FACULTY OF BUSINESS
Accounting and Auditing Program
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
|
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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;
|
| 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 acquire theoretical and practical knowledge and skills in the area. |
X | ||||
| 2 | To be able to approach problems with an analytical and holistic viewpoint. |
X | ||||
| 3 | To be able to gain knowledge about both national and international accounting and auditing standards. |
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| 4 | To be able to communicate the findings and solutions to the accounting and auditing problems in written and oral formats. |
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| 5 | To be able to critically evaluate the performance of accounting and other related management information systems, and organizations. |
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| 6 | To be able to develop innovative and creative approach to real-life business issues. |
X | ||||
| 7 | To be able to integrate knowledge gained in the main areas of accounting and auditing through a strategic perspective. |
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| 8 | To be able to act in accordance with the scientific and ethical values in studies related to accounting and auditing. |
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| 9 | To be able to demonstrate both leadership and team-work skills through being an efficient and effective team member. |
X | ||||
| 10 | To be able to have an ethical perspective and social responsiveness when evaluating and making business decisions. |
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| 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). |
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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 the human history to their field of expertise. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
NEWS |ALL NEWS
Transfer to the ‘Big Four’
4 young people who graduated from Faculty of Business, Izmir University of Economics (IUE) quickly climbed the ladder of success and transferred
Exemplary cooperation from IUE and PwC to support education
The agreement made with PwC firm will provide priority internship and job opportunities to the students of IUE Accounting and Auditing Program.
