COURSE UNIT TITLE

: EXPLORATORY DATA ANALYSIS AND DATA VISUALIZATION

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5006 EXPLORATORY DATA ANALYSIS AND DATA VISUALIZATION 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

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The objective of this course is to teach exploratory data analysis and graphical techniques using the latest tools.

Learning Outcomes of the Course Unit

1   Learn the basic concepts of data exploration and visualization.
2   Develop a strategy for data analysis.
3   Use latest tools to clean and organize various types of data effectively.
4   Develop graphics for real life applications.
5   Effectively use programming languages (R, Python, etc.) to summarize and visualize large varieties and volumes of data.

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 statistical concepts and a brief introduction to statistical programming language R
2 The basics of graphics and the base plotting system in R
3 Base graphic functions, histogram, scatter plot, boxplots and batch comparison
4 Cleaning and transforming data
5 Data import / export and data manipulation functions in tidyverse
6 Data data manipulation functions in tidyverse
7 Advance graphics, ggplot2
8 Advance graphics, ggplot2
9 Anomaly detection
10 Anomaly detection
11 Multivariate visualization
12 Dynamic reporting, rmarkdown
13 Dynamic reporting, rmarkdown
14 Student presentations

Recomended or Required Reading

Textbook(s): :
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: Cambridge University Press.
Supplementary Book(s):
1. Springer Kabacoff, I. R. (2011). R in Action. Data Analysis and Graphics with R.
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 homework/presentation/report

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
Web Search and Library Research 1 40 40
Preparing report 1 45 45
Preparing assignments 1 27 27
Preparing presentations 1 27 27
Final 0 0 0
Midterm 0 0 0
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1455
LO.2455
LO.345555
LO.4455
LO.5455555