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.
Dersin Öğrenme Kazanımları
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.
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
1
2
3
4
5
1
(S)he describes theoretical knowledge in economics and finance.
2
(S)he explains mathematical and statistical methods needed for economics and finance.
X
3
(S)he uses at least one computer program utilized for economic and financial analyses (SPSS, Eviews, STATA, R ve MATLAB).
X
4
(S)he has the foreign language proficiency necessary for economics and finance.
5
(S)he develops projects in the field and handles team work.
X
6
(S)he develops (her) his awareness of lifetime learning, follows the developments in (her) his field and adopts a critical approach.
X
7
(S)he uses theoretical and practical knowledge on economics and finance.
X
8
(S)he delivers (her) his opinions by making effective use of modern technologies and of at least one foreign language at a minimum level of level C1.
X
9
(S)he adopts and uses organizational, corporate and social ethical values.
10
(S)he adopts principles of social responsibility and acts whenever needed in light of social service sensitivity.
11
(S)he analyzes and uses basic knowledge and data regarding different disciplines to conduct inter-disciplinary studies.
X
12
(S)he benefits from (her) his proficiency in economics and finance to make policy suggestions and contribute to the field.
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
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
APPLIED DATA ANALYSIS
ECO3114316
Fall Semester
3+0
3
5
Course Program
Çarşamba 11:00-11:45
Çarşamba 12:00-12:45
Çarşamba 12:45-13:30
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.
Dersin Öğrenme Kazanımları
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.
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
1
2
3
4
5
1
(S)he describes theoretical knowledge in economics and finance.
2
(S)he explains mathematical and statistical methods needed for economics and finance.
X
3
(S)he uses at least one computer program utilized for economic and financial analyses (SPSS, Eviews, STATA, R ve MATLAB).
X
4
(S)he has the foreign language proficiency necessary for economics and finance.
5
(S)he develops projects in the field and handles team work.
X
6
(S)he develops (her) his awareness of lifetime learning, follows the developments in (her) his field and adopts a critical approach.
X
7
(S)he uses theoretical and practical knowledge on economics and finance.
X
8
(S)he delivers (her) his opinions by making effective use of modern technologies and of at least one foreign language at a minimum level of level C1.
X
9
(S)he adopts and uses organizational, corporate and social ethical values.
10
(S)he adopts principles of social responsibility and acts whenever needed in light of social service sensitivity.
11
(S)he analyzes and uses basic knowledge and data regarding different disciplines to conduct inter-disciplinary studies.
X
12
(S)he benefits from (her) his proficiency in economics and finance to make policy suggestions and contribute to the field.
