COURSE UNIT TITLE

: R PROGRAMMING FOR DATA SCIENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5034 R PROGRAMMING FOR DATA SCIENCE 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

PROFESSOR DOCTOR EMEL KURUOĞLU KANDEMIR

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

This course aims to provide the students with an overview of the R programming and data management for science. It also aims to managing data frames, analyzing data, simulating data and structures and profiling R codes.

Learning Outcomes of the Course Unit

1   Understanding both theoretical and practical knowledge of R programming and data science.
2   Understanding of the basic codding techniques and details.
3   Understanding of the basic code making and application for mathematical functions, managing data frames and making control structures.
4   Understanding of advanced applied tools used in R programming.
5   Having ability to develop program and analyzing data in R.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, History and Overview of R and Data Science.
2 R programming of the basic codding techniques and details.
3 Getting Data In and Out of R and Subsetting R Objects and Vectorized Operations.
4 Managing Data Frames, Dates and Times in R.
5 Control Structures in R.
6 Loop Functions and Debugging.
7 Presentation of Project 1
8 Midterm exam
9 Profiling R codes.
10 Data Analysis and interpreting the results.
11 Data Analysis Case Study
12 Data Analysis Case Study
13 Simulating and interpreting the data.
14 Presentation of Project 2

Recomended or Required Reading

Textbook(s)/References/Materials:

Textbook(s): Roger D. Peng, R Programming for Data Science, Wiley, 2014-2015.

Supplementary Book(s): Norman Matloff, The Art of R Programming: A Tour of Statistical Software Design, 2011

Recent literature papers.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.30 + MTE * 0.30 + RST * 0.40


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

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Email: emel.kuruoglu@deu.edu.tr

Office Hours

Will be announced

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 20 20
Preparation for final exam 1 20 20
Preparing assignments 2 20 40
Preparing presentations 2 15 30
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 205

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

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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