TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Professor Mehmet Güray Ünsal
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know a wide range of data analytics techniques (descriptive analytics, inferential analytics, and predictive analytics). 2) Master different data analysis problems encountered in applications with heavy data use.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	Data Science
COURSE DEFINITION	Data Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us to make better decisions. This course aims to present you with a wide range of data analytic techniques (descriptive, inferential, predictive, and prescriptive analytics).
COURSE CONTENTS	WEEK TOPICS 1st Week Basic Definitions: Data, Data Analytics, Parametric and Non-parametric tests 2nd Week Testing Assumptions for Parametric Tests 3rd Week One Sample T Test and Wilcoxon Signed Rank Test 4th Week Independent Samples T Test and Mann-Whitney U Test 5th Week Paired Samples T Test and Wİlcoxon Signed Rank Test 6th Week One Way ANOVA and Kruskal Wallis? H Test 7th Week Repetition 8th Week Mid-term Examination 9th Week Two Ways ANOVA and Friedman?s S Test 10th Week Chi-Square Tests for Independence, Goodness of Fit Tests 11th Week Social Network Analysis 12th Week Word Cloud 13th Week Repetition 14th Week Project Presentations
RECOMENDED OR REQUIRED READING	"Univariate, Bivariate, and Multivariate Statistics Using R", Daniel J. Denis, Wiley, 2020. "İstatistikte R ile Programlama", Necmi Gürsakal, Dora, 2018.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Discussion,Problem Solving
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2,5 2,5 Preparation for Quiz Individual or group work 14 4 56 Preparation for Final exam 1 20 20 Course hours 14 3 42 Preparation for Midterm exam 1 20 20 Laboratory (including preparation) Final exam 1 2,5 2,5 Homework Project 1 6 6 Total Workload 149 Total Workload / 30 4,96 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No