Class Projects
Students will build, test, and publish their own game in KTBlocks
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+, PY02 or Instructor Permisssion
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+, PY02 or Instructor Permisssion
Related Classes
Syllabus:
Course Overview, Python Review
Review of basic Python concepts: Variables, conditionals, for loops, functions, general syntax.
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.
Introductory Statistics
Central tendencies, mean vs median, population vs sample, standard deviation, variance.
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.
File Input and Output
Reading from text (.txt) files, Data analysis using matplotlib.
APIs I
GET vs POST requests, getting data, handling data, analyzing data using statistical methods. Using Rapid API's Weather API. Visualizing data using matplotlib.
Recursive Algorithms I
Basic recursion, finding sum of a list recursively, Fibonacci sequence, factorials, recursive trees with Python turtles, introduction to markov chains
Recursive Algorithms II
Geometric series, intro to time complexity, finding time complexity recursively, bubble sort. (Optional: 1st/2nd order recursive relations, Binet's formula)
Simulations I - Random Simulations
Coding probabilistic simulations in Python, random walks, coin flipping, estimating pi using matplotlib, geometric probability.
Simulations II - Matrices
Using Python to simulate discrete dynamical systems, advanced matplotlib, introduction to linear algebra, matrices, vectors
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
Course Overview, Python Review
Review of basic Python concepts: Variables, conditionals, for loops, functions, general syntax.
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.
Introductory Statistics
Central tendencies, mean vs median, population vs sample, standard deviation, variance.
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.
File Input and Output
Reading from text (.txt) files, Data analysis using matplotlib.
APIs I
GET vs POST requests, getting data, handling data, analyzing data using statistical methods. Using Rapid API's Weather API. Visualizing data using matplotlib.
Recursive Algorithms I
Basic recursion, finding sum of a list recursively, Fibonacci sequence, factorials, recursive trees with Python turtles, introduction to markov chains
Recursive Algorithms II
Geometric series, intro to time complexity, finding time complexity recursively, bubble sort. (Optional: 1st/2nd order recursive relations, Binet's formula)
Simulations I - Random Simulations
Coding probabilistic simulations in Python, random walks, coin flipping, estimating pi using matplotlib, geometric probability.
Simulations II - Matrices
Using Python to simulate discrete dynamical systems, advanced matplotlib, introduction to linear algebra, matrices, vectors
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)