Information Package / Course Catalogue
| Business Administration | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Econ., Admin. and Social Sciences | ||||
| Course Code | MGMT 434 | ||||
| Course Title in English | Data and Analytics Driven Management | ||||
| Course Title in Turkish | Veri ve Analitik Temelli İşletme | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Intermediate | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 136 hours per semester | ||||
| Number of Credits | 5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Undergraduate Students | ||||
| Overall Educational Objective | Main objectives in this course is to teach students how to use data science for competitive advantage and how to apply data analysis for business growth. In order to achieve this objective, students will be guided to (a) recognize the importance of data-driven business decisions for successful organizations; (b) understand how businesses create the right data analytics strategy and apply the different types of data analytics for their needs and goals, and (c) gain practical experience to develop solid business case within group project which covers the journey from data collection to value creation. | ||||
| Course Description | This course aims to prepare students to understand the dynamics of data analytics in business management. This course teaches the scientific process of transforming data into insights for making better business decisions and creating successful organizations. It covers the methodologies, issues, and challenges related to analyzing business data. The examples and use-cases from different industries will objectify students’ understanding for the concepts. Finally, students will apply business analytics algorithms and methodologies to business problems within the group project. | ||||
| Course Description in Turkish | Bu ders, işletme yönetiminde veri analitiğine ilişkin dinamikleri anlamaları konusunda öğrencileri geliştirmeyi hedeflemektedir. Ders, veriden iç görü yaratarak daha iyi iş kararları vermeyi ve daha başarılı organizasyonlar yaratmayı sağlayacak bilimsel araçlar sunmaktadır. Bu amaçla, veri temelli iş analitiğine ilişkin ilgili metodolojileri, konuları ve zorlukları aktarmaktadır. Ders kapsamında sunulacak örnekler ve senaryolar ile öğrencilerin konseptleri daha somut şekilde algılaması sağlanacaktır. Son olarak, öğrenciler grup projeleri kapsamında, çeşitli iş problemleri üzerinde iş analitiği algoritmaları ve metodolojilerini uygulayacaklardır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) approach business problems data-analytically 2) think systematically about whether and how data analytics improve business performance 3) develop business analytics ideas, analyze data using business analytics software, and generate business insights |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 |
|---|---|---|---|
| 1) Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | |||
| 2) Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | |||
| 3) Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | |||
| 4) Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | |||
| 5) Demonstrates individual and professional ethical behavior and social responsibility | |||
| 6) Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | |||
| 7) Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | |||
| 8) Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | |||
| 9) Displays computer proficiency to support problem solving and decision-making | |||
| 10) Demonstrates teamwork, leadership, and entrepreneurial skills | |||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | S | |
| 2) | Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | H | Project |
| 3) | Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | H | Project |
| 4) | Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | S | |
| 5) | Demonstrates individual and professional ethical behavior and social responsibility | N | |
| 6) | Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | N | |
| 7) | Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | S | Project |
| 8) | Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | H | Project |
| 9) | Displays computer proficiency to support problem solving and decision-making | N | |
| 10) | Demonstrates teamwork, leadership, and entrepreneurial skills | H | Project |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | S |
| Prepared by and Date | BURAK KUZUCU , May 2023 |
| Course Coordinator | CEYHAN MUTLU |
| Semester | Fall |
| Name of Instructor |
Course Contents
| Week | Subject |
| 1) | Introduction to Concepts |
| 2) | New Oil, New Way, New Roles |
| 3) | Data to Value: Collection and Management |
| 4) | Data to Value: Analysis and Modeling |
| 5) | Data to Value: Storytelling and Decision-Making |
| 6) | Statistics Toolset: Predictive, Prescriptive, and Experimental Analytics |
| 7) | Technology Toolset: Big Data, BI, Data Mining, and More |
| 8) | Demystifying AI/ML/DL |
| 9) | Machine Learning for Business Challenges |
| 10) | Conversation Primer: Machine Learning Terminology |
| 11) | Exploring the Machine Learning Toolset |
| 12) | Data Science of Economics, Banking, and Finance |
| 13) | Data Science of Retail, Sales, and Commerce |
| 14) | Data Science of Media and Entertainment |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | [Recommended] 1. HBR Guide to Data Analytics Basics for Managers, by Harvard Business Review 2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost and Tom Fawcett | ||||||||||||||||||
| Teaching Methods | For achieving learning objectives, the course will entail a set of related videos, readings, lectures, problem-solving activities and creative processes as well as interactions with real life businesses. With readings and lectures, the students will be able to grasp the key issues of data analysis in business management whereas with case studies and discussions, they will find the opportunity to expand their perspectives and apply the theoretical knowledge to real life situations. The course will cover basic terminology and theoretical structure as well as practical implications. The methods which will be used throughout the course are real life case studies, group projects, presentations, in-class discussions and key note speaker addresses. Every member of the class is expected to freely share her/his knowledge, ideas and questions with the group without any concern. Throughout the course, experiential, constructivist, research-based and reflective teaching strategies are used. In all kinds of teaching and learning activities, student participation, active learning and learning by doing are essential. | ||||||||||||||||||
| Homework and Projects | The students will be making one presentation as individual for one the lectures during course. In this way, they will contribute the content and scope of that lecture. In addition to this individual assignment, there will be a group project which will be studied almost all semester and presented by the end of the term. This project will mainly focus on one solid business case which covers the journey from data collection to value creation. | ||||||||||||||||||
| Laboratory Work | None | ||||||||||||||||||
| Computer Use | Personal notebook | ||||||||||||||||||
| Other Activities | None | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
kuzucub@mef.edu.tr Students are always free and encouraged to give feedback and ask questions about the course via e-mail to instructor (kuzucub@mef.edu.tr). In this course, active participation is key to learning and applying, as for a topic like international business, new ideas can be generated through questioning, brain storming and discussion. Thus the grading of the class participation will be done based on the quality of active student participation and contribution to in-class activities. Students are expected to attend all sessions and be in class on time. When they can not attend due to a sickness (which should require a report from a full facility hospital) or an excuse accepted my MEF regulations, they should inform the instructors by mail. As the feedback and questions are very valuable for making the course a distinctive learning experience, students may visit the instructors during office hours or send e mails, for any course related issues. Academic dishonesty and plagiarism will be subject to the YÖK disciplinary regulation. |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 3 | 1 | 84 | ||
| Project | 2 | 15 | 2 | 34 | |||
| Midterm(s) | 2 | 7 | 2 | 18 | |||
| Total Workload | 136 | ||||||
| Total Workload/25 | 5.4 | ||||||
| ECTS | 5 | ||||||