Data Science & Biotechnology
Theory: 2 hours/week | ECTS Units: 3
Tutoring in the English language is offered to Erasmus students
Learning outcomes
This course has a double objective: first to offer the theoretical background and the technical skills regarding data science and second, to make students understand how they can utilize data (i.e., biological data) and produce predictive models in biotechnology.
Upon completion of the course the students are expected to:
- Define data fundamentals
- Apply queries
- Collect data
- Prepare data
- Analyze data
- Produce conclusions
- Apply data mining techniques
Syllabus
This course will cover the following areas:
- Terms and theoretical background
- Predictive models
- Data types, quality, and pre-processing
- Decision models
- Descriptive statistics and visualization
- Using R language
- Data mining
- Data mining techniques’ application
Student performance evaluation
The performance of the students is evaluated through written exams at the end of the semester (70%) and through presentation of an assignment before the written exams (30%). The assignment is given to the students during the 4th week of the semester, and it is based on a real case scenario about data analytics, with a content relevant to the course. The students are expected to apply the methodologies that the course presents in a 10 min session followed by 5 minutes of QA.
Suggested bibliography
- Lantz, B. (2015). Machine Learning with R. Second Edition. Packt Publishing.
- Verikios, V.S, Kaglis, V. and Stavropoulos, I.K. (2015) Data Science via R language (in Greek). SEAV: Kallipos publishing.
- Simeonidis, P. and Gounaris, A. (2015). Databases, Data warehouses, and data mining with SQL Server: Laboratory Guide. SEAV: Kallipos publishing
- Provost, F. and Fawcett, T. (2013).Data Science for Business. O’Reilly Media, Inc: Sebastopol, Canada.
Indicative Journals:
- Big Data Research
- Data in Brief
- Computational Statistics & Data Analysis
- Statistical Analysis and Data Mining
- ACM Computing Surveys
Lecturer
Leonidas Anthopoulos (Course Coordinator)
Professor, Department of Business Administration, University of Thessaly