FACULTY OF BUSINESS
Department of Logistics Management
BUS 220 | Course Introduction and Application Information
Course Name |
Data Analytics for Business and Economics
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Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
BUS 220
|
Spring
|
2
|
2
|
3
|
5
|
Prerequisites |
None
|
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Course Language |
English
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Course Type |
Required
|
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Course Level |
First Cycle
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Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Application: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
Course Coordinator | ||||||
Course Lecturer(s) | ||||||
Assistant(s) |
Course Objectives | Processing analysis of data is a requirement for all professionals in today’s digital environment. This course aims to develop fundamental data analytics skills necessary in the business and economic fields. |
Learning Outcomes |
The students who succeeded in this course;
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Course Description | This course aims to develop data processing and analysis skills required in the fields of business and economics. In this course students learn computer coding skills focused on data processes, with case studies in their fields. In contrast to coding courses for students aiming an expertise in computing, this course approaches algorithms in terms of their function in business and economics problems and focuses on features and applications of data processing patterns. In this applied course students learn the programming languages Python and R, which are very common in business practice and research. In addition, the course covers the properties of big data analytics and technologies used for it. The course consists of three modules: 1-Big data (2 weeks): technologies (Hadoop, MapReduce), competencies, real time data processing, possible value creation pipelines in big data 2-Statistical processing with R (6 weeks): Exploratory statistics in R. 3-Introduction to coding for data analytics with Python (6 weeks): data types, searching/sorting, list processing for statistical calculations, web scraping for data |
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Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
Week | Subjects | Related Preparation |
1 | MODULE 1: Big Data Big data technologies (Hadoop, MapReduce), competencies, real time data processing Goal: Understand essential data transformations in big data. Case study: Design a data process to aggregate stock data from POS transactions in a supermarket. | “Big Data Analytics: Concepts, Technologies, and Applications” https://aisel.aisnet.org/cais/vol34/iss1/65/?utm_source=aisel.aisnet.org%2Fcais%2Fvol34%2Fiss1%2F65&utm_medium=PDF&utm_campaign=PDFCoverPages |
2 | Big data: Possible value creation pipelines in big data. Goal: Understand real time or offline value creation pipelines in big data. Case study: Consider transport vehicles data for Izmir Municipality. Propose value creation pipelines to improve public services by providing service information. | |
3 | MODULE 2: Statistical Programming With R Getting started with R and Rstudio, R scripts, R panes, installing packages, R basics (objects, workspace, variable names), | Chapter 1 Introduction to Data Science; Chapter 1 R for Data Science https://rafalab.github.io/dsbook/ |
4 | R and programming basics: Data types and vectors; matrices; factors; data frames; | Chapter 2 Introduction to Data Science |
5 | lists; indexing; subsetting Case Sudy: US Gun murders | Chapter 4 Introduction to Data Science |
6 | Introduction to visualisation with ggplot2 package (grammar of graphs, aestetics, facets, transformations) Miles per Gallon and Diamond carat data sets | Chapter 3 R for Data Science https://r4ds.had.co.nz/index.html |
7 | Exploratory Data Analysis (Variation, missing values, covariation) | Chapter 7 R for Data Science |
8 | Reporting with Rmarkdown and Wrapping up with a case study Gapminder data set (GDP per capita, life expectancy and fertility) | Chapter 9 Introduction to Data Science |
9 | MODULE 3: Introduction to Python data processing patterns * Python editor and interface. The syntax and grammar and vocabulary. Simple data types.help system. * Python scripts | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 1 |
10 | * Loops * Design patterns with loops | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 2 |
11 | Exploratory Data Analysis (Variation, missing values, covariation | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 3 |
12 | Python data structures Functions | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 4 |
13 | Using files .csv | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 5 |
14 | Statistical calculations with "Matlib" and "statistics" libraries | “Introduction to Python Programming for Business and Social Science Applications”, Chapter 6 |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | Introduction to Python Programming for Business and Social Science Applications (2020) Frederick Kaefer, Paul Kaefer, Sage publications
Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".
Tutorial: “Big Data Analytics: Concepts, Technologies, and Applications” |
Suggested Readings/Materials |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation |
1
|
10
|
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments |
1
|
30
|
Presentation / Jury |
1
|
20
|
Project | ||
Seminar / Workshop | ||
Oral Exams | ||
Midterm |
2
|
40
|
Final Exam | ||
Total |
Weighting of Semester Activities on the Final Grade |
5
|
100
|
Weighting of End-of-Semester Activities on the Final Grade | ||
Total |
ECTS / WORKLOAD TABLE
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
Study Hours Out of Class |
16
|
2
|
32
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
0
|
||
Portfolio |
0
|
||
Homework / Assignments |
2
|
3
|
6
|
Presentation / Jury |
1
|
26
|
26
|
Project |
0
|
||
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
2
|
1
|
2
|
Final Exam |
0
|
||
Total |
130
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
1
|
2
|
3
|
4
|
5
|
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1 | To be able to analyze complex problems in the field of logistics and supply chains |
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2 | To be able to have good knowledge of sector related market leaders, professional organizations, and contemporary developments in the logistics sector and supply chains |
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3 | To be able to participate in the sector-related communication networks and improve professional competencies within the business sector |
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4 | To be able to use necessary software, information and communication technologies in the fields of logistics management and supply chain |
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5 | To be able to understand and utilize the coordination mechanisms and supply chain integration |
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6 | To be able to analyze the logistics and supply chain processes using the management science perspective and analytical approaches |
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7 | To be able to design, plan and model in order to contribute to decision making within the scope of logistics and supply chains |
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8 | To be able to interpret and evaluate the classical and contemporary theories in the field of logistics and supply chains |
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9 | To be able to conduct projects and participate in teamwork in the field of logistics and supply chains |
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10 | To be able to have an ethical perspective and social responsiveness when making and evaluating decisions. |
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11 | To be able to collect data in the area of logistics and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1). |
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12 | To be able to speak a second foreign at a medium level of fluency efficiently. |
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13 | To be able to relate the knowledge accumulated throughout human history to their field of expertise. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
NEWS |ALL NEWS
Teaching both at Izmir and Sweden
Making a difference with her successful work in the international arena, Assoc. Prof. Dr. Aysu Göçer, Lecturer at Department of Logistics Management,
Memorial Scholarship reached to 78 young people
The education scholarship given on behalf of the late Doğan Turhan, the philanthropist from Izmir, the founder of one of Turkey's largest
‘Green’ logistics going abroad
Assoc. Prof. Dr. Işık Özge Yumurtacı Hüseyinoğlu from Izmir University of Economics (IUE) Department of Logistics Management and her 3 students have
Double prize in logistics
The 'intelligent decision support system' named LTLZone, which was developed by a team of 3 people at Izmir University of Economics (IUE),
Double reward in logistics
The intelligent decision support system (IDSS) named ‘LTLZone’, which was developed by a team of 3 people at Izmir University of Economics
Department of Logistics Management 14th University-Industry Cooperation Event
Izmir University of Economics Logistics Management Department senior students continue to shed light on real logistics problems with the projects they developed