# Python Data Science & AI Machine Learning Program NYC (High School) Canonical URL: ## Overview Python is the leading language used by programmers today. It is also ideal for beginners because it is 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 Sci-Kit learn. These packages will teach you how to input, analyze, and graph data. You'll also get introduced to how AI tools can support your Python workflow by helping generate code, explain concepts, and speed up problem-solving. You'll also see how Python is used behind the scenes in many AI applications, especially when working with data and building the foundations of machine learning. - **Duration & Schedule** : This course will be held in person in NYC over the summer. The schedules include a 10 am-4 pm each day, as well as shorter 10 am-1 pm and 1:30 pm-4:30 pm schedules. This Python summer course is also available live online. - **Prerequisites & Ages** : The program is ideal for high school students with a strong interest in coding. Prior coding/programming experience is not required, but students should be comfortable with computer basics. This course is great for any teen interested in coding, finance, journalism, marketing, and communication. ## What you'll learn 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. - Learn Python fundamentals including data types, conditionals, loops, and functions - Manipulate and clean real-world data using Pandas and NumPy - Read, write, and process files while working with string methods and structured data - Visualize data with Matplotlib by creating custom charts, histograms, and plots - Explore machine learning techniques such as linear regression, classification, and K-nearest neighbors - Complete a capstone project that demonstrates your ability to analyze and present data insights ## Curriculum #### Introduction to Programming - History of Python - Understanding Hardware - Anaconda Distribution - Jupyter Notebook Fundementals - 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 Fundementals - Import Data - Functions - Basic Data Tool #### Advanced Python Fundementals - Lists - Mutating Operations - Tuples, Sets, Dictionaries - Loops - Control Flow - List Comprehension - Error Handeling #### Processing - String Methods - Read & Write to Text Files - Natrual Language Processing - Mini Project #### Object Oriented Programming - Classes - Constrcutors - Object Methods - Writing Modules - Advanced Scripting - Terminal & Socket Connection #### Numerical Python - Arrays - Universal Functions - Concatenating, Indexing, Slicing - Arithmetic & Boolean Operations #### Python Data Analysis:Pandas 1 - Data Series - Data Frames - Import CSV & Excel Files - Organize Data Frames - Data Manipulation - Descriptive Statstics #### 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 ### Machine Learning Fundamentals #### 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 — NYC - Jun 29, 2026 – Jul 17, 2026 — NYC - Jul 20, 2026 – Jul 30, 2026 — NYC - Aug 3, 2026 – Aug 13, 2026 — NYC ## FAQ ### How is this class structured? The first half of this class covers Python the language and general computer science topics. The following 20 or so hours cover data science topics such as descriptive statistics, data importation, graphical representation of data, and forecasting models. ### How many students are in a given class? NextGen's typical class ranges from 10-14 students, but we allow up to 17 students to register for our course. However, NextGen always maintains an 8:1 student to teacher ratio. ### Is there mandatory work outside of the classroom? Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice. ### 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:** $2195