FACULTY OF BUSINESS

Department of International Trade and Finance

BUS 220 | Course Introduction and Application Information

Course Name
Data Analytics for Business and Economics
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
BUS 220
Spring
2
2
3
5

Prerequisites
None
Course Language
English
Course Type
Required
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Application: Experiment / Laboratory / Workshop
Lecture / 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;
  • Explain the structure of a program in procedural language.
  • Debug Python programs by testing.
  • Employ Python programming patterns to solve a business data processing problem, which may involve skills such as data connection web scraping, and so on.
  • Describe the advantages and capabilities of, and best practices for R statistical programming platform and add on RStudio software for analytics tasks in a business or research team setting.
  • Produce data insights using R notebooks.
  • Identify common big data processing and analytics pipelines.
  • Describe technologies and supply approaches to implement big data processing and analytics pipelines.
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

 



Course Category

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”  

https://aisel.aisnet.org/cais/vol34/iss1/65/?utm_source=aisel.aisnet.org%2Fcais%2Fvol34%2Fiss1%2F65&utm_medium=PDF&utm_campaign=PDFCoverPages

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
1

To be able to identify and analyze problems in the field of trade and finance, and to develop solutions.

2 To be able to use the theoretical and practical knowledge gained in the field of International Trade and Finance.
3 To be able to analyze the developments in global markets by using critical thinking skills.
4 To be able to analyze and interpret data in the field of finance, commerce and economics by using information technologies effectively.
5 To be able to acquire knowledge about the legal regulations and practices in the field.
6 To be able to foresee and define the risks that could be encountered in the field of trade and finance and to take decisions to manage such risks.
7 To be able to acquire and use verbal and numerical skills necessary for the nature of international trade and finance program.
8 To be able to obtain, synthesize and report the information related to the fields of trade and finance.
9 To be able to contribute to the solution of problems as individual, team member or leader.
10

To be able to evaluate the issues related to the field with an ethical perspective and social sensitivity.

11 To be able to collect data in the areas of International Trade and Finance 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 human history to their field of expertise.

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

 


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