IT dept
This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.
- Учитель: Goral Ercan
- Учитель: Kian Jazayeri
The course will introduce basic and fundamental programming constructs and techniques through using the C++ programming language in order to generate algorithmic solutions to problems. Upon completion of the course, students will learn an introduction to algorithms, solving problems by flowcharts and pseudo codes, header files, data types, arithmetic & logic operators, control statements (if, if/else, switch-case) and use them as inner statements, loop statements (while, do/while, for), functions, standard functions of programming language, random number generation and their area of use, user-defined functions, global and local variables, recursion, arrays, searching algorithms on arrays, sorting algorithms on arrays, pointers, pointer operators, using pointers with arrays and functions. In the laboratory hours, students are writing full programs or modifying existing programs for other solutions.
- Учитель: Opetunde Ibitoye
- Учитель: Kian Jazayeri
- Учитель: Tolgay Karanfiller
- Учитель: Cannur Onoral
- Учитель: Adacha Kwala
Data mining has emerged as one of the most exciting fields in Computer Science because of the growth of computerized data collections, which become more and more available in our modern digitalized world. Data mining have roots in the fields of artificial intelligence, machine learning (including classification, prediction, and clustering), statistical data analysis, data visualization and information retrieval. By completion of this course, students learn about different data mining methods such as classification, rule-based learning, decision trees and association rules. Students also learn about data selection and cleaning, machine learning techniques to “learn” about the “hidden” patterns in data, and the reporting and visualization of the resulting knowledge.
- Учитель: Sema Ozden
- Учитель: Oluwajana Dokun
- Учитель: Tugberk Kaya
- Учитель: Mehmet Yesiltas
- Учитель: Mary Agoyi
- Учитель: Oluwajana Dokun