FACULTY OF BUSINESS

Department of Business Administration

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 -
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-Introduction to coding for data analytics with Python (7 weeks): data types, searching/sorting, list processing for statistical calculations, web scraping for data 2-Statistical processing with R (5 weeks): Exploratory statistics in R. 3-Big data (2 weeks): technologies (Hadoop, MapReduce), competencies, real time data processing, possible value creation pipelines in big data

 



Course Category

Core Courses
X
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: Introduction to Python data processing patterns * How the Python interpreter works and communicates with us. Using RStudio as a Python editor and interface. * The syntax and grammar and vocabulary. Simple data types. * How Python’s memory works: variables. * Built-in functions and keywords, help system. * From repetitions to generalizations: defining functions. Goals: (1) Review of variables, values, and domains. (2) Understanding algorithms as repetitions and iterations that depend on the size of the problem. Case study: Number guessing game (search problem). Time complexity of guessing. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 1
2 * Python data variables: Lists and list processing. List indexing and comprehensions. * Modules and definition scope. * Looping. * Testing programs. Exception handling. Goals: Handle univariate data. Use looping patterns to handle such data. Case studies: * Read the list of daily numbers of Covid-19 cases (for all countries). Find maximum, average, and total of the list. * Read the list of countries from the Covid-19 data set. Find a unique list of countries. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 2
3 * Using RStudio to work with Python scripts. * Python scripts, paths, and file access. * Python input/output and shell redirection. * String processing. Parsing, splitting, joining, searching strings. Type conversion Goal: Constructing univariate data processing and reporting pipelines using mini programs. Case studies: (1) Select and output “bank”s in Borsa İstanbul company codes and names file. (2) Parse a file containing full names and grades of students into first, middle, last names and grades. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 3
4 * Using regular expressions for matching. * Iterators and file processing. * Dictionaries in Python. Using lists and dictionaries to represent structured data. * Web scraping utilities in Python and their uses. Goals: Processing unstructured data. Case studies: (1) Find all financial quantities in a file containing financial reports. (2) Parse a file containing country names and patient numbers of Covid cases in each line, report aggregated numbers. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 4
5 * Data sets in Python. * Using Pandas and Numpy modules and importing data. * Plotting a series or data frame. * Column statistics and helper functions: distinct, etc. * Indexing data frames. Goals: Use data aggregation code patterns Case studies: Aggregate a table containing country names and patient numbers of Covid cases in each row and convert to a table. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 5
6 Data set manipulation, subsetting and statistics. Handling data set formats. Goals: Group multivariate data, produce statistics for groups. Case study: Use Chicken farm data set that contains weights of chicken fed on different diets. Find and interpret the impact of diet using basic statistics. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 6
7 Data aggregation. Goal: Use analytic libraries to group and summarize data. Case study: Use NewYork city open dataset: city wide employee salary data. Find statistics for salaries of different groups. Kaefer, F., & Kaefer, P. (2020). Introduction to Python programming for business and social science applications. Thousand Oaks: SAGE Publications, Inc., Chapter 7&8
8 Python project presentations
9 MODULE 2: Statistical processing with R Introduction to R. Working with vectors and data frames. Loading data. Simple exploratory plots and statistics. Analytic reporting with R notebooks. Data indexing and selecting. Goal: Use analytic libraries to group and summarize data. Case study: Use NewYork city open dataset: city wide employee salary data. Find statistics for salaries of different groups using R. Grolemund, G. (2014). Hands-on programming with R. Sebastopol, CA: OReilly., ch1&2
10 Using R packages. Data visualization and descriptive statistics. Plotting with ggplot2 to visualize relationships between variables. Goal: use exploratory analytics to uncover relations between data in multivariate data sets. Case study: Use diamond prices data set. Explore and report effects of diamond weight, color, cut, etc. on the price. Grolemund, G. (2014). Hands-on programming with R. Sebastopol, CA: OReilly., ch3
11 Data grouping and aggregation. Goal: Group data and produce group summaries. Case study: Use NewYork city city wide employee salary data to find and report average salaries for employee types in the data. Grolemund, G. (2014). Hands-on programming with R. Sebastopol, CA: OReilly., ch4&5
12 Finding and visualizing clusters. Interactive visualizations. Goal: Dividing data into clusters. Case study: Use NewYork city city wide employee salary data to cluster employees in a reasonable way. Grolemund, G. (2014). Hands-on programming with R. Sebastopol, CA: OReilly., ch6
13 MODULE 3: 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. Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng Inf Technol 4: 3. doi: http://dx. doi. org/10.4172/2324, 9307(2).
14 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. Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng Inf Technol 4: 3. doi: http://dx. doi. org/10.4172/2324, 9307(2).
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 ISBN: 9781544377445

 

“Hands-On Programming with R”, 2014, Garrett Grolemund, O’Reilley. ISBN-13: 9781449359010

Web version: https://rstudio-education.github.io/hopr/index.html

Suggested Readings/Materials

“Programming with Python for Social Scientists” (2019),Phillip D. Brooke, Sage publications ISBN: 9781526431714

Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng Inf Technol 4: 3. doi: http://dx. doi. org/10.4172/2324, 9307(2).

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
3
90
Seminar / Workshop
Oral Exams
Midterm
Final Exam
Total

Weighting of Semester Activities on the Final Grade
4
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
1.5
24
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
3
20
60
Seminar / Workshop
0
Oral Exam
0
Midterms
0
Final Exam
0
    Total
148

 

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.

X
3

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

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.

9

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

X
10

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

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

 


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