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

Canonical URL: <https://www.nextgenbootcamp.com/classes/python-summer-course-level-1>

## 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 the leading language used by programmers today! It is the ideal language for beginners because it's both powerful and easy to learn. You'll also explore how AI tools can work alongside Python to speed up coding, generate scripts, and help debug your work more efficiently. As you dive into data science, you'll see how Python powers real-world AI applications, from analyzing datasets to building the foundations of machine learning models.

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 Sci-Kit learn. These packages will teach you how to input, analyze, and graph 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 before the course.

## What you'll learn

- Write Python scripts using key concepts like variables, functions, loops, and conditionals
- Use lists, dictionaries, and sets to organize and manipulate data efficiently
- Read and write text and CSV files, and clean and prepare data for analysis using Pandas
- Visualize data through custom charts with Matplotlib, including scatter plots and histograms
- Explore core machine learning techniques such as linear regression and K-nearest neighbors
- Complete a final project using real datasets to present insights with analysis and visuals

## 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

## FAQ

### Do you offer payment plans or student financing for this course?

This course does not qualify for payments plans or student financing.

### What's included with my tuition?

- A hands-on learning experience working on projects and exercises, which is proven to boost comprehension, retention, and engagement
- Expert instructors who are industry professionals and experienced educators that are driven to help you succeed
- Top-notch curricula that have been tried and tested over many cohorts and are consistently improved for an optimal learning experience
- Supplemental materials to assist both during and after the course - please refer to specific course pages to see what supplemental materials are offered
- A certificate of completion to verify your accomplishment

## Pricing

**Tuition:** $1699