BUS 220 | Course Introduction and Application Information - Department of Mathematics

Course Name

Data Analytics for Business and Economics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
BUS 220	Fall/Spring	2	2	3	5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Prof. Dr. Mehmet Gençer
Course Lecturer(s)	Dr.Öğretim Üyesi SİNEM TOKCAER Öğr.Gör.Dr. ASLI DİYADİN LENGER Öğr.Gör.Dr. BURÇİN ÖZDAMAR Prof. Dr. Ayla OĞUŞ BİNATLI Prof. Dr. MEHMET GENÇER
Assistant(s)	Araş. Gör. Hasan GÜLOĞLU

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
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	MODULE 1: Big Data. Big data introduction, Big data competencies, real time data processing, essential data transformations in big data. Big data technologies: Hadoop	“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: Value creation pipelines in big data. Real time or offline value creation pipelines in big data.	“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
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	Midterm Week	-
9	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
10	MODULE 3: Introduction to Python data processing patterns * Python editor and interface. The syntax and grammar and vocabulary. * Python data types, type conversions	“Introduction to Python Programming for Business and Social Science Applications”, Chapter 1
11	* lists, dictionaries * Indexing * list comprehension	“Introduction to Python Programming for Business and Social Science Applications”, Chapter 2
12	* pandas library * pandas statistical functions	“Introduction to Python Programming for Business and Social Science Applications”, Chapter 3
13	* Data quality and preprocessing	“Introduction to Python Programming for Business and Social Science Applications”, Chapter 4
14	* Processing and manipulating data * Aggregating data and aggregate functions	“Introduction to Python Programming for Business and Social Science Applications”, Chapter 5
15	Python Recap
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

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	4	30
Portfolio
Homework / Assignments
Presentation / Jury	1	20
Project	1	20
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	30
Total

Weighting of Semester Activities on the Final Grade	6	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
Total

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	4	3	12
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	7	7
Project	1	25	25
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	10	10
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.	-	-	-	-	-
2	To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.	-	-	-	-	-
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.	-	-	-	-	-
4	To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.	-	-	-	-	-
5	To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.	-	-	-	-	-
6	To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.	-	-	-	-	-
7	To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.	-	-	-	-	-
8	To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.	-	-	-	-	-
9	To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.	-	-	-	-	-
10	To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.	-	-	-	-	-
11	To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.	-	-	-	-	-
12	To be able to speak a second foreign language 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.	-	-	-	-	-