Python Level 3
[PYTHON 3]
Full 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.
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
Prerequisites:
Age 13+, PY02 or Instructor Permisssion
Related Classes
Related Classes
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!
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!