Educational Data Mining Without Coding
Course code
IFI7368.DT
old course code
Course title in Estonian
Hariduslik andmekaeve programmeerimiseta
Course title in English
Educational Data Mining Without Coding
ECTS credits
6.0
Assessment form
assessment
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
The course aims to provide an overview of various data mining methods in an educational context using plain language and to implement them in RapidMiner for hands-on practice.
Brief description of the course
This course offers a practical introduction to data mining for students without a background in computer science or programming, focusing on its application in education. Participants will learn key data mining techniques, including both supervised (like Decision Trees and Neural Networks) and unsupervised methods (such as clustering), which are applicable across various fields beyond education, including e-commerce. Through hands-on exercises using free open-source software, students will practice finding patterns and predicting future trends in data. The course is designed for beginners, with detailed video lectures and practice seminars to ensure understanding. By the end, participants will be equipped to use data mining for informed decision-making in their areas of interest.
Learning outcomes in the course
Upon completing the course the student:
- understands what data mining is and is not about (including data mining objectives, categorization and process, its differences with educational data mining, goals of educational data mining, and methods to achieve those goals);
- gets started with RapidMiner;
- knows about data preparation;
- knows how to implement descriptive statistics, univariate, and multivariate data visualization, as well as visualization of a large number of variables;
- knows the main idea behind different classification methods and how to implement them;
- knows the main idea behind different regression methods and how to implement them;
- knows about model evaluation and feature selection methods;
- knows about model evaluation and feature selection methods;
- knows the main idea behind different clustering methods and how to implement them.
- understands what data mining is and is not about (including data mining objectives, categorization and process, its differences with educational data mining, goals of educational data mining, and methods to achieve those goals);
- gets started with RapidMiner;
- knows about data preparation;
- knows how to implement descriptive statistics, univariate, and multivariate data visualization, as well as visualization of a large number of variables;
- knows the main idea behind different classification methods and how to implement them;
- knows the main idea behind different regression methods and how to implement them;
- knows about model evaluation and feature selection methods;
- knows about model evaluation and feature selection methods;
- knows the main idea behind different clustering methods and how to implement them.
Teacher
Danial Hooshyar
Study programmes containing that course