# Python for Data Science & AI Machine Learning Online (High School & College)

Canonical URL: <https://www.graduateschool.edu/courses/python-summer-course>

## Overview

This course introduces students to the fundamentals of Python programming and shows how the language powers modern data science and machine learning. You’ll start with core coding concepts, gain confidence writing Python scripts, and quickly become ready to tackle data analysis projects using real-world techniques.

As the class progresses, you’ll work hands-on with libraries like Pandas, Matplotlib, and scikit-learn to import, analyze, and visualize data. Python’s ease of use makes it ideal for beginners, and this live online program is designed for high school and college students with an interest in coding, no prior experience required.

## What you'll 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

## Curriculum

### 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 Strcutres
- 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 Fundementals

- Lists
- Mutating Operations
- Tuples, Sets, Dictionaries
- Loops
- Control Flow
- List Comprehension
- Error Handeling

### 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

## Schedule
- Jun 29, 2026 – Jul 17, 2026 — Live Online
- Jun 29, 2026 – Jul 17, 2026 — Live Online
- Jul 20, 2026 – Jul 30, 2026 — Live Online
- Aug 3, 2026 – Aug 13, 2026 — Live Online

## Pricing

**Tuition:** $1699