| School/Faculty/Institute | Faculty of Economics, Administrative and Social Sciences | ||||
| Course Code | BUS 432 | ||||
| Course Title in English | Business Analytics | ||||
| Course Title in Turkish | İş Analitiği | ||||
| Language of Instruction | EN | ||||
| Type of Course | Ters-yüz öğrenme | ||||
| Level of Course | İleri | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
|
||||
| Estimated Student Workload | 132 hours per semester | ||||
| Number of Credits | 5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites |
BUS 101 - Introduction to Business (Decision Making) |
||||
| Co-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Registration Restrictions | Only Undergraduate Students | ||||
| Overall Educational Objective | To understand the path to data science and gain firsthand experience and higher comprehension about the data analytics process with a real life project | ||||
| Course Description | Real life data and real life problems are expectedly more complex than any learning environment. Data is the core to understand business problems. Inferring meaning from data, producing solutions and communicating the results require a wide set of skills and tools. This course aims to present the whole analytics process with a hands-on approach to real life projects. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) communicate findings of analyzed data in a coherent and understandable way 2) manipulate data sets and creating summary tables, visualize data with the proper choice of tools (e.g. histogram, scatterplot, pie charts) 3) code with R and related packages (e.g. tidyverse) 4) perform reproducible research 5) apply basic data mining algorithms (e.g. regression, logistic regression) and interpret the output. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | |||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | |||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | |||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | |||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | |||||
| 6) Internalization and dissemination of professional ethical standards. | |||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | |||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | |||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | |||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | |||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | |||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | |||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,Homework,Derse Katılım |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Derse Katılım |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | Homework,Derse Katılım |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,Homework |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,Homework |
| Prepared by and Date | HANDE KARADAĞ , January 2019 |
| Course Coordinator | CEYHAN MUTLU |
| Semester | Spring |
| Name of Instructor | Öğr. Gör. BERK ORBAY |
| Week | Subject |
| 1) | Introduction to Business Analytics |
| 2) | Introduction to R |
| 3) | Introduction to R (II) and GitHub |
| 4) | Exploratory Data Analysis (Data Manipulation) |
| 5) | Exploratory Data Analysis (Visualization) |
| 6) | Reproducible Research |
| 7) | Interactive Analysis |
| 8) | Interactive Analysis |
| 9) | Presentations – I |
| 10) | Use of Cloud Computing in Business Analytics |
| 11) | Introduction to Machine Learning – I |
| 12) | Introduction to Machine Learning - II |
| 13) | Applications – II |
| 14) | Presentations – II |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | [Recommended] R for Data Science, H. Wickham & G. Grolemund, https://r4ds.had.co.nz/ | |||||||||||||||
| Teaching Methods | Design of this course is product centric. Students are expected to apply what they have learned in class and in their assignments into their individual and team works and present them. The medium of presentation is GitHub Pages, which we will call Progress Journals. This way the student will be able to demonstrate a proof of knowledge and skill in the form of an online portfolio. There will be short lectures aimed to introduce students to the subject and guide them through the obstacles. Then, students will be given a task to apply what they have learned by coding and analyzing. In these sessions, students are encouraged to collaborate and learn from each other. There will be discussions about business problems in general, analytics approaches, research and other relevant topics. Domain experts and special guests will be occasionally invited to give presentations about their experience. Concisely, it will be a very hands-on course directly building a product to improve coding and analysis skills. | |||||||||||||||
| Homework and Projects | There will be one group project. For this project, all group members will receive marks respective to their contribution. In general, students may freely communicate within their group and between groups. Collaboration is endorsed. Some group projects can be united under a major project. DataCamp will be used for assignments. | |||||||||||||||
| Laboratory Work | None | |||||||||||||||
| Computer Use | Personal notebook | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
|
|||||||||||||||
| Course Administration |
orbayb@mef.edu.tr The instructor may act as facilitator for class/group discussions and observe how each student contributes / adds value to the discussed topic. The topic may either cover previous material or assigned new material from videos and book chapters. Homework, if assigned, must be submitted on time and in the requested format. Late submission will not be accepted. This is a flipped course where each student is expected to read assigned material and watch videos in advance, follow class and Blackboard, and actively participate. If you are sick on the day of the exam you need to submit a legitimate doctor’s report explicitly stating that your excuse prevents you from taking the exam in line with university regulations. Academic dishonesty and plagiarism will be subject to the YÖK disciplinary regulation. |
|||||||||||||||
| 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 | 3 | 8 | 2 | 30 | |||
| Final Examination | 1 | 16 | 2 | 18 | |||
| Total Workload | 132 | ||||||
| Total Workload/25 | 5.3 | ||||||
| ECTS | 5 | ||||||