BÜYÜK VERİ ANALİZİ DERS TANITIM VE UYGULAMA BİLGİLERİ

Kodu	Dersin Adı	Yarıyıl	Süresi(T+U)	Kredisi	AKTS Kredisi
YBS413	BÜYÜK VERİ ANALİZİ	7	3	3	8

DERS BİLGİLERİ
Dersin Öğretim Dili :	Türkçe
Dersin Düzeyi	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Dersin Türü	Zorunlu
Dersin Veriliş Şekli	-
Dersin Koordinatörü	Assist.Prof. DİDEM TETİK KÜÇÜKELÇİ
Dersi Veren Öğretim Üyesi/Öğretim Görevlisi	Dr.Öğr.Üyesi SÜREYYA İMRE BIYIKLI
Ders Ön Koşulu	Yok

AMAÇ VE İÇERİK
Amaç:	This course aims to provide the students with the basic knowledge that will enable them to handle the difficulties. The Big Data field has many disciplines by its very nature. As it becomes popular, many software and hardware tools and new algorithms emerge. A data scientist must follow these changing trends to deal with real-world challenges.
İçerik:	Contents of the course include the subjects such as basic platforms such as Hadoop, Spark, and tools like IBM System G for big data, large scale machine learning methods, which are the basis for artificial intelligence and cognitive networks, Intel and Power chips, analytical optimization methods for different hardware platforms such as GPU and FPGA, difficulties in Linked Big Data field including issues such as graph, graphical models, spatial-temporal analysis, cognitive analytics etc.

DERSİN ÖĞRENME ÇIKTILARI (Öğrenciler, bu dersi başarı ile tamamladıklarında aşağıda belirtilen bilgi, beceri ve/veya yetkinlikleri gösterirler.)
Conclude driven and undriven deduction from big data. {conclude}
Analyze big data by usuing R language. {analysis}
Create applications related to graphical representation of big data.{create}
Recognize the current application areas.{recognize}
Modify the knowledge obtained from big data analysis to solve the problems in daily life.{modify}

HAFTALIK DERS KONULARI VE ÖNGÖRÜLEN HAZIRLIK ÇALIŞMALARI
Hafta	Ön Hazırlık	Konular	Yöntem
1	-	Introduction to the course, Understanding Big Data	Lecture & Discussion & Practice
2	Literature Review, Assignment	Big Data Components, Big Data Analitics, R Applications- I	Lecture & Discussion & Practice
3	Literature Review, Assignment	Big Data Analitics(Methodology), R Applications - II	Lecture & Discussion & Practice
4	Literature Review, Assignment	Machine Learning for Big Data Analysis	Lecture & Discussion & Practice
5	Literature Review, Assignment	Graph Calculation- Big Data Analitics	Lecture & Discussion & Practice
6	Literature Review, Assignment	Big Data Tools: Hadoop, MapReduce, Spark, NoSQL, MongoDB, Pig, Impala	Lecture & Discussion & Practice
7	Literature Review, Assignment	Statistical Methods for Big Data Analysis	Lecture & Discussion & Practice
8	-	MID-TERM EXAM	-
9	Literature Review, Assignment	Naive Bayes Classifier for Big Data	Lecture & Discussion & Practice
10	Literature Review, Assignment	Simple and Multivariate Regression Analysis for Big Data	Lecture & Discussion & Practice
11	Literature Review, Assignment	K-Means Clustering for Big Data	Lecture & Discussion & Practice
12	Literature Review, Assignment	Decision Trees for Big Data	Lecture & Discussion & Practice
13	Literature Review, Assignment	Logistic Regression for Big Data	Lecture & Discussion & Practice
14	Literature Review, Assignment	Project Presentation	Lecture & Discussion & Practice
15	Literature Review, Assignment	Project Presentation	Lecture & Discussion & Practice
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

KAYNAKLAR
Gursakal, N. (2014). Buyuk Veri. Dora Publications.
Mayer-Schonberger, V., & Cukier, K. (2013). Buyuk Veri: Yasama.  Calisma ve Dusunme Seklimizi Donusturecek Bir Devrim, Paloma Publications, Istanbul.

ÖLÇME VE DEĞERLENDİRME
Yarıyıl İçi Yapılan Çalışmaların Ölçme ve Değerlendirmesi	Etkinlik Sayısı	Katkı Yüzdesi	Açıklama

(0) Etkisiz	(1) En Düşük	(2) Düşük	(3) Orta	(4) İyi	(5) Çok İyi
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Define concepts such as management, manager and leader.					4
2	Analyze the accuracy, reliability and validity of the new information obtained from the data.						5

KNOWLEDGE
Factual
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Report the obtained data.						5
2	Prepare software and projects related with the field.						5

SKILLS
Cognitive
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Use the appropriate resources for data analysis related with the field.						5
2	Analyze the work processes.						5

SKILLS
Practical
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Manage projects as part of a team.						5
2	Apply the material, techniques and analyzes in relation with the subject for project and work flows.						5

OCCUPATIONAL
Autonomy & Responsibility
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Fulfill responsibility with a focus on result in individual and team studies.						5

OCCUPATIONAL
Learning to Learn
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Recognizes what he/she knows about his/her field or not.						5
2	Act the theoretical knowledge in real life with learning to learn approach.						5
3	Apply different methods and techniques with an innovative approach in his/her research.						5

OCCUPATIONAL
Communication & Social
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Apply the results obtained in accordance with voluntarism and social responsibility projects.						5
2	Establish a healthy contact with colleagues					4
3	Share the analyzes and obtained results with colleagues.				3
4	Cooperate with colleagues at international level with the help of foreign language competency.				3

OCCUPATIONAL
Occupational and/or Vocational
	Program Yeterlilikleri/Çıktıları	Katkı Düzeyi
		0	1	2	3	4	5
1	Behave in accordance with ethical values regarding the collection, analysis and reporting of data.						5
2	Participate the design of work processes and systems with full quality.						5
3	Cooperate with other employees for the continuation of sustainability in the profession.					4

DERSİN İŞ YÜKÜ VE AKTS KREDİSİ
Öğrenme-Öğretme Etkinlikleri İş Yükü
Öğrenme-Öğretme Etkinlikleri	Etkinlik(hafta sayısı)	Süresi(saat sayısı)	Toplam İş Yükü
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	13	3	39
Land Surveying	0	0	0
Group Work	4	5	20
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	4	4	16
Project Work	1	24	24
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	1	4	4
Final Exam	1	1	1
Preparation for the Final Exam	7	4	28
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	7	4	28
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	53	0	203