Intelligent Data Aanalysis in Climate Management

Major: Applied Ecology and Balanced Nature Management
Code of subject: 7.183.01.E.020
Credits: 4.00
Department: Ecological Safety and Nature Protection Activity
Lecturer: O.N. Kuz, PhD, Assoc. Prof.
Semester: 2 семестр
Mode of study: денна
Мета вивчення дисципліни: Studying the methods, models and algorithms of intelligent data analysis and processes used in the tourism industry, the principles of building and functioning of intelligent data analysis systems, as well as acquiring practical skills in using software tools for processing and analyzing large volumes of data in the field of tourist services.
Завдання: The study of an educational discipline involves the formation of competencies in students of education: general competences: - the ability to conduct research at the appropriate level, to have research skills that are manifested in the ability to form (by making presentations or presenting reports) new products in the chosen field, to choose the appropriate directions and appropriate methods for their implementation, taking into account the available resources; - skills in using information and communication technologies, implementing computer programs and using existing ones; professional competences: - the ability to use scientific research methods in the field of tourism and recreation; - the ability to analyze the geospatial organization of the tourist process and project its development on the basis of sustainability; - the ability to research modern trends in the development of the world market of tourist services, the dynamics and structure of international tourist flows, to identify and evaluate their qualitative characteristics; - the ability to perform intellectual data analysis in the tourism industry.
Learning outcomes: The student should know: basic concepts and definitions of data mining; basic methods of models construction to identify dependencies in large data arrays; modern software tools for data mining in tourism; comparison criteria for models and methods of data mining; elements of the theory of artificial neural networks; differences between conventional and intelligent systems. The student should be able: to compare models and methods of data mining; to choose a particular type of a model and data mining method at solving the practical problems of tourism industry; to use modern software tools for data mining in tourism industry; to solve the problems of data clustering, classification and analysis in tourism; to analyze the results of software tools usage for data mining while solving applied problems in the field of tourism.
Required prior and related subjects: Informatics, statistics in tourism, mathematics (prerequisites).
Summary of the subject: Introduction to Data Mining in tourism. Basic terms and main features of data mining. The main stages of data mining. Typical tasks of data mining. Preliminary data processing. The accumulation of data. Typical procedures for data processing. Normalization min /max. Standardization of data. Data mining models. Regression analysis. Approximation of functions by the least squares method. The task of data clustering. K-means algorithm for clustering. The measure of the group quality. Associative rules. The problem of data classification. Naive Bayesian classifier. The method of the nearest neighbor, the samples method, the method of k nearest neighbors. Artificial neural networks. The general model of a neuron. Function of activation. Single-layer and dual-layer neural network. Kohonen networks (self-organizing networks). The use of neural networks for clustering data. Decision trees. Classification and regression trees. The CART algorithm. C4.5 algorithm. Overview of software tools for data mining. Prospects of data mining methods and tools for the tourism industry.
Опис: Lectures and laboratory classes in the discipline "Intellectual analysis of tourism industry data" discuss topics that are relevant for specialists in the field of service, in particular tourism. Not only theoretical foundations, models and methods of intellectual data analysis are studied, but also practical aspects of their application in tourism. The software tools of intellectual data analysis are analyzed and the skills of their use in the professional activities of future tourism specialists are formed.
Assessment methods and criteria: The current control (40%) - labs exercises, oral questioning. Final control (60%) - examination.
Критерії оцінювання результатів навчання: Laboratory works: 7 works - timely completion of work and successful defense - 5 points; performance of work and successful defense - 4 points; performance of work and insufficient coverage of issues in the defense - 3 points; performance of work - 2-1 points. Tests: 8 tests on topics of 2 questions - correct answer - 0.5 points; incorrect answer - 0 points; Examination control: written component – 43 points; oral component - 10 points.
Порядок та критерії виставляння балів та оцінок: 100-88 points - certified with an “excellent” grade - High level: the student demonstrates an in-depth mastery of the conceptual and categorical apparatus of the discipline, systematic knowledge, skills and abilities of their practical application. The mastered knowledge, skills and abilities provide the ability to independently formulate goals and organize learning activities, search and find solutions in non-standard, atypical educational and professional situations. The applicant demonstrates the ability to make generalizations based on critical analysis of factual material, ideas, theories and concepts, to formulate conclusions based on them. His/her activity is based on interest and motivation for self-development, continuous professional development, independent research activities, implemented with the support and guidance of the teacher. 87-71 points - certified with a grade of “good” - Sufficient level: involves mastery of the conceptual and categorical apparatus of the discipline at an advanced level, conscious use of knowledge, skills and abilities to reveal the essence of the issue. Possession of a partially structured set of knowledge provides the ability to apply it in familiar educational and professional situations. Aware of the specifics of tasks and learning situations, the student demonstrates the ability to search for and choose their solution according to the given sample, to argue for the use of a particular method of solving the problem. Their activities are based on interest and motivation for self-development and continuous professional development. 70-50 points - certified with a grade of “satisfactory” - Satisfactory level: outlines the mastery of the conceptual and categorical apparatus of the discipline at the average level, partial awareness of educational and professional tasks, problems and situations, knowledge of ways to solve typical problems and tasks. The applicant demonstrates an average level of skills and abilities to apply knowledge in practice, and solving problems requires assistance, support from a model. The basis of learning activities is situational and heuristic, dominated by motives of duty, unconscious use of opportunities for self-development. 49-00 points - certified with a grade of “unsatisfactory” - Unsatisfactory level: indicates an elementary mastery of the conceptual and categorical apparatus of the discipline, a general understanding of the content of the educational material, partial use of knowledge, skills and abilities. The basis of learning activities is situational and pragmatic interest.
Recommended books: Навчально – методичне забезпечення 1. Інтелектуальний аналіз даних в кліматичному менеджменті: конспект лекцій для студентів другого (магістерського) рівня вищої освіти спеціальності 183 «Технології захисту навколишнього середовища» / О.Н. Кузь, М.В. Руда – Електрон. дан. — Київ : ГО «МНГ», 2023. – 368 с. : табл. — оn-line. 2. Інтелектуальний аналіз даних в кліматичному менеджменті: словник основних термінів для студентів другого (магістерського) рівня вищої освіти спеціальності 183 «Технології захисту навколишнього середовища» / О.Н. Кузь, М.В. Руда – Електрон. дан. — Київ : ГО «МНГ», 2023. – 156 с. 3. Лабораторний практикум з дисципліни «Інтелектуальний аналіз даних в кліматичному менеджменті» для студентів другого (магістреського) рівня вищої освіти спеціальності 183 «Технології захисту навколишнього середовища» / О.Н. Кузь, М.В. Руда – Електрон. дан. — Київ : ГО «МНГ», 2023. – с. 64. : табл. — оn-line. 4. Методичні вказівки до самостійної роботи з дисципліни «Інтелектуальний аналіз даних в кліматичному менеджменті» для студентів другого (магістерського) рівня вищої освіти спеціальності 183 «Технології захисту навколишнього середовища» / О.Н. Кузь, М.В. Руда – Електрон. дан. — Київ : ГО «МНГ», 2023. – 12 с. : табл. — оn-line. 5. Електронний навчально-методичний комплекс з дисципліни розміщений за адресою: https://vns.lpnu.ua/course/view.php?id=985 Рекомендована література Базова 1. Аналіз даних та знань: навчальний посібник / В.В. Литвин, В.В. Пасічник: Магнолія 2006, 2023. - 276 с. 2. Інтелектуальні системи підготовки рішень: підручник / І.С. Єремєєв, О.Г. Гуйда — Гальветика. — 2021. — 376 с. 3. Інтелектуальний аналіз даних (+CD): практикум / Фісун М.Т., Кравець І.О., Казмірчук П.П., Ніколенко С.Г., - Л.: «Новий світ – 2000», 2021. — 162 с. 4. Аналіз даних та знань. Навчальний посібник / Литвин В.В., Нікольський Ю.В, Пасічник В.В. - Л.: «Магнолія 2006», 2023. — 276 с. 5. Інтелектуальний аналіз даних Data Mining: навчально-методичний посібник. – Кропивницький, ФОП Піскова М. А., 2022. – 112 с. Допоміжна 1. Кармелюк Г.І. Теорія ймовірностей та математична статистика: посібник з розв’язування задач. / Г.І. Кармелюк. – Центр учбової літератури, 2020 – 576 с. 2. Загальна теорія статистики : підручник / за ред. А. В. Непрана, І. А. Дмитрієва ; авт. кол.: І. А. Дмитрієв, О. А. Дмитрієва, О. М. Гіржева, А. В. Непран, Н. О. Бірченко, А. А. Воронкова, Н. В. Чуйко. ? Харків : ПП Іванченка, 2022. ? 720 с. Інформаційні ресурси Iнтелектуальний аналiз даних: Практикум [Електронний ресурс] : навч. посiб. для студ. спецiальностi 124 «Системний аналiз», освiтнiх програм «Системний аналiз i управлiння», «Системний аналiз фiнансового ринку» / Н. I. Недашкiвська; КПI iм. Iгоря Сiкорського. – Електроннi текстовi данi (1 файл: 6 Мбайт). – Київ : КПI iм. Iгоря Сiкорського, 2021. – 105 с.