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) They will see the general use of R programming language, functions, conditions, loop structures in R, probability calculation in R, and probability distributions. 2) They will see the use of some functional libraries and packages in R, data visualization and statistical inference. 3) They will learn theoretically and practically some methods commonly used in Data Science.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	Data Analytics
COURSE DEFINITION	This course will emphasize practical techniques for working with large-scale date. Specific topics covered will include statistical modeling and machine learning, data pipelines, programming languages and real world topics and case studies. The use of R programming language will be used.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to R Programming Language, 2nd Week Matrix, Factor, List and Data Frames 3rd Week Data Entry in R, Function, Loop, Condition Structures in R 4th Week Probability and Probability Distributions in R 5th Week Data Visualization in R 6th Week Descriptive Statistics 7th Week Overview of Mid-Term 8th Week Mid-Term Exam 9th Week Regression and Correlation and R Applications 10th Week Time Series in R 11th Week Cluster Analysis and R Applications 12th Week Discriminant Analysis and R Applications 13th Week Overview of the term 14th Week Projects
RECOMENDED OR REQUIRED READING	"Univariate, Bivariate, and Multivariate Statistics Using R", Daniel J. Denis, Wiley, 2020. "İstatistikte R ile Programlama", Necmi Gürsakal, Dora, 2018. "Introduction to Data Science", Laura Igual, Santi Segui, Springer, 2017. "Uygulamalı Çok Değişkenli İstatistik Teknikleri", Ali Sait Albayrak, 2006.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Discussion,Case Study
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 2 Preparation for Quiz Individual or group work 14 3 42 Preparation for Final exam 1 25 25 Course hours 14 3 42 Preparation for Midterm exam 1 25 25 Laboratory (including preparation) Final exam 1 2 2 Homework 0 0 0 Project 1 12 12 Total Workload 150 Total Workload / 30 5 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No