Computer modeling and data visualization

Description: The course is aimed at training specialists who are able to effectively use computer modelling and visualisation to solve complex problems in their professional activities

Amount of credits: 6

Пререквизиты:

• Mathematical and computer modeling of physical processes

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• Explore methods for creating models of different systems and tools for visualising them, allowing data to be analysed and interpreted in a visual form

Objective

• To provide master students with knowledge of principles and methods of creating mathematical and computer models used in various scientific and engineering fields
• To teach undergraduates to conduct data analysis, interpret modelling results and assess the validity of the resulting models.
• To introduce modern software tools and libraries for data visualisation, allowing complex results to be converted into clear graphical formats
• To train master students to apply their knowledge in practice by solving real problems from different fields such as economics, ecology and engineering
• Develop skills in presenting and explaining modelling and data visualisation results to a variety of audiences including scientific, business and community groups.

Learning outcome: knowledge and understanding

• Understand the basic principles of mathematical and computer modelling, including numerical methods and algorithms used to create models
• Knowledge of modern software tools for data modelling and visualisation
• Understand the limitations and assumptions of models, and assess the accuracy and applicability of models for analysing real-world data

Learning outcome: applying knowledge and understanding

• Apply learnt modelling techniques to develop models describing real systems and processes (physical, biological, economic, etc.).
• Use knowledge of data types to select appropriate visualisation techniques, creating clear and informative graphs and charts
• Apply models to solve specific problems from different domains (e.g. forecasting, optimisation, simulation) and interpret the results correctly

Learning outcome: formation of judgments

• Develop the ability to critically evaluate the accuracy, reliability and applicability of models depending on their purpose and the assumptions used
• Reasonably select appropriate modelling and visualisation techniques based on the specifics of the data and analysis objectives, taking into account the limitations of each technique
• Form informed judgements about the resulting modelling results, distinguishing meaningful conclusions from possible errors or noise in the data

Learning outcome: communicative abilities

• Present complex data and modelling results using graphs, charts and other visualisations that make information accessible and understandable to different audiences
• Develop skills in clearly explaining and arguing the chosen modelling and visualisation techniques, justifying their decisions to teachers, colleagues or customers
• Be able to work in teams, effectively sharing results and findings, discussing problems and jointly finding solutions

Learning outcome: learning skills or learning abilities

• Develop the ability to quickly and independently learn new software tools and libraries for modelling and visualisation needed to solve problems in a rapidly changing environment
• Acquire skills in finding alternative approaches and methods when difficulties arise, as well as the ability to find and implement more effective solutions
• Ability to evaluate your models and visualisations, identify possible errors or deficiencies, and then apply new knowledge to improve them

Teaching methods

D. Serikbayev VKTU e-learning platforms

A practice-oriented approach where students solve real-world problems and develop modelling and visualisation projects, applying the acquired knowledge and skills in practice

Use of interactive presentations

Application of software tools for modelling and visualisation

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Introduction to data modelling and visualisation: basic concepts, aims and objectives of the course
• Mathematical models: types and classification
• Time series analysis
• Optimisation methods in modelling
• Fundamentals of probabilistic modelling
• Modelling complex systems
• Software tools for modelling
• Basics of data visualisation
• Interactive data visualisation
• Visualisation of multidimensional data
• Geospatial data and its visualisation
• Modelling of physical processes
• Simulation of random processes
• Modelling in biology and medicine
• Econometric models and their visualisation

Key reading

• Градов, В. М., Овечкин, Г. В., Овечкин, П. В., Рудаков, И. В. Компьютерное моделирование и визуализация данных. М.: КНОРУС, 2023. 240 с. ISBN 978-5-906818-79-9.
• Борзяк, А. А., Топорков, В. В., Емельянов, Д. М., Самочёрнов, О. И., Смирнов, Р. С. Основы компьютерного моделирования и визуализации: Учебное пособие для вузов. М.: Издательство "Лань", 2022. 244 с. ISBN 978-5-507-44951-4.
• Совертков, П. И. Компьютерное моделирование. М.: Издательство Лань, 2023. 424 с. ISBN 978-5-507-46708-2.
• Манцнер, Т. Визуализация данных. Полный и исчерпывающий курс для начинающих. М.: Эксмо, 2023. 464 с. ISBN 978-5-04106-797-7.

Further reading

• Уилке, К. Основы визуализации данных. Пособие по эффективной и убедительной подаче информации. М.: Эксмо; Бомбора, 2024. 352 с. ISBN 978-5-04-106457-0
• Дик Куслейка. Визуализация данных при помощи дашбордов и отчетов в Excel. М.: ДМК Пресс, 2022. 338 с. ISBN 978-5-97060-966-8
• Келлехер, Дж. Д., Тирни, Б. Наука о данных. Базовый курс. М.: Альпина Паблишер, 2022. 222 с. ISBN 978-5-9614-3170-4
• Акопов, А. С. Компьютерное моделирование : учебник и практикум для СПО / А. С. Акопов. — М. : Издательство Юрайт, 2019. — 389 с. ISBN 978-5-534-10712-8