Print

COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
EKNM 306APPLIED ECONOMICS SOFTWARE - II3 + 06th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective The aim of this course is to make some econometric and statistical applications with the use of econometric and statistical software such as SPPS, Eviews etc. .
Course Content Throughout this course the topics which are covered by Econometrics II course will be treated using SPPS and Eviews softwares .
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1The skill of data entry and analysis in Excel
2The skill of data entry and analysis in Eviews
3The skill of data entry and analysis in SPSS
4The skill of data entry and analysis in Stata

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001555555555555
LO 0025555555555 5
LO 003555555555555
LO 004555555555555
Sub Total202020202020202020201520
Contribution555555555545

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)13339
Assignments11010
Mid-terms11313
Final examination12626
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Spring2MUSTAFA SERDAR İSPİR
Details 2013-2014 Spring1MUSTAFA SERDAR İSPİR
Details 2012-2013 Spring1MUSTAFA SERDAR İSPİR
Details 2011-2012 Spring2MUSTAFA SERDAR İSPİR
Details 2009-2010 Spring1MUSTAFA SERDAR İSPİR


Print

Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
EKNM 306 APPLIED ECONOMICS SOFTWARE - II 3 + 0 2 English 2023-2024 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. MUSTAFA SERDAR İSPİR sispir@pau.edu.tr İİBF A0017 %70
Goals The aim of this course is to make some econometric and statistical applications with the use of econometric and statistical software such as SPPS, Eviews etc. .
Content Throughout this course the topics which are covered by Econometrics II course will be treated using SPPS and Eviews softwares .
Topics
WeeksTopics
1 Introduction and Overview of Python • Topics: Basic features of the Python programming language. • Python Applications: Installation, variables, data types, and editors.
2 Basic Python Programming • Topics: Basic programming logic in Python. • Python Applications: Basic Python programming examples.
3 Basic Python Programming (Functions and Modules) • Topics: Defining functions, module usage, library imports. • Python Applications: Creating new functions, using existing modules.
4 Basic Python Programming (Logical Commands) • Topics: Logical Commands. • Python Applications: Conditional command processes.
5 Data Analysis (Data Collection and Reading) • Topics: Data collection, reading/writing data files, working with CSV and Excel files. • Python Applications: Data collection, data creation processes.
6 Data Analysis (Data Preprocessing) • Topics: Data cleaning and manipulation. • Python Applications: Data cleaning and manipulation examples with Pandas library.
7 • Week 7: Data Analysis (Data Visualization) • Topics: Chart creation. • Python Applications: Data visualization examples with Matplotlib and Seaborn libraries.
8 Midterm Exam
9 Econometric Analyses (Linear Regression-1) • Topics: Basic properties of econometric models. • Python Applications: Simple linear model estimation with statsmodels library.
10 Econometric Analyses (Linear Regression-2) • Topics: Basic tests after model estimation. • Python Applications: Post-model tests with statsmodels library.
11 Econometric Analyses (Logistic Regression-1) • Topics: Estimation of the Logit model. • Python Applications: Logit model estimation examples with statsmodels or scikit-learn libraries.
12 Econometric Analyses (Logistic Regression-2) • Topics: Estimation of the Probit model. • Python Applications: Probit model estimation examples with statsmodels or scikit-learn libraries.
13 Basic Machine Learning (Classification Problems) • Topics: Fundamentals of machine learning and classification. • Python Applications: Classification analysis examples with scikit-learn.
14 Basic Machine Learning (Decision Tree Models) • Topics: Mechanics of decision tree models. • Python Applications: Model prediction with scikit-learn library.
Materials
Materials are not specified.
Resources
ResourcesResources Language
- Lutz, M. (2013). Learning Python (5th ed.). O'Reilly Media.4. Türkçe
- Stock, J. H., & Watson, M. W. (2018). Introduction to Econometrics (4th ed.). Pearson. Türkçe
-Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.). O'Reilly Media.Türkçe
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam60Final Exam
Midterm Exam40Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes