COURSE UNIT TITLE

: R FOR STATISTICAL COMPUTING AND PROGRAMMING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5003 R FOR STATISTICAL COMPUTING AND PROGRAMMING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR ENGIN YILDIZTEPE

Offered to

Data Science
Data Science - (Non-Thesis-Evening)

Course Objective

The objective of this course is to teach the fundamental and advance topics of the R statistical programming language and the necessary packages and functions for data science.

Learning Outcomes of the Course Unit

1   Implement R functions for statistical methods.
2   Perform statistical analysis using R functions.
3   Use R packages for data science applications.
4   Use R functions for data visualization and graphics.
5   Produce reproducible reports using markdown.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Installing R, R environment, R Editors, R Project, CRAN
2 R syntax: variables, operators, expressions, assignment, statements. Data types, vector. Conditional execution
3 Data types, matrix, data.frame, lists.
4 Data types, matrix, data.frame, lists. Data import / export
5 Math and statistical functions. String manipulation functions
6 Control structures, conditional statements, loops, apply family
7 Base graphics functions, low level functions
8 Base graphics functions, high level functions
9 Advanced graphics; ggplot2 package
10 Functions, Local variables, scope of variables.
11 Functions, Local variables, scope of variables.
12 R packages for data science applications
13 Reproducible reports, rmarkdown package
14 Student presentations

Recomended or Required Reading

Textbook(s): :
1. Braun, W. J., & Murdoch, D. J. (2016). A first course in statistical programming with R. Cambridge University Press.
2. Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press.
Supplementary Book(s):
1. Rizzo, M. L. (2007). Statistical computing with R. Chapman and Hall/CRC.
2. Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly.
Materials: Lecture Slides, R Manuals

Planned Learning Activities and Teaching Methods

The course consists of lecture and projects.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homeworks/reports/presentation/exam.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr/

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
phone: +90 232 301 86 04

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 40 40
Preparation for final exam 1 45 45
Preparing assignments 1 25 25
Preparing presentations 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1555
LO.2555545
LO.3555545
LO.45545
LO.555555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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