COURSE UNIT TITLE

: DATA ANALYSIS AND GRAPHICS USING R

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 3108 DATA ANALYSIS AND GRAPHICS USING R ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
Statistics(Evening)

Course Objective

This course aims to introduce students more advanced data analysis and graphic capabilities in R.

Learning Outcomes of the Course Unit

1   Importing and exporting data from/to external files
2   Creating and manipulating variables
3   Creating basic and advanced graphics
4   Editing features of graphs
5   Understanding the grammar of the advanced graphics in R
6   Running various statistical analyses in R
7   Doing a Monte Carlo simulation study using R

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Review of the R syntax
2 Data Import and export, data manipulations, aggregation and restructuring, control structures; conditional statements
3 Descriptive statistics and data summary
4 Probability and distributions
5 Random data, density and distribution functions, Confidence interval estimation
6 Hypothesis testing
7 base graphics system, basic graphs, graphics devices
8 base graphics functions
9 Advanced graphics; ggplot2
10 Advanced graphics; ggplot2
11 Monte Carlo simulation study
12 Advances in R

Recomended or Required Reading

Textbook(s):
1. Maindonald, J., & Braun, J. (2010). Data analysis and graphics using R: an example-based approach. Cambridge University Press.
2. Jones, O., Maillardet, R., & Robinson, A. (2014). Introduction to scientific programming and simulation using R. CRC Press.
Supplementary Book(s):
1. Kabacoff, R. (2015). R in Action: Data Analysis and Graphics with R. 2nd Ed. Manning Publications
2. Braun W.J., Murdoch D.J. (2016). A First Course in Statistical Programming with R. 2nd Ed.
References:
Materials: Lecture slides, R Manuals.

Planned Learning Activities and Teaching Methods

Lecture, homework assignments, examples and PC laboratory exercises.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 BNS FINAL COURSE GRADE (RESIT) MTE * 0.35 + ASG * 0.15 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.35 + ASG * 0.15 + 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 exams and homeworks.

Language of Instruction

English

Course Policies and Rules

Student responsibilities:
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 undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Faculty of Sciences 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 21 21
Preparation for final exam 1 30 30
Preparing assignments 2 7 14
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 125

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.155
LO.255
LO.355
LO.455
LO.53545
LO.63545
LO.73545

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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