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Undergraduate course

Introduction to Data Science

Level of Study

Bachelor

Teaching semester

Autumn

Objectives and Content

In today's information era we are increasingly faced with major data science challenges in research, business and society. Data science is a collection of methods to extract knowledge from different types of often large and complex data. In this course, you will get an overview of the whole data science pipeline, including data collection, data preprocessing, data mangagement, data analysis with statistical, machine learning and visualisation methods, and deploying data science solutions. We will also study ethical and societal issues related to data science.

Learning Outcomes

Upon completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge
The student should be able to

  • explain the basic prinsiples of each step in the data science pipeline
  • compare different approaches in each step of the data science pipeline

Skills
The student should be able to

  • preprocess and manage real wolrd data sets
  • analyze and visualize real-worl data sets
  • successfully implement and deploy a data science project 

General competence
The student should be able to

  • discuss successful examples of how data science can be used in different contexts in society
  • discuss ethical condiserations related to data science projects

Required Previous Knowledge

None

Recommended Previous Knowledge

Programming skills, INF100 or equivalent.

Credit Reduction due to Course Overlap

None

Access to the Course

Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.

Teaching and learning methods

Lectures, max. 4 hours per week
Exercises, 2 hours per week
Independent and group projects

Compulsory Assignments and Attendance

Approved compulsory exercises. Compulsory assignments are valid for two semesters; the semester the assignments were conducted and the subsequent one.

Forms of Assessment

Written, digital exam (3 hours). Compulsory exercises may count towards the final grade. Both the exam and the compulsory exercises must be passed.

Examination Support Material

None-programmable calculator, according to the faculty regulations.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester

Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.

Reading List

The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator

Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.

Contact

Contact Information

Student adviser:

Student adviser

T: 55 58 42 00

Exam information

  • For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam. Autumn 2020 written exams will be arranged either at home or on campus. Please see course information on MittUiB.

  • Type of assessment: Written examination

    Date
    19.11.2020, 09:00
    Duration
    3 hours
    Withdrawal deadline
    05.11.2020
    Examination system
    Inspera
    Digital exam