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Høst 2018

BED-2056 Introduction to Data Science - 10 stp

The course is administrated by

Faculty of Biosciences, Fisheries and Economics

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

1. juni for emner som tilbys i høstsemesteret. 1. desember for emner som tilbys i vårsemesteret.

Objective of the course

Data gathering, manipulation, visualizations and analysis will be carried out in R, a programming language and free software environment for statistical computing and graphics. The R software will be introduced and used comprehensively during the course. We will use online teaching resources from Datacamp ( for free. Especially some of the modules from the career track Data Scientist with R. In addition we will work on different cases, each case represents a new "data adventure," analyzing real datasets, exploring different questions and trying out different computational tools.


Students who have successfully completed the course should have achieved the following learning outcomes:

Knowledge and comprehension


Knowledge of the process of Data Science. Understanding of different computational tools that can be used to gather, visualize and analyze data. Students will learn to understand and explore conceptual challenges of inferential reasoning with data.



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.



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


1. data collection ¿ data wrangling and cleaning to get data suitable for analysis.

2. data management ¿ manipulating data consistently.

3. exploratory data analysis ¿ generating hypotheses and building intuition from data.

4. prediction or statistical learning from data.

5. communication ¿ present the extraction of knowledge and insights from data.

Language of instruction


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 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 project should showcase the ability to ask an interesting scientific or business relevant question, to gather and clean relevant data, to apply some meaningful analytical model, 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.

Date for examination

Portfolio assessment hand in date 10.12.2018

The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.

Recommended reading/syllabus

Textbook: R for Data Science. Hadley Wickham & Garrett Grolemund. Available online at:

An additional reading list will be published at the beginning of the semester in UiTs LMS.

Lectures Autumn 2018
Første gang:se timeplan på nett
Forelesninger Tromsø prof. Øystein Myrland
uni.lekt. Marius Runningen Larsson