Course

Introduction to Data Science (DAT540)

The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.


Dette er emnebeskrivelsen for studieåret 2019-2020. Merk at det kan komme endringer.

See course description and exam/assesment information for this semester (2024-2025)

Semesters

Fakta

Emnekode

DAT540

Vekting (stp)

10

Semester undervisningsstart

Autumn

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Autumn

Timeplan

Vis timeplan

Content

The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors, and industries. In this course, students become familiar with basic tools and processes used in Data Science. Students work through the whole data lifecycle from loading, through cleaning and modeling, to storing the data. The work is performed using Python stack consisting i.a. of: IPython, NumPy, Pandas, Matplotlib, and Jupyter Notebooks. Students learn to structure their work using CRISP-DM and Data Science Process (Ask, Get, Explore, Model, Communicate and Visualize).

Learning outcome

Knowledge:

  • Execute tools to load, parse, clean, transform, merge, reshape, and store data.
  • Compare regular Python, NumPy, and Pandas data structures and choose one for the given problem. Use the IPython shell and Jupyter notebook for exploratory computing.
  • Execute simple machine learning or data mining algorithms.

Skills:

  • Organize data analysis following CRiSP-DM and Data Science Process
  • Build engaging visualizations of data analysis using matplotlib
  • Optimize data analysis applying available structure and methods 
  • Evalute, communicate and defend results of data analysis

General qualifications:

  • Solve real-world data analysis problems following a well-structured process

Forkunnskapskrav

10 Credits in Programming, Databases or Software Engineering related courses.

Exam

Form of assessment Weight Duration Marks Aid Exam system Withdrawal deadline Exam date
Project work and oral presentation 1/1 Letter grades — — —


Project is completed in groups. Project work is to be performed at the times and in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the lecturer.

Both project and oral examination must be done before final grade is given. Each group member can receive a different grade based on their performance during the oral examination.

If a student fails the projectwork , he/she have to take this part again next time the subject is lectured.

Vilkår for å gå opp til eksamen/vurdering

Mandatory assignments
The course starts with 4 mandatory assignments completed individually. All mandatory assignments must be approved within a given deadline so that that student has the right to start with the project. 

Fagperson(er)

Head of Department:

Tom Ryen

Course coordinator:

Antorweep Chakravorty

Method of work

The work will consist of 6 hours of lecture, scheduled laboratory, supervised group work per week. Students are expected to spend additional 6-8 hours a week on self-study, group discussions, and development work (open laboratory).

Åpent for

Admission to Single Courses at the Faculty of Science and Technology
Data Science - Master of Science Degree Programme Computational Engineering - Master of Science Degree Programme Computer Science - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Industrial Automation and Signal Processing - Master's Degree Programme - 5 year
Exchange programme at Faculty of Science and Technology

Emneevaluering

Form and/or discussions.

Litteratur

(Required) Python for Data Analysis 2nd Edition, McKinney, O'Reilly ISBN: 978-1491957660

(Optional) Python Data Science Handbook, VanderPlas, O'Reilly ISBN: 978-1491912058

(Optional) Building Machine Learning Systems with Python 2nd Edition, Coelho and Richert, PACKT ISBN: 978-1784392772

Kontakt

Head of Department:

Tom Ryen

Course coordinator:

Antorweep Chakravorty

Tilbys av

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1