Type of Credit: Elective
Credit(s)
Number of Students
This course introduces the basics of programming and some well-established econometric methods with Python, a free and powerful programming language for scientific data analysis. This course emphasizes improving the logic and analytical thinking of students rather than coding itself. Practice in making codes in classes will help students enhance their logic ability and feel familiar with programming.
This course starts with an introduction to Python, including program installment and the basic functions and methods. Then, we focus on data management and data analysis with Numpy, Pandas, and Seaborn. I will introduce regression analysis and statistical inferences if time allows.
能力項目說明
Students are expected to improve their logic and analytical thinking with the practice of coding. Students will learn basic econometric and statistical techniques in data analyses, which is extremely useful for their future careers in the industry or research.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
* This schedule is temporary and subject to change.
|
週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
||||
|
1 |
Introduction, Install Python (bring your laptop) |
Teacher's materials |
In class |
3 |
1 |
|
2 |
Expression |
Teacher's materials |
In class |
3 |
3 |
|
3 |
Function and Method |
Teacher's materials |
In class |
3 |
3 |
|
4 |
Conditional Expression |
Teacher's materials |
In class |
3 |
3 |
|
5 |
Loops and Iterations |
Teacher's materials |
In class |
3 |
3 |
|
6 |
Strings |
Teacher's materials |
In class |
3 |
3 |
|
7 |
Lists |
Teacher's materials |
In class |
3 |
3 |
|
8 |
Dictionaries |
Teacher's materials |
In class |
3 |
3 |
|
9 |
Tuples, Set, and Bool |
Teacher's materials |
In class |
3 |
3 |
|
10 |
Files |
Teacher's materials |
In class |
3 |
3 |
|
11 |
Numpy |
Teacher's materials |
In class |
3 |
3 |
|
12 |
Numpy |
Teacher's materials |
In class |
3 |
3 |
|
13 |
Pandas |
Teacher's materials |
In class |
3 |
3 |
|
14 |
Pandas |
Teacher's materials |
In class |
3 |
3 |
|
15 |
Files in Pandas DataFrame |
Teacher's materials |
In class |
3 |
3 |
|
16 |
Visualization (Seaborn) |
Teacher's materials |
In class |
3 |
3 |
|
17 |
Final Exam |
Teacher's materials |
In class |
3 |
3 |
|
18 |
Practice with practical cases |
Teacher's materials |
|
|
|
|
Quiz |
40% |
|
Final Exam |
40% |
|
Homework Class participation and others |
20% |
Important Notes:
|
1. Participation |
Those who miss a class more than four times will be given “Fail." If students are absent four times, they will receive F without a notification. |
|
2. Small Quiz |
This class does not have a written mid-term exam. Instead, at the beginning of each class, a small quiz will be provided for about 20 minutes. Questions will be given based on the materials taught in the previous week. |
|
3. Final exam |
The final exam is associated with coding with Python. |
< Notice >
Students are requested to bring their laptops to the first class (Sep 11) because we will try to install Anaconda (JupyterLab) and other packages.
1. Course materials will be distributed before each topic begins.
2. Students could find helpful information and lectures from PY4E (https://www.py4e.com/), Coursera, edX, or any youtube in your language.
3. Any books in your language help learn Python.