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
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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 probl