Syllabus

Select major*

Ternopil Ivan Puluj National Technical University

Факультет комп'ютерно-інформаційних систем і програмної інженерії

Кафедра комп'ютерних наук

Data Mining

syllabus

Major 122 - Комп’ютерні науки та інформаційні технології (бакалавр)
Field of knowledge 12 Інформаційні технології
Academic degree bachelor's
Course
Course type required
special education
Study start course 1
Semesters 1
Form of education full-time
Study hours structure
16– lectures
32– laboratory classes
ECTS credits 4
Form of final examination credit
Lecturer
Academic degree Cand. Sc.
Academic title Assoc. Prof.
Full name Козбур Галина Володимирівна
Prerequirements (prerequisite courses)
Students must have basic knowledge of higher mathematics, probability theory and mathematical statistics, programming, theory of algorithms. 
Course goals and learning objectives
The purpose of the study is for students to master modern methods, tools and technologies of data processing, search in large data sets of practically useful knowledge and patterns necessary for business and other decisions, the ability to analyze data and present the results of such analysis. 
Course description
Lectures 1. Introduction to Data Mining. Stages, models, methods of Data Mining. The main tasks. Data, their types.
2. Statistical methods of numerical data processing. Types of data distributions. Box charts.
3. Fundamentals of machine learning. The problem of classification. Decision tree as a classifier. Criteria for division into classes. Algorithm retraining. Metric methods of classification. Quality metrics of classifiers. Solutions tree ensembles.
4. The problem of clustering. Iterative and hierarchical methods of clustering. Types of metrics. Clustering model validation techniques.
5. The regression problem. Single-factor and multiple regressions. Quality metrics for the regression model. Multicollinearity of signs.
6. Data visualization. Basic rules of infographics. Basic concepts of graphic design. Time series and their application. Trends.
Laboratory classes #1. Data Analysis in Spreadsheets
#2. Intermediate Spreadsheets
#3. Pivot Tables in Spreadsheets
#4. Data Visualization in Spreadsheets
#5. Python Data Science Toolbox (Part 1)
#6. Python Data Science Toolbox (Part 2)
Assessment criteria
The form of the final semester control is a credit.
The final semester grade consists of the sum of points obtained for laboratory work.
The points earned for the semester are automatically multiplied by 4/3. 
Course author
Cand. Sc., Assoc. Prof. Козбур Галина Володимирівна 
Дата останнього оновлення: 2020-10-29 12:48:57