Introduction to Data Science (DAT540)

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).

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

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

Introduction to Programming (DAT120), Probability and Statistics 2 (STA500)

Fagperson(er)

Head of Department:

Tom Ryen

Course coordinator:

Antorweep Chakravorty

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.

Admission to Single Courses at the Faculty of Science and Technology

Exchange programme at Faculty of Science and Technology

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Book Python for data analysis : data wrangling with pandas, NumPy, and Jupyter McKinney, Wes, McKinney, Wes, Sebastopol, Calif., O'Reilly, 550 s., 2022, isbn:9781098104030, Både 2. og 3. utgave kan benyttes / Both the 2nd and 3rd edition can be used Book Python for data analysis : data wrangling with Pandas, NumPy, and IPython McKinney, Wes, Beijing, O'Reilly, xvi, 528 sider, October 2017, isbn:978-1-491-95766-0, Både 2. og 3. utgave kan benyttes / Both the 2nd and 3rd edition can be used Book Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Coelho, Luis Pedro,, Richert, W., Birmingham, England :, Packt, 1 online resource (394 pages), 2018., isbn:1-78862-222-7, 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