COURSE INTRODUCTION AND APPLICATION INFORMATION

Course Name
Introduction to Business Analytics and Big Data
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
BA 464
Fall/Spring
2
2
3
6
Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery Online
Teaching Methods and Techniques of the Course Discussion
Problem Solving
Case Study
Q&A
Application: Experiment / Laboratory / Workshop
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives Two contemporary business trends are going hand in hand: operations are becoming more computerized, and decisions are becoming more fact driven. The effective use of data to drive timely, precise, and profitable decisions has been a critical strategic advantage for all kinds and sizes of companies. Thus, modern management is inseparable from business analytics. This course aims to equip business students with concepts, methods, and tools to turn business data to insights, and drive their business decisions. For this purpose, the course introduces a functional (rather than operational) understanding of essential data analysis techniques and their application to a variety of business problems, using essential software tools. The course is anchored on the value of these techniques to provide insights towards business decisions. To this end the course aims to develop critical thinking and alertness about data and analysis (including those conducted by someone else), and an ability to identify opportunities to create business value from data analysis. The course also covers the large data sets -so called big data- and special requirements and approaches to their analysis.
Learning Outcomes The students who succeeded in this course;
  • Construct common pipelines in business analytics suitable for given method.
  • Prepare data using R statistical platform which results in clean data suitable for most algorithms
  • Determine which of the exploratory and predictive methods is well suited for the business problems in hand.
  • Produce visual and numerical summaries of given data set suitable to explore a inform the business problem in focus.
  • Produce predictive models for given business problems.
Course Description This course focuses on methods and tools for analysis of data. It covers basics use of a common statistical platform (R), such as essential data handling tasks. Starting off with exploratory data visualization and summarizing methods,, the course proceeds into predictive methods from basic (e.g. linear models) to more complex (data mining methods such as decision trees or association rule analysis). Students develop both theoretical knowledge and hands on skills for translating business problems into data analysis problems, and exploring answers to those questions. The methods covered are overlaid onto an agile problem solving framework to facilitate application to ill-understood problems as well as more straightforward ones.
Related Sustainable Development Goals

 



Course Category

 Core Courses
Major Area Courses
X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Introduction, concepts Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 1
2 Analytics process, prediction Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 2
3 Exploratory analytics by data visualization Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 3
4 Linear regression Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 6
5 Model evaluation Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 5
6 Bayes classifier Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 8
7 Regression trees and classifier evaluation Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 9
8 Logistic regression and profiling Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 10
9 Association rules Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 14
10 Cluster analysis Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 15
11 Time series Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 16
12 Time series prediction Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 17&18
13 Big data techniques and tech stack, example: summarizing data Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 17
14 Advanced techniques: text analysis, social network analysis Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons. Chapter 19&20
15 In-class discussion
16 Review of the semester
Course Notes/Textbooks

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

ISBN: 978-1-118-87936-8
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
4
40
Portfolio
Homework / Assignments
5
40
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
1
20
Final Exam
Total

Weighting of Semester Activities on the Final Grade
10
100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
2
32
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
2
Study Hours Out of Class
13
2
26
Field Work
Quizzes / Studio Critiques
4
4
Portfolio
Homework / Assignments
5
10
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
1
24
Final Exams
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
 Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to solve problems with an analytical and holistic viewpoint in the field of business administration.

 X
2

To be able to present the findings and solutions to the business problems in written and oral formats.
3

To be able to interpret the application of business and economic concepts, and philosophies at the national and international levels.

 X
4

To be able to use innovative and creative approach for real-life business situations.
5

To be able to demonstrate leadership skills in different business situations.
6

To be able to interpret the reflections of new technologies and softwares to business dynamics.   

 X
7

To be able to integrate knowledge gained in the five areas of business administration (marketing, production, management, accounting, and finance) through a strategic perspective.
8

To be able to act in accordance with the scientific and ethical values in studies related to business administration.

 X
9

To be able to work efficiently and effectively as a team member.
10

To be able to have an ethical perspective and social responsiveness when making and evaluating business decisions.

 X
11

To be able to collect data in the area of business administration and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1).
12

To be able to speak a second foreign at a medium level of fluency efficiently.
13

To be able to relate the knowledge accumulated throughout the human history to their field of expertise.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest