Python Level 3

[PYTHON 3]

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Full Course

$1100 USD

Before any discounts or coupons

for 18 hours

Class Package

Students will make projects including a variety of games and applications. These can range from medium to advanced complexity.

Our Proprietary In-Browser Coding Platform.

The KTBYTE team developed the KTCoder™ with our students in mind! Most coding classes require software downloads, which is difficult and a hassle for young students. Our platform supports Processing (Java), Python, C++, KTBlocks, HTML/CSS, and a bunch of libraries (like Soundcipher and Fisica!). This revolutionary all-in-one coding platform supports our interactive online classes, our specialized curriculum, and (most importantly) our student's passion for learning.

KTBYTE will e-mail parents with behavior and grade progess reports.

Students can request a certificate of completion once they finish the 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

Syllabus

Course Overview, Functions and Imports Review

Review intermediate Python coding skills with imports and functions including outputs and kwargs.

Lists and Dictionaries Review

Review List and Dictionaries, including putting these two data structures together.

Introduction to NumPy I

Efficiency of NumPy arrays, difference between NumPy arrays and regular Python lists. Basic NumPy array declaration methods.

Introduction to NumPy II

Working with NumPy array operations, vectorized operations, time complexity.

Introductory Statistics

Central tendencies, mean vs median, population vs sample, standard deviation, variance.

Introduction to Pandas

Basics of Pandas, converting from .csv to DataFrames, Pandas Series, operations with DataFrames (e.g. .loc, .iloc, [], etc.). Handling JavaScript Object Notation (JSON).

File Input and Output

Learn how to read from files and write to a new file, including how to handle JavaScript Object Notation (JSON).

Weather API I

GET vs POST requests, getting data, handling data, analyzing data using statistical methods. Using Rapid API's Weather API. Visualizing data using matplotlib.

Weather API II

More statistical analysis techniques introduced, continuing on from projects.

yFinance

Getting financial data through yFinance. Analyzing simple data from financial products (e.g. stocks and bonds). Introduction to moving averages.

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, finding time complexity recursively, bubble sort. (Optional: 1st/2nd order recursive relations, Binet's formula)

Monte Carlo Methods

Coding probabilistic simulations in Python, random walks, coin flipping, estimating pi using matplotlib, geometric probability.

Discrete dynamical systems

Using Python to simulate discrete dynamical systems, advanced matplotlib, introduction to linear algebra, matrices, vectors

Time Complexity I

Introduction to elementary operations, bounding functions, big-O notation, definition of big-O.

Time Complexity II

Recursive time complexity analysis, big theta, big omega notations.

Final Project

During the last 2 weeks of the class, create your own final project!