EMÜ660 - DECISION MAKING WITH ANALYTICS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DECISION MAKING WITH ANALYTICS | EMÜ660 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Team/Group Work Drill and Practice Case Study Project Design/Management Other: Individual Study | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | The objective of this course is to help students develop skills in using descriptive and prescriptive analytics to facilitate the decision-making process. Through this course, students will learn how to collect and reshape raw data, implement data visualizations, use mathematical optimization to solve and automate the solution process of complex decision problems and gain a foundational understanding of productivity tools, including basic Unix, version control (Git), and reproducibility. Students will be able to effectively apply analytics techniques to real-world decision problems and make data-driven decisions with confidence. | |||||
| Learning outcomes |
| |||||
| Course Content | ? Fundamentals of descriptive and prescriptive analytics ? Informed decision-making practices through the use of analytic tools ? Programming skills necessary for effective decision-making ? Principles and techniques for data wrangling and data visualization ? Programming skills for mathematical optimization ? Productivity tools for organizing and creating reproducible analytics project | |||||
| References | ? Irizarry, Rafael A. (2019). Introduction to data science. CRC Press, FL, US, ISBN-978-0-367-35798-6 ? Peng, R. D., & Matsui, E. (2016). The art of data science. A Guide for Anyone Who Works with Data. Skybrude Consulting, LLC, ISBN-978-1365061462 ? Albright S.C., & Winston, W. L. (2019). Business Analytics: Data Analysis and Decision Making, 7e. Cengage Learning, Inc., Boston, US. ISBN: 978-0-357-10995-3 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Fundamentals of descriptive analytics |
| Week 2 | Programming skills for decision-making: data types, vectors and vector arithmetic, indexing |
| Week 3 | Programming skills for decision-making: conditional expressions, loops and iteration, functions |
| Week 4 | Data visualization: plot types, graph components, layers, customization, scales, labels, colors |
| Week 5 | Data visualization: grouping, sorting, faceting, transformations, data visualization principles |
| Week 6 | Productivity tools for decision-making projects: basic unix, reproducible reports |
| Week 7 | Productivity tools for decision-making projects: git and github |
| Week 8 | Midterm Exam |
| Week 9 | Gathering and wrangling data: importing spreadsheets, web scrapping |
| Week 10 | Gathering and wrangling data: reshaping and tidying data, regex |
| Week 11 | Fundamentals of prescriptive analytics |
| Week 12 | Decision-making with mathematical optimization: formulating models |
| Week 13 | Decision-making with mathematical optimization: solving models |
| Week 14 | Project presentations and discussions |
| Week 15 | Study for the final exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 3 | 15 |
| Presentation | 1 | 5 |
| Project | 1 | 15 |
| Seminar | 0 | 0 |
| Midterms | 1 | 15 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 6 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 3 | 42 |
| Presentation / Seminar Preparation | 1 | 20 | 20 |
| Project | 1 | 50 | 50 |
| Homework assignment | 3 | 12 | 36 |
| Midterms (Study duration) | 2 | 15 | 30 |
| Final Exam (Study duration) | 1 | 80 | 80 |
| Total Workload | 36 | 183 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods in engineering within the scope of advanced industrial engineering studies through scientific research and evaluate knowledge and methods and implement them. | X | ||||
| 2. Implement advanced analytical methods and modeling techniques to design processes, products and systems in an innovative and original way and improve them | X | ||||
| 3. Have the competency to plan, manage and monitor processes, products and systems. | X | ||||
| 4. Evaluate the data obtained from analysis of the processes, products and systems, complete limited or missing data through scientific methods, develop data driven solution approaches. | X | ||||
| 5. Develop original methods for the efficient integration of the scarce resources such as man, machine, and material, energy, capital and time to the systems and implement these. | X | ||||
| 6. Effectively utilize computer programming languages, computer software, information and communication technology to solve problems in the field of industrial engineering. | X | ||||
| 7. Report and present advanced studies, outcomes/results and the evaluations on the design, analysis, planning, monitoring and improvement of processes, products and systems. | X | ||||
| 8. Are aware of the professional responsibility, describe the technological, economic and environmental effects of the industrial engineering applications, work as an individual independently and as a team member having an understanding of the scientific ethical values, take responsibility and lead the team. | X | ||||
| 9. Are aware of the up-to-date engineering applications, follow the necessary literature for advanced researches, have the competency to reach knowledge in a foreign language, to quote and implement them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest