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.
Course Description The students who succeeded in this course;
  • will be able to use graphical methods to describe and summarize data
  • will be able to analyze relationshiops between variables
  • will be able to model relationships between variables using regression models
  • will be able to compare several population means
  • will be able to test hypotheses related to a population
  • • will be able to discuss the basic concepts of Data Mining
Course Content

 



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 (Installation, Editors) Introduction (R for Data Science) Basics (Introductory Statistics with R)
2 Data Structures in R, Built-in functions, R packages Basics, The R environment (Introductory Statistics with R)
3 Random data, density and distribution functions, Data Import and Export, Data Manipulation Probability and distributions (Introductory Statistics with R)
4 Control Structures, Conditional statements The R environment (Introductory Statistics with R)
5 Quantitative methods to describe data, Relationships between several variables Descriptive statistics and graphics (Introductory Statistics with R)
6 Data Visualization Graphical methods to describe data, Base graphics system in R, Basic graphs Descriptive statistics and graphics (Introductory Statistics with R)
7 Advanced graphics in R, ggplot2 Data visualization (R for Data Science)
8 Hypothesis testing, One sample tests One- and two-sample tests (Introductory Statistics with R)
9 Hypothesis testing, Two-sample tests, Analysis of Variance One- and two-sample tests (Introductory Statistics with R)
10 Nonparametric Test of Hypotheses, One Sample tests, Goodness of Fit tests One- and two-sample tests (Introductory Statistics with R)
11 Nonparametric Test of Hypotheses, Two sample tests, k-samples tests One- and two-sample tests Analysis of variance and the Kruskal–Wallis test (Introductory Statistics with R)
12 Linear regression models Regression and correlation (Introductory Statistics with R)
13 Basics of Data Mining Introduction (Data Mining: Concepts and Techniques)
14 Basics of Data Mining Introduction (Data Mining: Concepts and Techniques)
15 Review of the Semester
16 Review of the Semester

 

Course Notes/Textbooks

Lecture Notes

 

Introductory Statistics with R, P. Dalgaard, Springer, 2008.7.

Suggested Readings/Materials

R for Data Science, H. Wickham, G. Grolemund, 2017.

 

Practical Data Science with R, N. Zumel and J. Mount, Manning Publications, 2014.

 

Data Mining: Concepts and Techniques, Han, M. Kamber, and J. Pei, Morgan Kaufmann, 2011.

 

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
Portfolios
Midterms / Oral Exams
1
30
Final / Oral 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
Course Hours
Including exam week: 16 x total hours
16
3
48
Laboratory / Application Hours
Including exam week: 16 x total hours
16
Study Hours Out of Class
12
2
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
16
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
20
Final / Oral Exam
1
30
    Total
158

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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