Course details
Faculty | Health Sciences | ||
Department | Medicine | ||
Education level | Postgraduate / Master of Science | ||
Course code | B1 | Semester | 2 |
Course title | Big Data Analytics | ||
Independent teaching activities | Hours per week | ECTS | |
Lectures | 2 | ||
Practice | 6 | ||
Total | 8 | 7,5 | |
Coursetype | General setting course, skills development | ||
Prerequisite courses | None | ||
Teaching and assessment language | English |
Learning outcomes
Objective
The course aims to introduce students to the basic concepts comprising big data analytics, i.e. characteristics of big data applications, contemporary big data architectures, exploration and visualization of big data, knowledge extraction from big data.
Knowledge
Upon completion of the course, graduate students will be familiar with:
- Big data challenges and advantages
- Popular big data domains
- Big data analysis tasks
- Machine learning techniques used for big data analytics problems
Skills
The course participants upon completion will be able to:
- Query big data infrastructures
- Visualize and (pre)process big data collections
- Understand the fundamental machine learning techniques and algorithms
- Apply machine learning techniques (classification, clustering, regression analysis, outlier/deviation detection) to pilot problems
- Select the most efficient algorithm, based on problem requirements
- Design the methodology for big data analysis problems of medium complexity
Course contents
- Introduction to Big data analytics: Definitions Examples Application areas
- Modeling big data – Big data management architectures
- Data exploration/visualization
- Data Preparation and Preprocessing
- Machine learning techniques (Part 0): Model evaluation
- Machine learning techniques (Part I): Classification, Overview Definitions Algorithms
- Machine learning techniques (Part II): Clustering, Overview Definitions Algorithms
- Machine learning techniques (Part III): Regression, Overview Definitions Algorithms
- Machine learning techniques (Part IV): Outlier/deviation detection, Overview Definitions Algorithms
- Machine learning techniques (Part IV): Ensemble methods, meta-learning
- Automated machine learning: Overview, techniques, hyper-parameter optimization
Teaching methods | Face to face Distance learning | |
Use of information and communication technologies (ICT) | Use of ICT in Teaching- Moodle Virtual learning environment (VLE) (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process) | |
Use of ICT in Communication with students (email, instant messaging via Moodle) | ||
Module structure | Work Hours per Semester | Activity |
Lectures | 55 | |
Exercises (Quiz) | 10 | |
Exercises (Wikis) | 10 | |
Exercises (Online discussion fora) | 20 | |
Exercises (Study relevant papers) | 20 | |
Essay background work | 40 | |
Essay writing | 45 | |
Overall work for the course | 200 | |
Assessment Methods | Written assignment, in English, approximately 2,500 words long, to be submitted by each student at the end of the course | |
Assessment of knowledge at the beginning and the end of the course with short-answer questions and essays development | ||
Weekly quizes, with multiple choice questions | ||
Assessment based on comments submitted by each student in online discussion fora |
Recommended bibliography
- Introduction to Data mining, P. Tan, M. Steinbach & V. Kumar, Addison Wesley, 2005.
- Data Mining; Concepts and Techniques, 2nd edition, J. Han and M. Kamber, Morgan Kaufmann, 2006.