Big Data Analytics (English) (Non-Thesis)
Master	Length of the Programme: 1.5	Number of Credits: 90	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF: Level 7

Ders Genel Tanıtım Bilgileri

School/Faculty/Institute

Gradutate School of Science and Engineering

Course Code

BDA 591

Course Title in English

Project

Course Title in Turkish

Project

Language of Instruction

Type of Course

Project

Level of Course

Introductory

Semester

Spring,Fall,Summer School

Contact Hours per Week

Lecture: 3

Recitation:

Lab:

Other:

Estimated Student Workload

308 hours per semester

Number of Credits

10 ECTS

Grading Mode

Standard Letter Grade

Pre-requisites

BDA 589 - Project Proposal

Expected Prior Knowledge

BDA 589 Proje Önerisi

Co-requisites

None

Registration Restrictions

Only Graduate Students

Overall Educational Objective

To learn how to conduct a project individually.

Course Description

The aim of the course is to learn the basics of how to conduct a research-based project

Course Description in Turkish

Bu dersin amacı, araştırma-temelli bir projenin nasıl yapılacağını öğretmesidir.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) Provide a through out literature survey
2) Develop a project framework and methodology
3) Analyze the obtained data and provide a sufficient analysis
4) Evaluate the findings in adequate details.

Program Learning Outcomes/Course Learning Outcomes	1	2	3	4
1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning.
2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning.
3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations.
4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results.
5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning.
6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities.
7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses.
8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility.
9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2.
10) Understanding of social and environmental aspects of machine learning applications.

Relation to Program Outcomes and Competences

N None	S Supportive	H Highly Related

	Program Outcomes and Competences	Level	Assessed by
1)	Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning.	H	Project
2)	Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning.	H	Project
3)	Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations.	H	Project
4)	Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results.	H	Project
5)	Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning.	H	Project
6)	Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities.	N	Project
7)	Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses.	S	Project
8)	Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility.	N	Project
9)	Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2.	H	Project
10)	Understanding of social and environmental aspects of machine learning applications.	N	Project

Prepared by and Date	,
Course Coordinator	ÖZGÜR ÖZLÜK
Semester	Spring,Fall,Summer School
Name of Instructor	Prof. Dr. ÖZGÜR ÖZLÜK

Course Contents

Week	Subject
1)	Introduction
2)	Introduction
3)	Fundamental Concepts in Project Format I
4)	Fundamental Concepts in Project Format II
5)	Literature Search and Survey I
6)	Literature Search and Survey II
7)	Designing the Research Framework I
8)	Designing the Research Framework II
9)	Applying Methodology I
10)	Applying Methodology II
11)	Outputs and Findings I
12)	Outputs and Findings II
13)	Potential Conclusions and Alternative Approaches I
14)	Potential Conclusions and Alternative Approaches II
15)	Final Examination Period
16)	Final Examination Period

Required/Recommended Readings

None

Teaching Methods

Flipped classroom. Students will work for projects.

Homework and Projects

Project

Laboratory Work

None

Computer Use

Students will apply the methods they learned

Other Activities

None

Assessment Methods

Assessment Tools	Count	Weight
Paper Submission	1	% 100
TOTAL		% 100

Course Administration

02123953600

ECTS Student Workload Estimation

Activity	No/Weeks	Hours			Calculation
	No/Weeks per Semester	Preparing for the Activity	Spent in the Activity Itself	Completing the Activity Requirements
Course Hours	14	1	5	1	98
Paper Submission	14	14	1		210
Total Workload					308
Total Workload/25					12.3
ECTS					10