Data Science and Analytics
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General Introduction Information of the Course

Course Code:

TURK101

Course Title:

Turkish Language l

Course Semester:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
2	0	2	2

Language of instruction:

Course Prerequisites:

Does the Course Require Work Experience?:

Type of course:

Necessary

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator :

Öğr.Gör. Egemen SÖNMEZ

Course Lecturer(s):

Öğretim Elemanı
Öğr.Gör. Egemen SÖNMEZ
Dr. Süleyman YİĞİT

Course Assistants:

Course Objectives and Content

Course Objectives:	To be able to get proficiency Turkish literature, language and culture
Course Content:	Language, Culture, Art, Literature, History

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Interpret the relation between Turkish Language and Turkish Culture.
2) Explain lifestyles and cultures of Turkish communities that live throughout the World.
3) Recognize publishing activities and cosmopolitan pattern in Istanbul in the 19th and 20th centuries and interpret these centuries in line with publishing theme.
4) Critique the periods before the Alphabet Revolution conducted on the 1st of Novermber, 1928 and the period after the Revolution.
5) Identify the common mispellings and use the language correctly.
6) Recognize and explain official writing types and prepare official writing types.
7) Recognize and explain scientific writing types and prepare scientific writing types.
2 - Skills
Cognitive - Practical
1) Extrapolate which languages commonly used words derive from.
3 - Competences
Communication and Social Competence
1) Evaluate Turkish evolution in time and compare and contrast changes in Turkish that has undergone till now.
2) Compare and contrast the relation between Turkish and World languages.
3) Prepare a public speaking text and make a speech by taking stress and intonation of the language into consideration.
4) Make a speech by applying the important issues that need to be taken into consideration.
Learning Competence
1) Identify the words that are transfered from Turkish to World languages and vice versa and infer the origins of the words.
2) Identify the current problems of Turkish and find solutions for these problems.
Field Specific Competence
1) Review literature to construct scientific texts.
Competence to Work Independently and Take Responsibility

Course Weekly Plan

Week	Subject	Related Preparation
1)	Genres of Writing	Anadolu Üniversitesi – Türk Dili II (e-kitap) Atatürk Üniversitesi – Türk Dili I (e-kitap) İstanbul Üniversitesi – Türk Dili II (e-kitap)
2)	Official Writing: Petition, Report, Curriculum Vitae, E-mail	Anadolu Üniversitesi – Türk Dili II (e-kitap) Atatürk Üniversitesi – Türk Dili I (e-kitap) İstanbul Üniversitesi – Türk Dili II (e-kitap)
3)	Language and Culture	Mehmet Kaplan – Kültür ve Dil Nihad Sâmi Banarlı – Türkçenin Sırları
4)	The place of Turkish language among world languages and historical background	Ahmet Bican Ercilasun – Türk Dili Tarihi Mustafa Altun – Alfabe Değişiminin Tarihsel Gelişimi Üzerine Bir Değerlendirme
5)	Interactions among world languages and interaction of Turkish language with world languages	Johann Strauss – Osmanlıda Kimler Neleri Okurdu? (Makale) Günay Karaağaç – Türkçenin Dünya Dillerine Etkisi
6)	Turkish in Turkey Region	Geoffey Lewis – Trajik Başarı / Türk Dil Reformu Ömer Seyfeddin – Yeni Lisan Zeynep Korkmaz – Cumhuriyet Devrinde Türk Dilinin Kültürümüzdeki Yeri Zeynep Korkmaz – Atatürk’ün Düşünce Sisteminde Türk Dilinin Yeri
7)	Vocabulary of Turkish	Anadolu Üniversitesi-Türk Dili I (e-kitap), İstanbul Üniversitesi Türk Dili I (e-kitap), Doğan Aksan-Türkçenin Söz Varlığı, Ali Özgün Öztürk-Dil İnkılabının Türkçenin Söz Varlığına Etkileri
8)	Midterm Exam
9)	Current problems of Turkish and suggestions for these problems	Yusuf Akçay – Peyami Safa’ya Göre Türk Dili (Türkçenin Sorunları / Çözüm Önerileri) Zeynep Korkmaz – Türk Dilinin Bugünkü Sorunları
10)	Culture of Reading	İbrahim Tüzer & Muhammed Hüküm - Edebiyat Sosyolojisi
11)	Writing Guidelines	TDK Yazım Kılavuzu www.sozluk.gov.tr www.tdk.gov.tr
12)	Writing Guidelines (Application)	TDK Yazım Kılavuzu www.sozluk.gov.tr www.tdk.gov.tr
13)	Scientific Writing II : Concepts	Anadolu Üniversitesi – Türk Dili II (e-kitap) Atatürk Üniversitesi – Türk Dili I (e-kitap) İstanbul Üniversitesi – Türk Dili II (e-kitap)
14)	Scientific Writing III : Examples	Anadolu Üniversitesi – Türk Dili II (e-kitap) Atatürk Üniversitesi – Türk Dili I (e-kitap) İstanbul Üniversitesi – Türk Dili II (e-kitap)
15)	Scientific Writing : Features	Anadolu Üniversitesi – Türk Dili II (e-kitap) Atatürk Üniversitesi – Türk Dili I (e-kitap) İstanbul Üniversitesi – Türk Dili II (e-kitap)
16)	Final Exam

Sources

Course Notes / Textbooks:	Anadolu Üniversitesi – Türk Dili I Atatürk Üniversitesi – Türk Dili I İstanbul Üniversitesi – Türk Dili I
References:	Mehmet Kaplan - Kültür ve Dil Nihâd Sami Banarlı - Türkçenin Sırları

Relationship Between Course and Program Learning Outcomes

Course Learning Outcomes	1	3	6	8	10	13	14	2	4	11	12	5	9	7	15
Program Outcomes
1) Ability to effectively apply acquired knowledge in data science, artificial intelligence, machine learning, statistics, and computer science to analysis and decision-making processes. Ability to identify data analysis needs in various disciplines, obtain appropriate datasets, and transform compiled and processed data into meaningful information. Ability to design necessary software and algorithms for data analysis and modeling processes. Ability to develop projects aimed at solving data science problems and propose alternative solution methods.
2) Ability to perform data collection, cleaning, preprocessing, and analysis processes. Ability to apply and interpret statistical analysis methods and machine learning algorithms. Ability to design and implement big data technologies and database management processes. Ability to perform data analysis using programming languages (such as Python, R) and develop the necessary software.
3) Ability to follow current developments in data science using both Turkish and English, and effectively convey professional knowledge both in written and verbal formats. Ability to act in accordance with ethical principles in professional practices and demonstrate sensitivity to data privacy issues.
4) Ability to develop projects in the field of data science; take an active role as a team member or project leader and develop innovative approaches. Ability to critically evaluate acquired knowledge and skills by integrating them with different disciplines and generate new learning opportunities.
5) Ability to participate effectively in teamwork within data science projects, assume individual responsibility, and take on project leadership when necessary.

Relationship Between Course and Learning Outcome

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

	Program Outcomes	Level of Contribution
1)	Ability to effectively apply acquired knowledge in data science, artificial intelligence, machine learning, statistics, and computer science to analysis and decision-making processes. Ability to identify data analysis needs in various disciplines, obtain appropriate datasets, and transform compiled and processed data into meaningful information. Ability to design necessary software and algorithms for data analysis and modeling processes. Ability to develop projects aimed at solving data science problems and propose alternative solution methods.
2)	Ability to perform data collection, cleaning, preprocessing, and analysis processes. Ability to apply and interpret statistical analysis methods and machine learning algorithms. Ability to design and implement big data technologies and database management processes. Ability to perform data analysis using programming languages (such as Python, R) and develop the necessary software.
3)	Ability to follow current developments in data science using both Turkish and English, and effectively convey professional knowledge both in written and verbal formats. Ability to act in accordance with ethical principles in professional practices and demonstrate sensitivity to data privacy issues.
4)	Ability to develop projects in the field of data science; take an active role as a team member or project leader and develop innovative approaches. Ability to critically evaluate acquired knowledge and skills by integrating them with different disciplines and generate new learning opportunities.
5)	Ability to participate effectively in teamwork within data science projects, assume individual responsibility, and take on project leadership when necessary.

Learning Activities and Teaching Methods

Lecture
Course

Assessment and Evaluation Methods and Criteria

Written Exam (open-ended questions, multiple-choice, true/false, matching, fill-in-the-blanks, ordering)

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 50
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Study Hours Out of Class	10	2	20
Midterms	1	1	1
Final	1	1	1
Total Workload			50