DATA SCIENCE

TKM522

Course Code: TKM522
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree With Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Professor Mehmet Güray Ünsal

Learning Outcomes

At the end of this course, the students;

They will see the general use of R programming language, functions, conditions, loop structures in R, probability calculation in R, and probability distributions.
They will see the use of some functional libraries and packages in R, data visualization and statistical inference.
They will learn theoretically and practically some methods commonly used in Data Science.
They will learn Parametric and Non-Parametric Staitstical Tests and R applications.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Data Analytics

Course Description

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
1^st Week	Introduction to R Programming Language, Matrix, Factor, List and Data Frames
2^nd Week	Data Entry, Function, Loop, Condition Structures in R
3^rd Week	Probability calculation and Probability Distributions and Data Visualization in R
4^th Week	Descriptive Statistics, Regression and Correlation and R Applications
5^th Week	Time Series in R
6^th Week	Cluster Analysis, Discriminant Analysis and R Applications
7^th Week	Overview of Mid-Term
8^th Week	Mid-Term Exam
9^th Week	Parametric and Non-Parameter Statistical Tests and Applications in R - 1
10^th Week	Parametric and Non-Parameter Statistical Tests and Applications in R - 2
11^th Week	Parametric and Non-Parameter Statistical Tests and Applications in R - 3
12^th Week	Parametric and Non-Parameter Statistical Tests and Applications in R - 4
13^th Week	Presentations
14^th Week	Projects

Recommended 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 Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	3	3
Preparation for Quiz
Individual or group work	14	4	56
Preparation for Final exam	1	40	40
Course hours	14	3	42
Preparation for Midterm exam	1	40	40
Laboratory (including preparation)
Final exam	1	3	3
Homework	2	8	16
Project	1	60	60
Quiz	1	35	35
Total Work Load			295
Total Work Load / 30 (h)			9.83
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1	X
K2	X
K3	X	X
K4	X
K5		X
K6	X
K7	X	X
K8		X
K9	X
K10	X
K11	X
K12	X	X
K13	X