course-details-portlet

TDT4259 - Applied Data Science

About

New from the academic year 2020/2021

Examination arrangement

Examination arrangement: Assignment and Work
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Assignment 50/100 1 semesters ALLE
work 50/100 1 semesters ALLE

Course content

Data science comprises a significant variety of methods and technologies for mining, aggregating and analzying data.


The aim of most courses in AI is understanding the finer details of the methodological aspects. This course, however, is aimed at developing knowledge of, skills in and competance of the most used methods.

The course exploits the fact that very many business-relevant, practical problems applications of data science do not require the most sophisticated methods. Many practical problems from private and public organizations may be tackled with known methods readily available in commodified technologies in the form of open source.

Learning outcome

Knowledge: The candidate will establish deep knowledge about the variety of data science methods, technologies and algorithms.

Skills: The candidate will gain solid skills in setting up and configuring data science tools. The candidate will develop good skills in identifying what methods are appropriate for what type of problems. The candidate will also develop good skills in preparing and pre-processing data for input.

Competence: The candidate will estabslish a compentence in the conditions for the application of selected data science methods to address business and strategica challenges.

Learning methods and activities

The course consists of lectures and project work.

The students need to complete a group-based project as well as an individual assignment. In the group project the students go through a realistic, problem-oriented analytics of the data.

The group project is develop practical skills in configuring the relavant tools/ technologies, pre-processing of data and conducting the analytics. The individual assignment discusses the group project in light of relevant literature from the course’s curriculum.

The group-based project counts for 50% and the indivual assignment 50% of the evaluation. Both parts need a pass for a student to obtain a pass in the course.

The course may be lectured in English if there are students not knowledgable in Norwegian

Further on evaluation

Both parts need a pass grade for a student to obtain a pass in the course. There will be no re-sit. If the course is failed, the student must retake the course in its entirety.

Required previous knowledge

None

Course materials

Provided at beginning of semester

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2020

No.of lecture hours: 3
Lab hours: 2
No.of specialization hours: 7

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Information Systems
  • Industrial Economics
  • Business Economics
  • Entrepreneurship
  • Business Econimics and Management
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science

Phone:

Examination

Examination arrangement: Assignment and Work

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD work 50/100 ALLE
Room Building Number of candidates
Autumn ORD Assignment 50/100 ALLE
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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