Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
CSE0544		Computational Thinking and Data Science	3/0/0	DE	Türkçe	9

Course Goals	p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'} The course will focus on decomposition, pattern recognition / data representation, generalization / abstraction, and algorithms to give an experience of computational thinking practices. Also, this course will provide a general overview of advanced methods for data wrangling, statistical modeling and prediction of real life. Materials covered will integrate the four key facets of an investigation using data: big data and database management - data wrangling, cleaning, and sampling to get a suitable data set, accessing data quickly and reliably; interactive visualizations – generating hypotheses and building intuition; nonlinear statistical models; and deep learning – summarizing results through visualization, and interpretable summaries.
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Instructor(s)	Assoc. Prof. Fatma P. AKBULUT
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Course Schedules
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Assessments
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Program Outcomes PO-1 an ability to apply knowledge from undergraduate and graduate engineering and other disciplines to identify, formulate, and solve novel and complex electrical/computer engineering problems that require advanced knowledge within the field PO-2 knowledge of advanced topics within at least two subdisciplines of computer engineering PO-3 the ability to understand and integrate new knowledge within the field; PO-4 the ability to apply advanced technical knowledge in multiple contexts PO-5 a recognition of the need for, and an ability to engage in, life-long learning PO-6 the ability to plan and conduct an organized and systematic study on a significant topic within the field PO-7 an ability to convey technical material through formal written reports which satisfy accepted standards for writing style PO-8 the ability to analyze and use existing literature PO-9 the ability to demonstrate effective oral communication skills PO-10 the ability to stay abreast of advancements in the area of computer engineering

Learning Outcomes

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10