Course Detail

Course Description Course Outline Course Contribution to Program Qualifications Assessment Methods ECTS-Workload Detail Informations of the Course Numerical Data

Course Description

Course	Code	Semester	T+P (Hour)	Credit	ECTS
APPLIED DATA ANALYSIS	MIS4114316	Fall Semester	3+0	3	5

Course Program

Çarşamba 08:00-08:45

Çarşamba 09:00-09:45

Çarşamba 10:00-10:45

Cumartesi 08:00-08:45

Cumartesi 09:00-09:45

Cumartesi 10:00-10:45

Prerequisites Courses
Recommended Elective Courses

Language of Course	English
Course Level	First Cycle (Bachelor's Degree)
Course Type	Elective
Course Coordinator	Assist.Prof. Recep ÖZSÜRÜNÇ
Name of Lecturer(s)	Assist.Prof. Recep ÖZSÜRÜNÇ
Assistant(s)
Aim	Using R and R Studio software, importing data to the software, editing data, and analyzing and interpreting the results.
Course Content	This course contains; Introduction to R Programming and Basic Functions,Installing Packages and Importing Data (Excel and SPSS), Data Types and Scale of Measurements (Nominal (factor, character), Ordinal (integer), Interval (numeric) and Ratio(numeric) ),Creating data.frame and Manipulating Data,Summary Statistics of Data and Interpretation,Summary Statistics of Data and Interpretation (continue),Applications on Real Data,Visualizing Data (Histogram and Box Plots),Visualizing Data (Scatter Plots) ,Visualizing Data (Clustering),Visualizing Data (Clustering)-Continue,Visualizing Data (Time Series Data),Visualizing Data (Time Series Data)-Continue,Applications on Real Data.

Course Learning Outcomes	Teaching Methods	Assessment Methods
1. Will be able to analyze data with codes in R programming language.	12, 16, 4, 9	A, F
1.1 Can import and edit data in the R program.
1.2 Can export the analysis results of the data in the desired format.
2. Will be able to make statistical data analysis.	12, 16, 4, 9	A, F
2.1 can explain data types.
2.2. Can determine the appropriate statistical test for the data.
2.3. Can interpret the results of the statistical analysis.
3. Will be able to make the necessary visualizations with the data through the R program.	12, 16, 4, 9	A, F
3.1. Can draw histograms and box plots using the data set.
3.2. Can draw scatter plots using the data set.
4. will be able to run the R codes and commands, knowing the logic of the codes and commands, and running the necessary codes for the data.	12, 16, 4	A, F
4.1. can explain the working logic of R codes.
4.2. can explain the working logic of the commands in R codes.
5. will be able to import data from external software such as Excel and SPSS and export the R data to these software.	12, 16, 4, 9	A, F
5.1. can import data from external software such as Excel and SPSS.
5.2. Can export R data to software such as Excel and SPSS.

Teaching Methods:	12: Problem Solving Method, 16: Question - Answer Technique, 4: Inquiry-Based Learning, 9: Lecture Method
Assessment Methods:	A: Traditional Written Exam, F: Project Task

Course Outline

Order	Subjects	Preliminary Work
1	Introduction to R Programming and Basic Functions
2	Installing Packages and Importing Data (Excel and SPSS)
3	Data Types and Scale of Measurements (Nominal (factor, character), Ordinal (integer), Interval (numeric) and Ratio(numeric) )
4	Creating data.frame and Manipulating Data
5	Summary Statistics of Data and Interpretation
6	Summary Statistics of Data and Interpretation (continue)
7	Applications on Real Data
8	Visualizing Data (Histogram and Box Plots)
9	Visualizing Data (Scatter Plots)
10	Visualizing Data (Clustering)
11	Visualizing Data (Clustering)-Continue
12	Visualizing Data (Time Series Data)
13	Visualizing Data (Time Series Data)-Continue
14	Applications on Real Data

Resources

Bivand, R. S., Pebesma, E. J., Gómez-Rubio, V., & Pebesma, E. J. (2008). Applied spatial data analysis with R (Vol. 747248717, pp. 237-268). New York: Springer. Lecture notes and files shared in teams Witten, D., & James, G. (2013). An introduction to statistical learning with applications in R. springer publication.

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer. https://www.youtube.com/@statquest Follow this channel and watch relevant videos.

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
No	Program Qualification	Contribution Level
No	Program Qualification	1	2	3	4	5
1	Defines the theoretical issues in the field of information and management.
2	Describes the necessary mathematical and statistical methods in the field of information and management.
3	Uses at least one computer program in the field of information and management.
4	Sustains proficiency in a foreign language requiredor information and management studies.
5	Prepares informatics/software projects and work in a team.
6	Constantly updates himself / herself by following developments in science and technology with an understanding of the importance of lifelong learning through critically evaluating the knowledge and skills that s/he has got.7. Uses theoretical and practical expertise in the field of information and management
7	Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication skills.
8	Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication.
9	Adopts organizational / institutional and social ethical values.
10	Within the framework of community involvement adopts social responsibility principles and takes initiative when necessary.
11	Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies.
12	Writes software in different platforms such as desktop, mobile, web on its own and / or in a team.

Assessment Methods

Contribution Level	Absolute Evaluation
Rate of Midterm Exam to Success		40
Rate of Final Exam to Success		60
Total		100

ECTS / Workload Table
Activities	Number of	Duration(Hour)	Total Workload(Hour)
Course Hours	14	3	42
Guided Problem Solving	3	14	42
Resolution of Homework Problems and Submission as a Report	1	14	14
Term Project	4	2	8
Presentation of Project / Seminar	0	0	0
Quiz	0	0	0
Midterm Exam	1	20	20
General Exam	1	20	20
Performance Task, Maintenance Plan	0	0	0
Total Workload(Hour)			146
*Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30=(146/30)**			5
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.

Detail Informations of the Course