECTS in Statistics, with linear regression and logistic regression, or similar knowledge

## Form of instruction

The course consists of lectures, lessons, tutorials, seminars and supervision. Mandatory assignments and seminars are included.

## Examination modes

The examination consists of a written assignment, which should also be presented orally during at least one mandatory seminar. Opposition of another student's work is also part of the examination. The grades used are: VG (Pass with distinction), G (Pass), and U (Fail).

Grades on the course are awarded when students have passed all examinations and mandatory course elements. The grade is a comprehensive evaluation of the results of the various parts of the examinations and is not granted until all mandatory tasks have been passed. Any compensating assignments must be completed in accordance with instructions given, no later than two weeks after the completion of the course.

A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional test will be held according to a set schedule. Any supplementation of the assignment should be completed within two weeks after the students have been notified of the examination result.

Exceptions from examination form as stated in the syllabus can be made for a student who has a decision on pedagogical support for disabilities. Individual adaptations of the examination form should be considered based on the student's needs. The examination form shall be adapted within the framework of the expected learning outcomes stated in the course syllabus. At the request of the student, the course responsible teacher, in consultation with the examiner, must promptly decide on the adapted examination form. The decision must then be notified to the student.

After two failed examinations in one module, the student has the right to request another grading teacher unless special reasons exist. Written requests should be handed to the Director of Studies no later than two weeks before the date of the next examination.

Examinations based on the same course syllabus as the ordinary examinations are guaranteed to be offered up to two years after the date of the student's first registration for the course.

Academic credit transfers are according to the University credit transfer regulations.

## Literature

### Valid from: 2023 week 44

A Guide for Ethical Data Science  A collaboration between the Royal Statistical Society (RSS) and the Institute and Faculty of Acturaries (IFoA)
Royal Statistical Society (RSS) and the Institute and Faculty of Acturaies (IFoA) : 2019 :
https://www.actuaries.org.uk/documents/guide-ethical-data-science
Mandatory

O'Neil Cathy
Weapons of math destruction : how big data increases inequality and threatens democracy
London : Penguin Books : 2017. : x, 259 p. :
ISBN: 9780141985411
Mandatory
Search the University Library catalogue

An introduction to statistical learning : with applications in R
James Gareth, Witten Daniela, Hastie Trevor, Tibshirani Robert
Second edition. : New York, NY : Springer : [2021] : xv, 607 pages :
ISBN: 9781071614204
Mandatory
Search the University Library catalogue
Reading instructions: The book is avaliable as a free online resource at https://www.statlearning.com