Overview

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.

Syllabus

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

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