304 APPLIED INFORMATICS
| Category: Mandatory Class Hours: 4 Laboratory Hours: 0 ECTS: 6 |
Course Type: Core Knowledge Prerequisite Courses: Language of Teaching and Exams: Greek Is the course offered to ERASMUS students: Yes (in English) |
Course Curriculum:
- Introduction to Programming. Historical review, basic algorithm concepts, iteration structures, control structures.
- Programming Languages. Differences between low and high level programming languages, compiling/interpreting programs, virtual machines and web programming.
- Algorithms. Analysis and design methods, procedures for designing, writing, debugging and compiling/interpreting programs
- Introduction to the R programming language. Arithmetic and Logical Operators, Comparison Operators. Check and repeat commands. Examples.
- Complex Data Structures: Define vector (vector), table (matrix, array), list (list), data frame (data frame) in the R programming language.
- Advanced Programming. Emphasis on the R pragmatic language.
- Building Functions. Functions with many results. Flashback. Special orders.
- Evaluating programs. Finding logical errors, analyzing and evaluating complexity and performance of basic algorithms. Comparison of search and sorting programs.
Literature:
1. D. Ioannidis, I. Athanassiasis, Statistics and Machine Learning with R: Theory and Applications, Tziola Publications, 2017, Eudoxus Code: 59384938
2. J. Verzani, Introduction to Statistics with R, Kleidaritmos Publications, 2016, Eudoxus Code: 50656357
3. M.J Crawley, Statistical Analysis with R, Broken Hills Publishers, 2013, Eudoxus Code: 32997808
4. V. Verykios, V. Kaglis, H. Stavropoulos, “The science of data in from the R language“ , 2016, Eudoxus Code: 320151
5. D. Karlis, I. Dzoufras, “Introduction to Programming and Statistical Analysis with R”, 2016, Eudoxus Code: 320222
