GRADUATE SCHOOL
M.SC. In Industrial Engineering (With Thesis)
MATH 658 | Course Introduction and Application Information
| Course Name |
Advanced Data Analysis
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MATH 658
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Third Cycle
|
|||||
| 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, two-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
|
4
|
56
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
1
|
23
|
23
|
| Project |
1
|
28
|
28
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
30
|
30
|
| Final Exam |
1
|
40
|
40
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. |
|||||
| 2 | To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
|||||
| 3 | To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
|||||
| 4 | To be able to conduct basic research and write and publish articles in related conferences and journals. |
|||||
| 5 | To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
|||||
| 6 | To be able to manage decision-making processes in industrial systems. |
|||||
| 7 | To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. |
|||||
| 8 | To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. |
|||||
| 9 | To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. |
|||||
| 10 | To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
|||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest