Syllabus | İzmir University of Economics

FACULTY OF ENGINEERING

Department of Software Engineering

MATH 485 | Course Introduction and Application Information

Course Name
Exploratory Data Analysis
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 485
Fall/Spring
3
0
3
6

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator -
Course Lecturer(s)
Assistant(s)
Course Objectives The main objective of this course is to provide a basic understanding of data analysis concepts and to use it in applications with using some statistical software packages. The course will cover basic approaches in statistical inference and data mining, as well as modeling.
Learning Outcomes The students who succeeded in this course;
  • will be able to use graphic methods to describe and summarize data.
  • will be able to analyze the relationships between variables.
  • will be able to analyze the relationships between variables and regression models.
  • will be able to compare several audience averages.
  • will be able to create hypothesis tests for an audience.
  • will be able to use simple classification methods in data mining concepts.
Course Description

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to data analysis, data science, data scientist, data scientist’s toolbox, SPSS - Introduction to R environment "R for Data Science" by H. Wickham, G. Grolemund, O'Reilly Media, 1st Edition, 2017. ISBN-13: 978-1491910399, Introduction and "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Basics
2 Data structures in R, built-in functions, R packages "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Basics, The R environment
3 Random data, density and distribution functions, data import and export, data manipulation "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Probability and distributions
4 Control structures, conditional statements "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, The R environment
5 Quantitative methods to describe data, relationships between several variables "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Descriptive statistics and graphics
6 Data visualization, graphical methods to describe data, base graphics system in R, basic graphs "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Descriptive statistics and graphics
7 Advanced graphics in R, ggplot2 "R for Data Science" by H. Wickham, G. Grolemund, O'Reilly Media, 1st Edition, 2017. ISBN-13: 978-1491910399, Data visualization
8 Hypothesis testing, one sample tests "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, One- and two-sample tests
9 Hypothesis testing, two-sample tests, analysis of variance "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, One- and two-sample tests
10 Nonparametric test of hypotheses, one sample tests, goodness of fit tests "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, One- and two-sample tests
11 Nonparametric test of hypotheses, two sample tests, k-samples tests "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, One- and two-sample tests Analysis of variance and the Kruskal–Wallis test
12 Linear regression models "Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534, Regression and correlation
13 Basics of data mining "Data Mining: Concepts and Techniques" by Han, M. Kamber, and J. Pei, Morgan Kaufmann, 3rd Edition, 2011. ISBN-13: 978-9380931913, Introduction
14 Basics of data mining "Data Mining: Concepts and Techniques" by Han, M. Kamber, and J. Pei, Morgan Kaufmann, 3rd Edition, 2011. ISBN-13: 978-9380931913, Introduction
15 Semester Review
16 Final Exam

 

Course Notes/Textbooks

"Introductory Statistics with R" by P. Dalgaard, Springer, 2nd Edition, 2008. ISBN-13: 978-0387790534

Lecture Notes

Suggested Readings/Materials

"R for Data Science" by H. Wickham, G. Grolemund, O'Reilly Media, 1st Edition, 2017. ISBN-13: 978-1491910399

"Data Mining: Concepts and Techniques" by  Han, M. Kamber, and J. Pei, Morgan Kaufmann, 3rd Edition, 2011. ISBN-13: 978-9380931913

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
10
Project
1
20
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
3
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Homework / Assignments
0
Presentation / Jury
1
15
15
Project
1
20
20
Seminar / Workshop
0
Oral Exam
0
Midterms
1
25
25
Final Exam
1
30
30
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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