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Python Programming

Learn Python from fundamentals to data science and AI-ready projects.

A structured Python path for middle school, high school, and advanced students. Students build programming confidence first, then move into automation, data analysis, visualization, and introductory machine learning when they are ready.

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Python programming on a laptop

Student tools

Practice CenterFlashcardsOnline Compiler

Course Focus

Python that students can actually use

Foundations

Variables, expressions, strings, conditionals, loops, functions, and debugging habits.

Data Structures

Lists, tuples, dictionaries, sets, nested data, traversal, searching, and sorting.

Automation

Files, CSV data, APIs, web requests, scripts, and practical workflows students can reuse.

Data Science

NumPy, pandas, visualization, notebooks, data cleaning, statistics, and model-readiness.

Detailed Curriculum

A full Python sequence from coding basics to data science

Unit 1

Python Foundations

  • Variables, expressions, input/output, type conversion, and arithmetic
  • Strings, indexes, slices, common methods, formatting, and parsing
  • Conditionals, Boolean logic, loops, counters, accumulators, and tracing
  • Debugging with print tracing, error messages, and small test cases
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Unit 2

Functions and Program Design

  • Function parameters, return values, scope, helper functions, and decomposition
  • Writing reusable code with clear names, preconditions, and test examples
  • Randomness, simulations, menu-driven programs, and input validation
  • Introductory unit testing habits with simple expected-output checks
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Unit 3

Collections and Algorithms

  • Lists, tuples, dictionaries, sets, nested structures, and data modeling
  • Traversal patterns, filtering, counting, frequency maps, and grouping
  • Linear search, binary search ideas, selection sort, insertion sort, and merge sort concepts
  • Runtime intuition, edge cases, empty data, duplicates, and off-by-one errors
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Unit 4

Object-Oriented Python

  • Classes, objects, instance variables, methods, constructors, and string representations
  • Encapsulation, composition, object relationships, and object collections
  • Designing small systems such as gradebooks, game objects, inventories, and simulations
  • Connecting Python OOP ideas to Java and AP Computer Science A vocabulary
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Unit 5

Files, APIs, and Automation

  • Reading and writing text files, CSV files, JSON data, and directory paths
  • Using requests for APIs and Beautiful Soup for introductory web data extraction
  • Automating repetitive tasks, report generation, simple dashboards, and data cleanup
  • Environment setup with pip, virtual environments, Jupyter notebooks, and Google Colab
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Unit 6

Data Science With Python

  • NumPy arrays, vectorized operations, broadcasting, descriptive statistics, and random data
  • pandas DataFrames, Series, indexing, filtering, grouping, joins, missing values, and reshaping
  • Data cleaning, feature creation, exploratory data analysis, and reproducible notebooks
  • Reading CSV, Excel, JSON, and web data into analysis workflows
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Unit 7

Visualization and Communication

  • Matplotlib charts, subplots, labels, legends, scales, and chart selection
  • Seaborn statistical visuals for distributions, categories, correlations, and heatmaps
  • Plotly interactive visuals and student-friendly dashboards when appropriate
  • Writing short findings, explaining trends, and presenting data without overclaiming
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Unit 8

AI/ML Readiness

  • scikit-learn workflows: train/test split, preprocessing, fitting, prediction, and evaluation
  • Classification, regression, clustering, model metrics, overfitting, and baseline models
  • Introductory SciPy and statsmodels concepts for students ready for deeper analysis
  • Responsible AI habits: bias, privacy, data quality, and explaining model limitations
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Data Science Stack

Python packages students can grow into

Core Python Tools

Jupyter NotebookGoogle Colabpipvenvpytest

Data Handling

NumPypandasopenpyxlcsvjson

Visualization

MatplotlibSeabornPlotly

Web and APIs

requestsBeautiful SoupFastAPI basics

Machine Learning

scikit-learnSciPystatsmodels

Project Extensions

StreamlitSQLAlchemySQLite

Projects and Pathways

Students learn by building, not just watching syntax

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Beginner Apps

Number games, quiz apps, grade calculators, flashcards, and interactive command-line programs.

Automation Scripts

File organizers, CSV cleanup tools, API data collectors, and report generators.

Data Analysis

Sports, finance, school, weather, or public data analysis with pandas and visualizations.

AI/ML Portfolio

Introductory prediction, classification, recommendation, or clustering projects using scikit-learn.

Who It Fits

Python paths by student level

Middle School

Creative coding, games, problem solving, and confidence with loops, functions, and lists.

High School

School support, AP Computer Science Principles-style projects, data analysis, automation, and preparation for Java/AP Computer Science A.

Advanced Students

Data science, algorithms, APIs, dashboards, machine learning, and portfolio projects.