ECONOMIC DATA ANALYSIS

ECON324

Course Code: ECON324
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Learn data, the importance of data analysis, basic data analysis methods
Learn how to import, organize, visualise, perform basic analyses and report data with R programming language at introductory level

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

ECON217, ECON218

Course Description

This course covers the basic concepts and methods of data analysis required for economic analysis, compiling, organizing and visualising data with the R programming language, performing basic analyses and reporting the results of the analysis.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to the course
2^nd Week	Data, data types and basic data analysis methods
3^rd Week	Installing R / RStudio
4^th Week	Basic R: Objects, functions and vectors
5^th Week	Basic R: Objects, functions and vectors
6^th Week	Data types and data structures
7^th Week	Midterm
8^th Week	Importing and exporting data, wrangling and summarizing data frames
9^th Week	Graphics with base R
10^th Week	Graphicas with ggplot
11^th Week	Graphicas with ggplot
12^th Week	Simple statistics in R
13^th Week	Programming in R
14^th Week	Reporting with R markdown

Recommended or Required Reading

Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau (2024), An Introduction to R,https://intro2r.com/
Alfonso Zamora Saiz vd. (2020), An Introduction to Data Analysis in R: Hands-on Coding, Data Mining, Visualization and Statistics from Scratch, Springer.
Franz Kronthaler, Silke Zöllner (2021), Data Analysis with RStudio: An Easygoing Introduction, Springer.
German Rodriguez (2022), Introducing R, https://grodri.github.io/R
W. N. Venables, D. M. Smith & the R Core Team (2024), An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics, Version 4.4.1 (2024-06-14), https://cran.r- project.org/doc/manuals/r-release/R-intro.pdf

Planned Learning Activities and Teaching Methods

Questions/Answers
Lecture
Discussion

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	1	20
Attendance	1	10
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	2	2
Preparation for Quiz
Individual or group work	14	3	42
Preparation for Final exam	1	2	2
Course hours	14	3	42
Preparation for Midterm exam	1	60	60
Laboratory (including preparation)	5	4	20
Final exam	1	2	2
Homework	1	3	3
Total Work Load			173
Total Work Load / 30 (h)			5.76
ECTS Credit of the Course			5

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2
K1
K2
K3
K4	X	X
K5	X	X
K6
K7	X	X
K8	X	X
K9
K10
K11	X	X
K12
K13