Cloud computing has shifted infrastructure from something you rack and cable to something you provision through an API, and this course is about learning to think in that model — what you're really paying for, where your data lives, and how to assemble services into something that scales without falling over. The work is hands-on with AWS: seven labs walking through compute, storage (EBS, S3, EFS, Glacier), managed databases (RDS, DynamoDB, Redshift, Aurora), networking and auto-scaling, capped by a project you present at the end. It sits naturally after the networking and database courses in the CTIS track and gives you the deployment vocabulary that almost any modern software job assumes you already have.
→ STARS müfredatı (resmi syllabus)
You ARE ALLOWED to use GenAI tools while preparing your CTIS473 project reports, with the condition that you clearly specify with text coloring the parts of the document that were generated by AI, and adding a discussion paragraph to the end of every section that GenAI was used explaining the prompts that were used to generate the text and how you edited the text to be a part of your overall text flow. If a section or sections of your document were generated by AI and you used them without clear
İlk dosyayı sen atarsan — not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa — defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.
Lecture and lab attendance is compulsory in this course. Students are expected to attend at least 50% of all the lectures/lab hours and collect min. 20 out of 70 points from the weighted average of the lab work, midterm and team project to be eligible to attend the final exam. Otherwise, he/she will be assigned FZ.