Python Data Science & Machine Learning Live Online (High School & College)

This course will cover the fundamentals of Python programming and its applications in data science and machine learning. Students will get up and running in Python quickly and be ready to use Python for data analysis projects.

Python is one of the leading languages used by programmers today. It is the ideal language for beginners because it's both powerful and easy to learn.

In the first half of this hands-on Python course, you will begin by learning the fundamentals of Python code and then transition into more complicated programming tasks. The second half of the course focuses primarily on data science using Pandas, Matplotlib, and scikit-learn. These packages will teach you how to input, analyze, and visualize data.

Class Notes

• Method of Delivery: Live Online (live-streamed with the ability to ask questions and interact with the instructor in real-time).
• Prerequisites & Ages: The program is ideal for high school and college students with a strong interest in coding. Prior coding/programming experience is not required, but students must be comfortable with computer basics.
• Computer: Live online attendees should have their own Mac or PC. We will assist with any software setup prior to the course.

What You Will Learn

• Programming fundamentals in Python
• How to write conditional statements in Python
• Import and manipulate data using the pandas package
• Clean and wrangle data
• Visualize and interpret complex data
• Use machine learning algorithms

Course Outline

Day 1-3

Introduction to Programming

• History of Python
• Understanding Hardware
• Anaconda Distribution
• Jupyter Notebook Fundamentals
• Writing First Program (“Hello World”)

Terminal Commands

• Navigate & Manipulate Directory Structures
• Edit Files
• Basic Scripting

Python Fundamentals

• Data Types
• Operators
• Expression
• Indexing & Slicing
• Strings
• Conditionals
• Functions
• Control Flow
• Nested Loops
• Sets & Dictionaries

Data Science Fundamentals

• Import Data
• Functions
• Basic Data Tool

Advanced Python Fundamentals

• Lists
• Mutating Operations
• Tuples, Sets, Dictionaries
• Loops
• Control Flow
• List Comprehension
• Error Handling

Day 4-5

Processing

• String Methods
• Read & Write to Text Files
• Natural Language Processing
• Mini Project

Object Oriented Programming

• Classes
• Constructors
• Object Methods
• Writing Modules
• Advanced Scripting
• Terminal & Socket Connection

Day 6-8

Numerical Python

• Arrays
• Universal Functions
• Concatenating, Indexing, Slicing
• Arithmetic & Boolean Operations

Day 9-10

Python Data Analysis: Pandas 1

• Data Series
• Data Frames
• Import CSV & Excel Files
• Organize Data Frames
• Data Manipulation
• Descriptive Statistics

Advanced Python

• File Input
• User Input
• List Comprehension
• Packages

Data Analysis

• Cleaning Data
• Filtering Data
• Advanced Grouping
• Pivot Tables

Data Visualization

• Plotting with Matplotlib
• Scatter Plots
• Histograms & Bar Plots
• Custom Visualizations

Day 11-15

Basic Regression Analysis

• Linear Regression
• Mean squared error
• Training set vs Test set
• Cross validation

Advanced Regression Analysis

• Multi-linear regression
• Feature engineering
• Overfitting

Classification

Logistic Regression

• Regression vs Classification
• Logistic Regression
• Sigmoid function

K-nearest Neighbors

• K-nearest neighbors
• Model-based vs memory-based
• Parametric vs non-parametric
• Evaluating performance

Final Project

Details

• Curate Data
• Import, Clean, and Merge Data
• Analyze Data
• Visualize Data
• Present Results