| Course Title | Programming for data analytics |
| Course Code | M-227-04 |
| Level | Master |
| ECTS Credits | 9.00 |
| Faculty/unit | Engineering Faculty |
| Field | Computer and information sciences |
| Course Type | Standard |
| Course Leader | Pavlyuk Dmitry - Dr. sc. ing. professor |
| Annotation | The course is designed to be accessible to major students with little or no programming experience and emphasizes writing programmes that can retrieve and manipulate a large amounts of data. The first half of the course focuses on intensive learning of Python programming language (base syntax, programme structures, and data types), while the second half of the course covers selected topics of data processing - work with multidimensional arrays (numpy), data frames (pandas), visualisation (metplotlib), and modelling (statsmodels, scikit-learn). |
| Aim | The aim of this course is to introduce the fundamental concepts behind software programming, and to give students practical hands-on experience reading and writing computer programs for manipulate large data sets. |
| LO1 | Able to design, develop, maintain, test and evaluate novel data analytics, machine learning, and artificial intelligence solutions and apply them for solving real-world problems (PLO8) |
| LO2 | Comprehensively understand and apply appropriate Python libraries, data structures and functions to data tasks
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| LO3 | Understand and critically evaluate the functional design paradigm for solving data analysis problems |
| LO4 | Understand how to use the Python programming language for data engineering/analysis |
| LO5 | Robustly test, evaluate and identify errors in coding, produce clear, concise and well-documented code |
| Required Literature | - McKinney, W. (2018), Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second edition., O’Reilly Media, Inc, Sebastopol, California.
- Wickham, H. and Grolemund, G. (2016), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, First edition., O’Reilly, Sebastopol, CA.
- Idris, Ivan. Python Data Analysis, Packt Publishing, Limited, 2014. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=1826990.
- Fischetti, Tony. Data Analysis with R, Packt Publishing, Limited, 2015. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=4191340.
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| Assessment Method | Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final grade but will provide learners with developmental feedback. The course will be a combination of lectures, in-class discussions, readings, practical assignments, timed interactive labs, and the final project. The examination will be conducted by the end of the course in a computer class and aims to solve practical tasks using Python. |
| Independent study | The independent study in the frame of the course is organised around practical assignments, which should be completed individually by each student and work around the project (development and preparation of the report). |
| Full-time |
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| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
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| Reports | 50 | | LO1, LO2, LO3, LO4, LO5 | | Presentations | 50 | | LO1, LO2, LO3, LO4, LO5 |
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