Class Projects
Students will build, test, and publish their own projects in Processing Java.
CODING PLATFORM
The KTCoder all-in-one coding platform supports our interactive online classes, our specialized curriculum, and our students’ passion for learning.
STUDENT HELP HOURS
Help hours are led by our highly qualified teaching assistants. It is an easy and free way to get immediate feedback on your code.
PROGRESS REPORTS
KTBYTE will e-mail parents with behavior and grade progess reports.
COMPLETION CERTIFICATES
Students can request a certificate of completion once they finish each course.
Class Description:
Python Level 3 is your passport to a deeper understanding of Python. We will start by reviewing the basics – lists, loops, functions, etc. – before moving on to more advanced features. We then go over more advanced functions and function algorithms, classes, and JSONS, which segways us into APIs and programs using free APIs. We finish off with an introduction to data statistics and science with Python, using Pandas’ DataFrames, Numpy, and Matplotlib’s Pyplot.
Prerequisites
Age 13+, [Python 2] or Instructor Permission
Class Description:
Python Level 3 is your passport to a deeper understanding of Python. We will start by reviewing the basics – lists, loops, functions, etc. – before moving on to more advanced features. We then go over more advanced functions and function algorithms, classes, and JSONS, which segways us into APIs and programs using free APIs. We finish off with an introduction to data statistics and science with Python, using Pandas’ DataFrames, Numpy, and Matplotlib’s Pyplot.
Prerequisites:
Age 13+, [Python 2] or Instructor Permission
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)
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)