Course Details

BUSINESS ANALYTICS & BIG DATA FOR BUSINESS AND ECONOMICS

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
4	DSBE518	BUSINESS ANALYTICS & BIG DATA FOR BUSINESS AND ECONOMICS	3+0+0	3	7,5

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Providing an understanding of how managers use business analytics. Solving business problems and supporting managerial decision making Interpreting statistical results. Familiarizing students with the processes needed to develop, report and analyze big business data.
Course Content	The course is an introduction to concepts of machine learning and big data analytics, and it is designed as a combination of statistical data analysis, data mining, machine learning and artificial intelligence. Students work on projects involving big data sets that are especially related to business in order to solve real-world problems. Completing the course will help students apply the concepts of big data analytics and statistical applications to varied aspects of managerial decision making, and understand how big data technologies and data mining techniques help data driven decisions in business.
Course Methods and Techniques	Traditional in class lectures and real life applications and cases will be utilized.
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Umut Türk
Name of Lecturers	Associate Prof.Dr. Umut Türk
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	-
	Course materials will be provided throughout the semester

Course Category

Mathematics and Basic Sciences	%25
Social Sciences	%15
Field	%60

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Sunum için Hazırlık	2	30	60
Sunum	2	10	20
Araştırma	4	10	40
Kişisel Çalışma	10	4	40
Yüz Yüze Ders	14	3	42
Derse Devam	14	1	14
Final Sınavı	1	2	2
Total Work Load			Number of ECTS Credits 7,5 218

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Use the most common algorithms, in order to make sense of large amounts of data, which are applicable to most business and management problems.
2	Analyze the problems encountered in practice with appropriate methods.
3	Interpret the results obtained with statistical and data mining techniques correctly in order to make better decisions.
4	Use appropriate statistical and machine learning software.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Course introduction, introduction to business analytics	Review syllabus and course objectives	Syllabus
2	Data types and data sources	Research business intelligence concepts	Lecture notes
3	Basic data analytics tools and processes	Complete setup of Excel/Power BI	Software guideline
4	Descriptive analytics: summary stats and visualization	Create basic graphs on a dataset	Application file
5	Descriptive models and pattern recognition	Study clustering algorithms	Reading material
6	Predictive analytics and regression models	Run basic regression analysis	Textbook chapter
7	Classification algorithms	Explore examples of decision trees and k-NN	Academic article
8	Midterm Exam	Review previous topics	-
9	Big data concepts and technologies	Study Hadoop and Spark frameworks	Presentation slides
10	Web scraping and data mining	Practice scraping in Python/R	Code examples
11	Data privacy and security in big data environments	Review GDPR and local regulations	Policy document
12	Real-time data analytics	Explore streaming data application	Application scenario
13	Student project presentations	Finalize and rehearse your presentation	Student presentations
14	General review and final preparation	Solve sample cases for final	Course summary

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75111&lang=en