课程项目
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进度报告
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结业证书
学生完成每门课程后均可申请结业证书。
課程描述:
Python Level 3将引导学生深入理解Python编程。课程会先复习基础内容，例如列表、循环、函数等，然后逐步深入学习高级函数与算法、类的应用、以及JSON数据格式。这些内容将自然过渡到使用免费API进行实际项目开发。 课程后半部分将引入Python在数据统计与数据科学领域的应用，学生将学习如何使用Pandas的DataFrame、NumPy，以及 Matplotlib的绘图模块Pyplot。
入班要求
适合13岁以上，完成 [Python 2] 或获得老师许可的学生
Class Description:
Python Level 3将引导学生深入理解Python编程。课程会先复习基础内容，例如列表、循环、函数等，然后逐步深入学习高级函数与算法、类的应用、以及JSON数据格式。这些内容将自然过渡到使用免费API进行实际项目开发。 课程后半部分将引入Python在数据统计与数据科学领域的应用，学生将学习如何使用Pandas的DataFrame、NumPy，以及 Matplotlib的绘图模块Pyplot。
Prerequisites:
适合13岁以上，完成 [Python 2] 或获得老师许可的学生
Related Classes
Syllabus:
Advanced Functions - *Args, **Kwargs
Review intermediate Python coding skills with imports and functions including outputs and kwargs.
Advanced List Methods
Review of lists, list alias, list slicing, pointers, cloning list methods
Numerical Python (NumPy) I
Efficiency of NumPy arrays, difference between NumPy arrays and regular Python lists. Basic NumPy array declaration methods.
Numerical Python (NumPy) II
Working with NumPy array operations, vectorized operations, time complexity.
Visualizing Data
Introduction to data visualization with Matplotlib. Scatter plots, histograms, subplots, and axis scaling (linear vs log).
Pandas & DataFrames I
Basics of Pandas, converting from .csv to DataFrames, Pandas Series, operations with DataFrames (e.g. .loc, .iloc, [], etc.).
Pandas & DataFrames II
Filtering data using complex conditionals (&, |), Slicing data, Grouping and sorting data.
Feature Engineering
Basics of machine learning, categorical features, one-hot encoding, text features
Random Simulations
Coding probabilistic simulations in Python, random walks, coin flipping, estimating pi using matplotlib, geometric probability.
Time Series Analysis
Analyzing time-series data using Python. Decomposing signals into trend, seasonality, and noise. Visualization with Matplotlib [Climate Data](https://drive.google.com/file/d/1KDqRRlieVyBqTf_rjTxJvT3--u54XEm1/view?usp=sharing)
Final Project
[Project Guideline](https://docs.google.com/document/d/1owLtjYdwTgDZdAWLo5s2FSNr2BmeHjJxjAMXjzIgFuo/edit?usp=sharing)<br> [Project Planning](https://docs.google.com/document/d/1bNQ1nMSzzcjNUccRmt_Iq4lR6lE8-alV8V8wyVJ80gM/edit?usp=sharing)
课程大纲
Advanced Functions - *Args, **Kwargs
Review intermediate Python coding skills with imports and functions including outputs and kwargs.
Advanced List Methods
Review of lists, list alias, list slicing, pointers, cloning list methods
Numerical Python (NumPy) I
Efficiency of NumPy arrays, difference between NumPy arrays and regular Python lists. Basic NumPy array declaration methods.
Numerical Python (NumPy) II
Working with NumPy array operations, vectorized operations, time complexity.
Visualizing Data
Introduction to data visualization with Matplotlib. Scatter plots, histograms, subplots, and axis scaling (linear vs log).
Pandas & DataFrames I
Basics of Pandas, converting from .csv to DataFrames, Pandas Series, operations with DataFrames (e.g. .loc, .iloc, [], etc.).
Pandas & DataFrames II
Filtering data using complex conditionals (&, |), Slicing data, Grouping and sorting data.
Feature Engineering
Basics of machine learning, categorical features, one-hot encoding, text features
Random Simulations
Coding probabilistic simulations in Python, random walks, coin flipping, estimating pi using matplotlib, geometric probability.
Time Series Analysis
Analyzing time-series data using Python. Decomposing signals into trend, seasonality, and noise. Visualization with Matplotlib [Climate Data](https://drive.google.com/file/d/1KDqRRlieVyBqTf_rjTxJvT3--u54XEm1/view?usp=sharing)
Final Project
[Project Guideline](https://docs.google.com/document/d/1owLtjYdwTgDZdAWLo5s2FSNr2BmeHjJxjAMXjzIgFuo/edit?usp=sharing)<br> [Project Planning](https://docs.google.com/document/d/1bNQ1nMSzzcjNUccRmt_Iq4lR6lE8-alV8V8wyVJ80gM/edit?usp=sharing)