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
|
8
|
Prerequisites |
None
|
|||||
Course Language |
English
|
|||||
Course Type |
Elective
|
|||||
Course Level |
First Cycle
|
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Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Application: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
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;
|
Course Description |
|
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, H. Wickham, G. Grolemund, (Ch-1, Ch-2), Introductory Statistics with R, P. Dalgaard (Ch-1) |
2 | Data structures in R, built-in functions, R packages | Introductory Statistics with R, P. Dalgaard (Ch-1) |
3 | Random data, density and distribution functions, data import and export, data manipulation | Introductory Statistics with R, P. Dalgaard (Ch-3) |
4 | Control structures, conditional statements | Introductory Statistics with R, P. Dalgaard (Ch-1.2) |
5 | Quantitative methods to describe data, relationships between several variables | Introductory Statistics with R, P. Dalgaard (Ch-4) |
6 | Data visualization, graphical methods to describe data, base graphics system in R, basic graphs | Introductory Statistics with R, P. Dalgaard (Ch-4.2) |
7 | Advanced graphics in R -1, tidyverse syntax, Advanced graphics in R -2, ggplot2 | R for Data Science, H. Wickham, G. Grolemund, (Ch-3) |
8 | Midterm Exam | |
9 | Hypothesis testing, one sample tests | Introductory Statistics with R, P. Dalgaard (Ch-5) |
10 | Hypothesis testing, two-sample tests | Introductory Statistics with R, P. Dalgaard (Ch-5) |
11 | Checking assumptions, goodness of fit tests | Introductory Statistics with R, P. Dalgaard (Ch-5) |
12 | Simple lineer regression and correlation | Introductory Statistics with R, P. Dalgaard (Ch-6) |
13 | Dynamic reporting | R for Data Science, H. Wickham, G. Grolemund, (Ch-27) |
14 | Data mining, basic concepts of statistical learning, supervised learning, unsupervised learning | R for Data Science, H. Wickham, G. Grolemund, (Ch-22) |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | 1- Introductory Statistics with R, P. Dalgaard, Springer, 2008. ISBN-13: 978-0-387-79054-1. (https://link.springer.com/book/10.1007/978-0-387-79054-1#toc)
2- R for Data Science, H. Wickham, G. Grolemund, 978-1491910399. (https://r4ds.had.co.nz/) |
Suggested Readings/Materials | 1- R in Action: Data Analysis and Graphics with R. 2nd Ed., R. Kabacoff, 2015. 978-1617291388.
2- Practical Data Science with R, N. Zumel and J. Mount, Manning Publications, 2014. 9781617291562. |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
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
|
||
Portfolio |
0
|
||
Homework / Assignments |
0
|
||
Presentation / Jury |
1
|
25
|
25
|
Project |
1
|
40
|
40
|
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
1
|
40
|
40
|
Final Exam |
1
|
45
|
45
|
Total |
240
|
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. |
X | ||||
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. |
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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. |
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9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. |
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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. |
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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) |
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12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
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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. |
X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest