BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
BDA5015	Exploratory Data Analytics and Visualization	Fall Spring	3	0	3	8

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:
Course Coordinator :	Assist. Prof. ECE GELAL SOYAK
Recommended Optional Program Components:	Students should have prior knowledge of basic statistical concepts and programming experience in Python or R. Familiarity with data visualization libraries (e.g., Matplotlib, Seaborn, Plotly) is recommended.
Course Objectives:	This course provides an in-depth exploration of Exploratory Data Analytics and Visualization, emphasizing co-creation and co-production methodologies in data-driven decision-making. Students analyze real-world datasets and develop skills in data preprocessing, exploratory data analysis, and advanced visualization methods. Through case studies, interactive discussions, and practical applications, students collaboratively explore techniques to derive insights and effectively communicate data-driven results.

Learning Outcomes

The students who have succeeded in this course;
By the end of the course, students will be able to:
1. Understand the fundamentals of exploratory data analytics and visualization.
2. Perform data preprocessing and transformation techniques.
3. Conduct exploratory data analysis to uncover patterns and trends.
4. Implement effective data visualization techniques for different types of data.
5. Utilize interactive visualization tools such as Tableau and Power BI.
6. Apply statistical and machine learning techniques to enhance visual storytelling.
7. Work with geospatial data visualization.
8. Evaluate ethical considerations in data analytics and visualization.
9. Develop interactive dashboards for real-time data exploration.
10. Engage in co-creation and co-production practices to collaboratively generate insights and solutions.

Course Content

This course equips students with essential skills in exploratory data analytics and visualization, fostering analytical thinking, problem-solving abilities, and proficiency in data visualization tools. The inclusion of co-creation and co-production enhances collaborative learning and industry-relevant data-driven decision-making approaches.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Bridging Disicplines: Data & AI’s Role in Social Science and Its Societal Implications
2)	Introduction to Exploratory Data Analytics and Visualization & Societal Implications
3)	Data Preprocessing and Cleaning Techniques
4)	Exploratory Data Analysis
5)	Fundamentals of Statistical Visualization
6)	Advanced Graphing with Python and R
7)	Visualizing Time-Series and Streaming Data
8)	Data Storytelling and Narrative Visualization with Co-Production Methodologies
9)	Midterm
10)	Geospatial Data Visualization
11)	Project Updates
12)	Project Updates
13)	Project updates
14)	Future Trends in Data Visualization & Co-Creation Practices [Final Project Report Submission]

Sources

Course Notes / Textbooks:

References:

• Tufte, E. (2001) The Visual Display of Quantitative Information.
• Hadley Wickham (2009). ggplot2: Elegant Graphics for Data Analysis.
• Aggarwal (2015) Data Mining: The Textbook.
• Cole Nussbaumer Knaflic (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals
• Leskovec, Rajaraman, Ullman (2020) Mining of Massive Datasets.
• Russell & Norvig (2021) Artificial Intelligence: A Modern Approach.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Jury	1	% 60
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 100
PERCENTAGE OF FINAL WORK		%
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	4	56
Presentations / Seminar	0	15	0
Homework Assignments	3	10	30
Midterms	1	30	30
Final	1	40	40
Total Workload			198

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.