Høst 2020

BED-2056 Introduction to Data Science - 10 stp

The course is administrated by

Handelshøgskolen ved UiT

Type of course

This course may be taken as a singular course.

Course overlap

SOK-1005 Computer science for economists 10 stp

Course contents

Firms and organizations use internal and external data for reporting and decision support. Data science is a collection of skills required to extract knowledge or insights from various types of data. This course will provide an introduction to this process, and students will learn skills applied to data collection, data management, and the ability to manipulate data sets with code. The course focuses on the analysis of messy, real life data, e.g., from finance and social media, and to report on insights from this process.

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.

Objective of the course

Knowledge and comprehension:

Knowledge of the process of Data Science.
Understanding of different computational tools that can be used to gather and analyze data.
Understand and explore conceptual challenges of inferential reasoning with data.

Skills:

Be able to identify interesting Data Science opportunities, questions and data sources.

Be able to write code that extracts data from various relevant sources.

Be able to write code that manipulates and transforms data.

Be able to write code that visualize data.

Be able to write code that model relationships in data.

Be able to use code that produces insight from data, and the principles behind reproducible code/projects.

Competence:

The student should be able to develop competence that adds value to data in the following five fields of Data Science:

Data collection - data wrangling and cleaning to get data suitable for analysis.
Data management - manipulating data consistently.
Exploratory data analysis - generating hypotheses and building intuition from data.
Prediction or statistical learning from data.
Communication - present the extraction of knowledge and insights from data.

Language of instruction

English

Teaching methods

The course has various teaching methods as lectures, seminars, data lab and online resources. Students should expect to participate in group work.

Assessment

Assessment will be based on a portfolio of obligatory assignments, and a portfolio project. The portfolio project can be submitted as a part of a group. The portfolio should showcase the ability to ask an interesting scientific or business relevant question, to gather and clean relevant data, to apply some meaningful analytical analyses, and to showcase or visualize the results in an engaging, digestible manner.

A graded scale of five marks from A to E for pass and F for fail. Only one overall grade is given for the course. There will not be a re-sit exam for this course.