Infopaket

Applied Program Packages

ВБ 2.2

Вибіркова дисципліна для ОП

First

2024/2025

5 Semester

5

Gain expertise in methods and algorithms for analytical processing and intelligent data analysis for tasks such as classification, forecasting, cluster analysis, associative rule discovery, and decision-making. Understand the methodologies and theoretical foundations of developing and applying application program packages, their structure, main components, and types, as well as stages and development trends. Become familiar with packages such as MatLab, SAS, SAS Enterprise Miner, their interfaces, and basic functionalities. Learn research methods grounded in fundamental mathematical statistics concepts and operations, utilizing cutting-edge tools for analysis, visualization, forecasting, decision-making, and management based on modeling and predicting random phenomena dynamics. Develop the ability to solve typical data analysis problems using basic random process models, leveraging Data Mining and Text Mining technologies in the decision-making process.

Full-time form

For successful completion of the "Applied Program Packages" course, students must possess competencies acquired during their studies of the following disciplines: "Discrete Mathematics", "Algorithmization and Fundamentals of Programming", "Algorithms and Data Structures", "Probability Theory and Mathematical Statistics", "Object-Oriented Programming", and "Design and Analysis of Computational Algorithms".

The "Applied Program Packages" course is designed to equip students with the tools and techniques of Metastock's technical analysis, visualization, and decision-making system, as well as the analysis and forecasting systems MatLab, SAS, SAS Enterprise Miner for investigating dynamic processes in macroeconomic, technical, technological, and financial systems. The development of standard solutions based on modeling and forecasting dynamic processes is explored. The objective of the "Applied Program Packages" course is to cultivate a comprehensive understanding of theoretical knowledge and practical skills in developing integrated application program packages, examining the structure and methods of organizing component interactions using built-in tools for analysis, visualization, data forecasting, and decision-making.

2. Tedrow K. Natural Language Processing with SAS®: Special Collection. New York: SAS Institute, 2020. 74 с. URL: https://support.sas.com/content/dam/SAS/support/en/books/free-books/nlp-with-sas.pdf 3. Gilliland M. Forecasting with SAS®: Special Collection. New York: SAS Institute, 2020. 72 с. URL: https://support.sas.com/content/dam/SAS/support/en/books/free-books/forecasting-with-sas.pdf 4. Profi M. SAS® and Open-Source Model Management: Special Collection. New York : SAS Institute, 2020. 148 с. URL: https://support.sas.com/content/dam/SAS/support/en/books/free-books/sas-and-open-source-management.pdf 5. Gibbs P. Exploring Modern Regression Methods Using SAS®: Special Collection. New York : SAS Institute, 2019. 141 с. URL: https://support.sas.com/content/dam/SAS/support/en/books/free-books/exploring-modern-regression-methods-special-collection.pdf

Lectures, laboratory classes, individual work

The assessment of the "Applied Program Packages" course is based on two content modules. The level of achievement of planned learning outcomes throughout the semester is determined by the results of completing laboratory work, writing control papers according to the given option, and ultimately, taking a written exam. Students who have scored less than the critical calculation minimum of 36 points are not permitted to take the exam. The recommended minimum for eligibility to take the exam is 36 points. The examination ticket includes theoretical questions and practical tasks encompassing all aspects of the academic discipline.

Ukrainian