• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Gain knowledge of statistical analysis and machine learning and how to apply them in big data analytics.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Formulate and solve advanced engineering problems
   3. Acquire scientific knowledge
   4. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
   5. Develop awareness for new professional applications and ability to interpret them

   Method of assessment

   1. Homework assignment
   2. Seminar/presentation
2. Obtain experience in application of the frameworks and methods for manipulating, storing, and analyzing big data.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Formulate and solve advanced engineering problems
   3. Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
   4. Find out new methods to improve his/her knowledge.
   5. Write progress reports clearly on the basis of published documents, thesis, etc

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Seminar/presentation
3. Apply big data analytics in IT, healthcare and social applications.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Use advanced knowledge of mathematics, science, and engineering
   3. Acquire scientific knowledge
   4. Find out new methods to improve his/her knowledge.
   5. Write progress reports clearly on the basis of published documents, thesis, etc
   6. Develop awareness for new professional applications and ability to interpret them

   Method of assessment

   1. Homework assignment
   2. Seminar/presentation
   3. Term paper

Contents
Week 1:	Statistical analysis with R and R Studio.
Week 2:	Visualization with R.
Week 3:	Data cleanup and standardization.
Week 4:	MapReduce framework.
Week 5:	An introduction to Hadoop.
Week 6:	Batch processing.
Week 7:	HBase a low latency NoSQL.
Week 8:	Near real time analytics and search with Impala and Flume. (Midterm exam)
Week 9:	Stream computing.
Week 10:	Predictive analytics.
Week 11:	Visualizing large data sets.
Week 12:	Case studies - big data in IT.
Week 13:	Case studies - big data in social and health sciences.
Week 14:	Final projects and class presentations.
Week 15*:	Final projects and class presentations.
Week 16*:	Final projects and class presentations.
Textbooks and materials:	Ders notları.
Recommended readings:	Ders notları ve makaleler.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	20
Other in-term studies:		0
Project:	10	40
Homework:	4-10	20
Quiz:		0
Final exam:	16	20
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	4	6
Term project:	6	8
Term project presentation:	6	1
Quiz:	0	0
Own study for mid-term exam:	8	1
Mid-term:	2	1
Personal studies for final exam:	8	1
Final exam:	3	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)