COURSE UNIT TITLE

: EXPLORATORY DATA ANALYSIS WITH R

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5101 EXPLORATORY DATA ANALYSIS WITH R 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

ASSOCIATE PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

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   Use 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 fundamental topics of statistics, brief introduction to R
2 Probability distributions and random number generation
3 Transforming data
4 Exploratory graphs, Histogram, Scatter plot, Boxplots and multiple graphs
5 Density estimation
6 Advanced graphics, lattice
7 Advanced graphics, ggplot2 graphical system
8 Advanced graphics, ggplot2 graphical system
9 Advanced graphics, ggplot2 graphical system
10 Graphical devices
11 Clustering
12 Clustering
13 Monte Carlo techniques, resampling methods
14 Student presentations

Recomended or Required Reading

1. Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".
2. Maindonald, J., & Braun, W. (2010). Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press.

Supplementary Book(s):
1. Springer Kabacoff, I. R. (2011). R in Action. Data Analysis and Graphics with R.

Other Materials: Lecture Slides

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 exams, 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://www.fbe.deu.edu.tr/en/

Contact Details for the Lecturer(s)

Dr. Engin YILDIZTEPE
DEU Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
phone: +90232 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 final exam 1 50 50
Preparing assignments 1 50 50
Preparing presentations 1 50 50
Final 1 2 2
TOTAL WORKLOAD (hours) 208

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

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