BIG DATA ANALYSIS PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
YBS413	BIG DATA ANALYSIS	7	3	3	8

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. DİDEM TETİK KÜÇÜKELÇİ
Instructor(s) of the Course Unit	Assist.Prof. SÜREYYA İMRE BIYIKLI
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	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.
Contents of the Course Unit:	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.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
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}

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
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	-

SOURCE MATERIALS & RECOMMENDED READING
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.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		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
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Report the obtained data.						5
2	Prepare software and projects related with the field.						5

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		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
	Programme Learning Outcomes	Level of Contribution
		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
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Fulfill responsibility with a focus on result in individual and team studies.						5

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		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
	Programme Learning Outcomes	Level of Contribution
		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
	Programme Learning Outcomes	Level of Contribution
		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

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
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