FACULTY OF ARTS AND SCIENCES

Department of Mathematics

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
Mode of Delivery face to face
Teaching Methods and Techniques of the Course Application: Experiment / Laboratory / Workshop
Lecture / 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;
  • 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
X
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 be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.

X
2

To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.

X
3

To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.

X
4

To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.

5

To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.

6

To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.

7

To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.

X
8

To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.

9

To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.

10

To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.

11

To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.

12

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

13

To be able to relate the knowledge accumulated throughout the human history to their field of expertise.

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

 


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