Selected Sections of Data Science (КН)
Type: Normative
Department: system design
Curriculum
| Semester | Credits | Reporting |
| 9 | 8 | Exam |
Lectures
| Semester | Amount of hours | Lecturer | Group(s) |
| 9 | 32 | Associate Professor Lyashkevych V. Y. | ФеІм-11, ФеІм-12, ФеІм-13, ФеІм-14 |
Laboratory works
| Semester | Amount of hours | Group | Teacher(s) |
| 9 | 48 | ФеІм-11 | Associate Professor Lyashkevych V. Y. |
| ФеІм-12 | Associate Professor Lyashkevych V. Y. | ||
| ФеІм-13 | Associate Professor Lyashkevych V. Y. | ||
| ФеІм-14 | Associate Professor Lyashkevych V. Y. |
Опис навчальної дисципліни
The discipline “Selected sections of data science” is a normative discipline in the specialty 122 – Computer Science for the educational program “Computer Science”, which is taught in 1 semester in the amount of 8 credits (according to the European Credit Transfer System ECTS).
The discipline is designed to provide participants with the necessary knowledge required to master the basic concepts related to the organization and use of data, the use of data technologies. Therefore, the discipline provides both an overview of the basic concepts and tools for working with data and the tools needed to solve typical tasks when using, configuring environments and technologies for working with data, developing programs and program interfaces.
The purpose of studying the discipline “Selected Sections of Data Science” is to master the basic concepts, theoretical knowledge of data, the capabilities of information systems built on the basis of data processing and analysis, data visualization, building pipelines for data analysis and transformation with subsequent use in various subject areas of human activity to solve various kinds of tasks and business problems. The goal is to be able to configure data services, design data processing stages, and develop programs for data processing.
Recommended Literature
- 1. Christopher M. Bishop (2018) Pattern Recognition and Machine Learning, 738p.
- 2. Sarah Guido (2016) Introduction to Machine Learning with Python: A Guide for Data Scientists, 400p.
- 3. EMC Education Services (2015) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 432p.
- 4. Cole Nussbaumer Knaflic (2015) Storytelling with Data: A Data Visualization Guide for Business Professionals, 288p.
- 5. Peter Bruce (2017) Statistics for Data Scientists: 50 Essential Concepts, 298p
- 6. Data Mining: The Complete Guide. – Columbia Engineering, 2023. URL: https://bootcamp.cvn.columbia.edu/blog/data-mining-guide/
- 7. Paul Crickard. Data Engineering with Python – Birmingham: Packt Publishing, 2020. – 337 p. -ISBN 978-1-83921-418-9.
- 8. Wang L., Fu X. Data Mining with Computational Intelligence. –Springer, 2005. –280 p.
- 9. Wes McKinney. Python for Data Analysis – Sebastopol: O’Reilly Media, 2018. – 522 p. – ISBN 978-1-491-95766-0.
- 10. Joakim Sundnes. Introduction to Scientific Programming with Python – Lysaker: Simula SpringerBriefs, 2020, Volume 6. – ISBN: 978-3-030-50355-0. (eBook)
- 11. Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser. Data Structures & Algorithms in Python. Wiley: Courier Westford, 2013. – 748 p. (eBook)
- 12. Massimo di Pierro. Annotated Algorithms in Python – Chicago: Experts4Solutions, 2017. – 227 p. – ISBN: 978-0-9911604-0-2.