COURSE UNIT TITLE

: EXPLORATORY DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5065 EXPLORATORY DATA ANALYSIS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASISTANT PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
STATISTICS

Course Objective

The objective of this course is to cover modern techniques in exploratory data analysis with R applications, including graphical techniques and novel approaches.

Learning Outcomes of the Course Unit

1   Construct and interpret graphics
2   Understand the dual role of exploratory and confirmatory approaches to data analysis
3   Develop a strategy for data analysis
4   Interpret the results of quantitative analyses
5   Develop graphics for inclusion in papers and thesis
6   Using R for data analysis and graphics

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Review of basic statistics, brief introduction to R
2 Probability distributions
3 Graphical methods, Histogram, Scatter plot, Boxplots and batch comparison
4 Transforming data
5 Density estimation
6 Advance graphics, lattice
7 Advance graphics, ggplot2
8 Graphical devices
9 Clustering
10 Examining residuals, Residuals and the fit
11 Comparing location estimators: Trimmed means and medians. Choosing the robust estimator
12 Monte Carlo techniques
13 Bootstrap, Jackknife, cross-validation
14 Student presentations

Recomended or Required Reading

1. D. C. Hoaglin, F. Mosteller, J. W. Tukey, Understanding Robust And Exploratory Data Analysis, 2000.
2. Tukey J. W. Exploratory Data Analysis, Addison Wesley, 1977.
3. Chang, W., R Graphics Cookbook, 2012.
4. Kabacoff, R.I., R in Action, 2011.
5. Maindonald, J., Braun, W.J., Data Analysis and Graphics Using R, 2010.

Planned Learning Activities and Teaching Methods

Lecture, computer applications and homework.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PAR PARTICIPATION
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE ASG * 0.40 +PAR * 0.10 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.40 +PAR * 0.10 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Exam, class participation, homework/presentation

Language of Instruction

English

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

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics B261/1
e-mail: engin.yildiztepe@deu.edu.tr
phone: 90 232 301 86 04

Office Hours

Tuesday 16:00 - 17:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparing presentations 1 30 30
Preparation for final exam 1 45 45
Preparing assignments 1 40 40
Preparations before/after weekly lectures 14 1 14
Final 1 2 2
TOTAL WORKLOAD (hours) 173

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15543322
LO.25553322
LO.35553322
LO.45553322
LO.55543322
LO.65543322

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

HOME / ABOUT US / DEGREE PROGRAMMES / GENERAL INFORMATION FOR STUDENTS / INTERNATIONAL RELATIONS