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 2022-2023. Merk at det kan komme endringer.
Semesters
Fakta
Emnekode
DAT540
Vekting (stp)
10
Semester undervisningsstart
Autumn
Undervisningsspråk
English
Antall semestre
1
Vurderingssemester
Autumn
Timeplan
Litteratur
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 ia 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/Develop 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/Develop 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
- Evaluate, 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.
Anbefalte forkunnskaper
Introduction to Programming (DAT120), Probability and Statistics 2 (STA500)
Exam
| Form of assessment | Weight | Duration | Marks | Aid | Exam system | Withdrawal deadline | Exam date |
|---|---|---|---|---|---|---|---|
| Project work in groups | 3/5 | Letter grades | — | — | — | ||
| Written exam (Multiple Choice) | 2/5 | 3 Hours | Letter grades | Inspera assessment | 22.11.2022 | 06.12.2022 |
Project Work in Groups
The 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.
A project report, included source code, contribute to the grade. The students must present the project work orally in order to get a grade in the subject.
If a student fails the project work, he / she has to take this part again next time the subject is lectured.
Fagperson(er)
Head of Department:
Tom RyenCourse coordinator:
Antorweep ChakravortyMethod of work
The work will consist of 6 hours of lecture, scheduled laboratory, supervised group work per week. Students are expected to spend an additional 6-8 hours a week on self-study, group discussions, and development work.
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
Litteratur
Book Python for data analysis : data wrangling with Pandas, NumPy, and IPython McKinney, W., Beijing, O'Reilly, XVI, 528 sider, 2017, isbn:978-1-491-95766-0, Book Building machine learning systems with Python : get more from your data through creating practical machine learning systems with Python Coelho, L.P., Richert, W., Birmingham, England, Packt Publishing, 1 online resource (326 p.), ©2015, isbn:1-78439-288-X, https://bibsys-ur.userservices.exlibrisgroup.com/view/uresolver/47BIBSYS_UBIS/openurl?ctx_enc=info:ofi/enc:UTF-8&ctx_id=10_1&ctx_tim=2019-11-25T08%3A29%3A34IST&ctx_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&url_ver=Z39.88-2004&rfr_id=info:sid/primo.exlibrisgroup.com-BIBSYS_ILS&req_id=&rft_dat=ie=47BIBSYS_UBIS:5155298430002208,ie=47BIBSYS_UBB:51130169800002207,ie=47BIBSYS_UBA:5164517660002209,ie=47BIBSYS_UBTO:51114176660002205,ie=47BIBSYS_NMH:5116912120002309,ie=47BIBSYS_UBIN:5148118220002211,ie=47BIBSYS_NB:51243574860002202,ie=47BIBSYS_NTNU_UB:51212133650002203,ie=47BIBSYS_NMBU:5125993220002213,ie=47BIBSYS_NLA:5131219200002228,ie=47BIBSYS_MF:5128211320002227,ie=47BIBSYS_MH:5125391120002296,ie=47BIBSYS_HIB:5134925840002221,ie=47BIBSYS_HVO:5138469310002220,ie=47BIBSYS_HIO:5166313490002218,ie=47BIBSYS_HIT:5141235480002210,ie=47BIBSYS_HIOA:5166278170002212,ie=47BIBSYS_HIM:5134265290002223,ie=47BIBSYS_HH:5149075130002214,ie=47BIBSYS_BI:5172882850002215,ie=47BIBSYS_FHS:5118701700002275,ie=47BIBSYS_NETWORK:71520532450002201,language=eng,view=UBIS&svc_dat=viewit&u.ignore_date_coverage=true&user_ip=10.16.56.140&req.skin=primoView online Book Python data science handbook : essential tools for working with data VanderPlas, Jacob T., Sebastopol, Calif. :, O'Reilly, XVI, 529 sider ;, cop. 2017., isbn:978-1-491-91205-8,
The course description is retrieved from FS (Felles studentsystem). Version 1