CURRENT TOPICS IN COMPUTER SCIENCES

CSE395

Course Code: CSE395
Semester: -
Theory+Practice: 3 + 1
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Learn the basic concepts and methods of statistical analysis.
Interpret statistical data sets by applying numerical and graphical methods used in summarizing.
Determine the appropriate analysis methods.
Apply the appropriate analysis method to the relevant hypothesis and can making inferences
Will be able to use at least one programming language or package program and reporting analysis results.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

MAT250 Probability and Statistics

Course Description

To learn statistical analysis concepts and methods for compiling, analyzing and interpreting data, to determine appropriate analysis methods, to gain the ability to use at least one programming language or package program and to report the results.

Course Contents

WEEK	TOPICS
1^st Week	Course introduction.What is Statistics? Basic definitions and concepts.
2^nd Week	Introduction to R and R Studio. Data structures and data input in R.
3^rd Week	Descriptive Statistics: Measures of central tendency, measures of distribution, frequency tables.
4^th Week	Data visualization.
5^th Week	Some discrete and continuous distributions.
6^th Week	Deciding the appropriate analysis method. The goodness of fit test to normal distribution.
7^th Week	Parametric tests (one and two samples).
8^th Week	Mid-term exam.
9^th Week	Analysis of variance.
10^th Week	Non-Parametric tests (one and two samples).
11^th Week	Categorical Data Analysis (Chi-square tests).
12^th Week	Correlation analysis.
13^th Week	Linear regression analysis.
14^th Week	Logistic regression.
15^th Week	General Review

Recommended or Required Reading

1. Cotton, R. (2013). Learning R. O'Reilly Media, Inc. (Çeviri: Herkes için İstatistiksel Programlama ve Analiz: R, Pegem Akademi, 2020).
2. Fischetti, T. (2015). Data Analysis with R. Packt Publishing.
3. Demir, İ (Editör). (2017). R ile Uygulamalı İstatistik. Papatya Bilim.
4. Peter Daalgard (2008). Introductory Statistics with R, Springer.

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Problem Solving
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	3	15
Quiz	4	20
Total(%)		65
Contribution of In-term Studies to Overall Grade(%)		65
Contribution of Final Examination to Overall Grade(%)		35
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work
Preparation for Final exam
Course hours
Preparation for Midterm exam
Laboratory (including preparation)
Final exam
Homework
Total Work Load			0
Total Work Load / 30 (h)			0
ECTS Credit of the Course			5

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5
K1	X		X
K2			X
K3
K4
K5	X	X	X	X	X
K6
K7			X	X	X
K8	X			X
K9					X
K10
K11