An MBA-level introduction to using data as a decision-making tool, this course bridges the gap between describing what happened and recommending what to do next, with predictive modeling (regression, clustering, classification) feeding into prescriptive optimization. You'll work in Python through hands-on exercises on real industry datasets, complete two homeworks and a final project, and face frequent quizzes alongside a midterm and final. It complements the operations, finance, and marketing tracks by giving you the quantitative toolkit managers increasingly expect, and serves as a practical foundation for any later coursework or thesis work involving empirical business problems.
→ STARS müfredatı (resmi syllabus)
ECTS - Workload Table: Activities Number Hours Workload Preperation for final project 1 20 20 Quiz 5 1 5 Individual or group work 14 4 56 Final project 1 1 1 Preparation for Quiz 5 2 10 Course hours 14 3 42 Homework 2 3 6 Preparation for Midterm exam 1 10 10 Midterm exam 1 3 3 Total Workload: 153 Total Workload / 30: 153 / 30 5.1 ECTS Credits of the Course: 5
Bu kalemler %110'e toplanıyor, yani 100'ü aşıyor. İki sebebi olabilir: bonus bir kalem vardır ya da izlencenin tablosunda aynı satır iki kez yazılmıştır. Hangisi olduğunu dersin STARS izlencesinden doğrula. Hesap makinesi bu yüzden kapalı.
Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.
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.
Course Learning Outcomes: Course Learning Outcome Assessment Students will be able to analyze datasets to uncover insights and identify trends using Python and business analytics techniques. Midterm exam Regular Quiz Final Project Students will be able to develop effective data visualizations to communicate findings clearly and persuasively. Midterm exam Students will be able to apply machine learning methods, such as regression, clustering, and classification, to solve real-world business problems. Midterm exam Final Project Students will be able to formulate and solve optimization problems to support data-driven decision-making. Homework Students will be able to synthesize predictive and prescriptive analytics techniques to recommend optimal actions in various business scenarios. Final Project