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. 4) They will learn Parametric and Non-Parametric Staitstical Tests and R applications.
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, Matrix, Factor, List and Data Frames
2nd Week
Data Entry, Function, Loop, Condition Structures in R
3rd Week
Probability calculation and Probability Distributions and Data Visualization in R
4th Week
Descriptive Statistics, Regression and Correlation and R Applications
5th Week
Time Series in R
6th Week
Cluster Analysis, Discriminant Analysis and R Applications
7th Week
Overview of Mid-Term
8th Week
Mid-Term Exam
9th Week
Parametric and Non-Parameter Statistical Tests and Applications in R - 1
10th Week
Parametric and Non-Parameter Statistical Tests and Applications in R - 2
11th Week
Parametric and Non-Parameter Statistical Tests and Applications in R - 3
12th Week
Parametric and Non-Parameter Statistical Tests and Applications in R - 4
13th Week
Presentations
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
20
Assignment
2
20
Project
1
20
Presentation of Article
1
20
Total(%)
80
Contribution of In-term Studies to Overall Grade(%)
80
Contribution of Final Examination to Overall Grade(%)
20
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
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 Workload
295
Total Workload / 30
9,83
ECTS Credits of the Course
10
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
