Weeks | Topics |
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.
|