STATISTICS I PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
EKF223	STATISTICS I	3	3	3	6

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	, TYY: + , EQF-LLL: , QF-EHEA:
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit
Instructor(s) of the Course Unit	Assist.Prof. DİDEM TETİK KÜÇÜKELÇİ
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course aims to provide the student with the ability to interpret basic statistical techniques and results based on mathematical foundations.
Contents of the Course Unit:	Contents of the course include the subjects such as definition and basic concepts, measures of central tendency and variability, probability and probability distributions.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Describe the basic statistical terms. {Knowledge / Bloom's 1}
Defines the dependent and independent variable in regression analysis {Knowledge / Bloom's 1}
Creates the Frequency Distribution Table for a given series.{Create / Bloom's 6}
Calculates the central tendency measures for a given series. {Understand / Bloom's 2}
Interprets and calculates the central distribution measures for a given series. {Understand / Bloom's 2} & {Evaluate / Bloom's 5}
Calculates the probability values by using z-table {Understand / Bloom's 2}

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Reading	Principles of Counting, Factorial, Permutation	Lecture & Question Answer & Problem-Solving Method
2	Reading	Combination, Probability	Lecture & Question Answer & Problem-Solving Method
3	Reading	Conditional Probability, Bayes Theorem	Lecture & Question Answer & Problem-Solving Method
4	Reading	Fundamental Definitions of Statistics, Large Number Law	Lecture & Question Answer & Problem-Solving Method
5	Reading	Tables, Frequency Distribution Tables of a given series, Cumulative, Contingency Tables	Lecture & Question Answer & Problem-Solving Method
6	Reading	Central Tendency Measures, Arithmetic Mean	Lecture & Question Answer & Problem-Solving Method
7	Reading	Central Tendency Measures, Mode, Median and Central Tendency Measures comparison	Lecture & Question Answer & Problem-Solving Method
8	-	MID-TERM EXAM	-
9	Reading	Central Distribution Measures	Lecture & Question Answer & Problem-Solving Method
10	Reading	Random Variables (Discrete, Continuous), Expected Value, Variance	Lecture & Question Answer & Problem-Solving Method
11	Reading	Some Discrete Probability Distribution-1 (Bernoulli, Binom, Poisson, Pascal, Uniform Distribution)	Lecture & Question Answer & Problem-Solving Method
12	Reading	Some Discrete Probability Distribution-2 (Bernoulli, Binom, Poisson, Pascal, Uniform Distribution)	Lecture & Question Answer & Problem-Solving Method
13	Reading	Normal Distribution	Lecture & Question Answer & Problem-Solving Method
14	Reading	Z-Distribution	Lecture & Question Answer & Problem-Solving Method
15	Reading	Comparison of Normal Distribution and Standard Normal Distribution	Lecture & Question Answer & Problem-Solving Method
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Murray R. Spiegel, Larry J. Stephens. (2013). Statistic. Nobel Academic Publications
Cil, B (2008). Istatistik. 8th Edition. Detay Publications
Fikri Akdeniz, Olasılık ve İstatistik

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	List the history of communication, mass media, communication theories and leading theorists.
2	List the historical, social and cultural types of communication and explain the related concepts.
3	Define the important points of the history and theories of communication through daily life practices and social life.

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Compare the traditional media and new media economic policies.
2	Interpret digital culture with constantly updated and self-renewing topics.
3	Interpret the technical, socio-political and legal aspects of cyber security issues in the field of new media.

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Define the basic concepts of communication history, communication theories, traditional and new media channels.

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Prepare web pages with CSS codes.
2	Produce creative content in new media environments, create an image and sound and practical studies about programming.
3	Analyze the sub-texts and their semantics of the studies presented to the society by mass media.
4	Use qualitative and quantitative elements to construct arguments on studies in the field of communication.

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Manage social media accounts of brands, corporate firms and public institutions thanks to its advanced knowledge in content production and user experience.

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Review local and foreign studies in the field of New Media. Creates innovative works in his/her field.
2	Criticize the effects of social media activities on socio-political field.

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Plan scientific studies in any area that can be encountered in different disciplines and transfer them to people from different disciplines.
2	Determine how much of the content produced by the media is right and wrong.

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Follows the developments that have begun to guide the present and the future such as "Software". Produce various software products for different sectors.
2	Design using new architectures of big data processing systems.
3	Determine the logic of operation of artificial intelligence algorithms and determines the possible effects on media and indirectly society.

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	2	26
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	10	2	20
Assignment (Homework)	7	2	14
Project Work	0	0	0
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	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	4	8	32
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	2	8	16
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	52	0	152