Information Package / Course Catalogue
BUS 432 Business Analytics
MEF University
Degree Programs
Business Administration
General Information For Students
Diploma SupplementErasmus Policy Statement
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 BUS 432
Course Title in English Business Analytics
Course Title in Turkish İş Analitiği
Language of Instruction EN
Type of Course Flipped Classroom
Level of Course Advanced
Semester Spring
Contact Hours per Week
Lecture: 3 Recitation: Lab: Other:
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)
Expected Prior Knowledge None
Co-requisites 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 Description in Turkish Gerçek hayat verisi ve problemleri, şaşırtıcı olmayan bir şekilde öğrenme ortamlarından daha karmaşıktır. İş problemlerini anlamanın temeli ise veridir. Veriden anlam çıkarma, çözümler üretme ve sonuçların iletişimini kurabilmek geniş bir yetenek yelpazesine ihtiyaç duyar. Bu ders, bütün analitik süreci, uygulamalı bir şekilde gerçek hayat problemleri üzerinden göstermeyi hedeflemektedir.

Course Learning Outcomes and Competences

Upon 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) 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 Project
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 S Participation
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 N
5) Demonstrates individual and professional ethical behavior and social responsibility S Participation
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 N
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 H Exam
10) Demonstrates teamwork, leadership, and entrepreneurial skills N
11) Displays learning skills necessary for further study with a high degree of autonomy N
Prepared by and Date BERK ORBAY , January 2019
Course Coordinator CEYHAN MUTLU
Semester Spring
Name of Instructor Öğr. Gör. BERK ORBAY

Course Contents

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 MethodsDesign 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 ProjectsThere 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
Computer UsePersonal notebook
Other Activities
Assessment Methods
Assessment Tools Count Weight
Homework Assignments 1 % 30
Project 1 % 40
Final Examination 1 % 30
TOTAL % 100
Course Administration orbayb@mef.edu.tr

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 3 8 2 30
Final Examination 1 16 2 18
Total Workload 132
Total Workload/25 5.3
ECTS 5