✦ Senior Coder · Python Programs · Level 2 · Ages 10–15+

Advanced Python Programming & AI Foundations

Build Advanced Software, Data & AI Skills for Tomorrow's Technology Careers

For students who have completed Python Foundations or have prior Python programming experience.

⏱ 55 Hrs + 5–8 Hrs Assessment

🔴 Online - Personalized Live Classes

💻 Python Programming Language

🎖 Certificate On Completion

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Course Overview

What Is This Course About?

Technology careers are evolving rapidly.

Modern developers do far more than write code. They build software systems, work with data, connect applications to APIs, collaborate using version control, develop intelligent applications and increasingly work alongside AI-powered tools.

Senior Coders Level 2 takes students from learning Python to thinking like software engineers, data professionals and AI developers.

Through project-based learning, students strengthen their Python expertise while exploring:

  • Advanced Python Programming
  • Object-Oriented Programming
  • Data Structures
  • Data Science
  • Statistics
  • Data Visualization
  • APIs
  • GUI Development
  • Image Processing
  • Artificial Intelligence
  • Machine Learning Foundations
  • Git & GitHub
  • Agile Development
  • Professional Software Development

Students don't simply complete coding exercises. They design, build, test, debug and improve increasingly sophisticated software projects.

Why Advanced Python?

Python is one of the most widely used programming languages across modern technology.

It provides a foundation for careers and further study in:
  • Software Engineering
  • Artificial Intelligence & Machine Learning
  • Data Science & Analytics
  • Computer Vision
  • Automation
  • Web & Cloud Applications
  • Robotics
  • Research & Scientific Computing
  • Data Engineering
  • Technology Product Development
The Level 2 Shift

From Programmer to Software Developer

Level 1 teaches students how to program.

Level 2 teaches them how to build software.

Students move beyond isolated programming exercises and begin working with the concepts used to design larger software systems.

What Makes This Different From a Traditional Advanced Python Course?

A traditional Python course may teach students increasingly difficult syntax.

Senior Coders Level 2 focuses on something bigger:

How is real software actually designed and built?

Students don't just learn:

"What is a class?"

They learn:

"Why would a software engineer use classes to design this application?"

They don't just learn:

"How does Pandas work?"

They learn:

"How can I use data to answer a real question and communicate what the data is telling me?"

They don't just learn:

"What is an API?"

They learn:

"How can my Python application connect to a real-world service and use live information?"

And they don't just learn:

"What is AI?"

They begin asking:

"How can data, models and intelligent systems be used to solve problems?"

They learn to:

  • Break complex problems into components
  • Design reusable code
  • Build multi-file applications
  • Model real-world systems using OOP
  • Work with structured data
  • Connect applications to APIs
  • Create graphical applications
  • Analyse real datasets
  • Visualize and communicate data
  • Process images programmatically
  • Work with Git and GitHub
  • Understand software development workflows
  • Plan and develop larger projects
  • Test, debug and improve software

By completing Senior Coders Level 2, students will be able to:

  • Apply statistics to real problems
  • Understand AI and ML foundations
  • Work with AI tools for data analysis
  • Develop a professional problem-solving mindset
  • Build a portfolio of advanced Python projects

The Level 2 Difference

Traditional Advanced Python
Learn advanced syntax Complete exercises Follow examples
Senior Coders Level 2
Understand Design Build Analyse Test Collaborate Use AI Evaluate Improve

Students develop both technical depth and engineering thinking.

AI-Powered Data Analysis & Decision Making

Learn to Work With AI — Not Just Learn About AI

AI is increasingly becoming part of the modern developer and data professional's toolkit. But simply asking AI to analyse data is not enough.

That is why Level 2 introduces students to AI-assisted data analysis after they have developed their own Python, statistics and data-analysis skills.

Students work with:

Julius AI

To explore datasets, generate AI-assisted analysis and investigate patterns.

ChatGPT

To question findings, explore alternative interpretations, explain statistical concepts and challenge conclusions.

NotebookLM

To organise learning resources, analyse course/project material and support evidence-based learning and revision.

The AI + Human Data Analysis Workflow

Students learn a modern analytical workflow:

1

Analyse

Use Python, Pandas, statistics and visualization to investigate the data.

2

Interpret

Develop their own conclusions based on the analysis.

3

Ask AI

Use AI tools to generate additional insights or alternative interpretations.

4

Compare

Compare the AI-generated findings with their own analysis.

5

Investigate

Identify agreements, differences and unexpected conclusions.

6

Verify

Return to the underlying data and calculations.

7

Decide

Determine which conclusions are actually supported by evidence.

8

Communicate

Present the final findings with reasoning and evidence.

AI suggests. Data decides. The student evaluates.

This is the core philosophy of AI integration in Level 2.

Why Is This Important?

The future of technology is not simply about knowing how to code.

Modern technology professionals increasingly need to combine:

Programming + Data + AI + Critical Thinking

Students therefore learn an important principle:

AI can help generate insights, but humans remain responsible for evaluating whether those insights are correct.

Projects

What Students Will Build

Learning is project-based, with projects becoming progressively more sophisticated.

Students may build:

Intelligent Desktop Applications

Create graphical applications using Tkinter with structured software architecture.

Data Analysis Dashboards

Work with datasets using Pandas and Matplotlib to uncover and communicate insights.

API-Powered Applications

Connect Python applications to live online services and work with JSON data.

Computer Vision Projects

Manipulate and process digital images using Python.

Object-Oriented Applications

Design larger programs using classes, objects, inheritance and reusable architecture.

Data-Driven Applications

Work with CSV files, structured information and real-world datasets.

AI-Inspired Applications

Explore the foundations of intelligent systems and machine learning.

Independent Capstone Project

Plan, design, develop, test, document and present a complete software project.

Curriculum

Curriculum: 55 Hours + 5–8 Hours Assessment

Build the software engineering, AI, data science, and professional development skills behind modern technology careers.

Students move beyond programming fundamentals and begin working with the same concepts, tools, workflows, and technologies used by software engineers, AI developers, data scientists, and technology teams. Through project-based learning, students develop advanced coding skills while building real-world applications, data-driven solutions, intelligent systems, and professional software projects.

1 Transitioning from Programmer to Software Developer Developer Mindset Challenge
  • Revisiting core Python concepts
  • Thinking like a software engineer
  • Computational thinking and problem decomposition
  • Understanding software architecture
  • Designing larger programs
  • Development workflows
  • Professional coding practices
  • Developer Mindset Challenge
2 Advanced Functions & Code Organization Utility Toolkit Project
  • Advanced function design
  • Parameters, return values, and scope
  • Modular programming
  • Reusable code architecture
  • Building utility libraries
  • Code readability and maintainability
  • Documentation fundamentals
  • Utility Toolkit Project
3 Advanced Data Structures Smart Data Organizer Project
  • Revisiting lists and dictionaries
  • Nested data structures
  • Complex data modelling
  • Real-world data representation
  • Choosing the right structure
  • Performance considerations
  • Data organization strategies
  • Smart Data Organizer Project
4 List, Dictionary & Set Comprehensions Data Transformation Challenge
  • Advanced list comprehensions
  • Dictionary comprehensions
  • Data filtering techniques
  • Data transformation workflows
  • Efficient coding patterns
  • Functional thinking
  • Real-world applications
  • Data Transformation Challenge
5 Lambda Functions & Functional Programming Data Processing Mini Project
  • Anonymous functions
  • Lambda expressions
  • Map, filter, and sorting techniques
  • Writing concise code
  • Functional programming concepts
  • Data processing pipelines
  • Advanced coding practices
  • Data Processing Mini Project
6 Error Handling & Software Reliability Fault-Tolerant Application Project
  • Understanding software failures
  • Exceptions and runtime errors
  • Custom exception handling
  • Debugging strategies
  • Building resilient applications
  • Professional troubleshooting
  • Fault-Tolerant Application Project
7 Regular Expressions (RegEx) Smart Form Validator Project
  • Pattern matching fundamentals
  • Searching and validating data
  • Extracting information from text
  • Data cleaning techniques
  • Input validation systems
  • Real-world automation examples
  • Building validation tools
  • Smart Form Validator Project
8 Object-Oriented Programming Foundations Digital User Management System
  • Why software engineers use OOP
  • Classes and objects
  • Attributes and methods
  • Object state and behaviour
  • Encapsulation concepts
  • Modelling real-world systems
  • Designing reusable software
  • Digital User Management System
9 Advanced Object-Oriented Programming OOP Game Engine Project
  • Constructors and initialization
  • Inheritance
  • Code reuse strategies
  • Polymorphism concepts
  • Building scalable applications
  • Software architecture thinking
  • Industry use cases
  • OOP Game Engine Project
10 Professional Project Development with OOP Interactive Game Project
  • Multi-file applications
  • Separating responsibilities
  • Designing software modules
  • Building maintainable systems
  • Team-style development
  • Refactoring techniques
  • Software scalability
  • Interactive Game Project
11 File Handling & Data Persistence Personal Data Vault Project
  • Advanced file operations
  • Managing application data
  • Structured data storage
  • Text and binary files
  • Data persistence concepts
  • Real-world software applications
  • Data lifecycle management
  • Personal Data Vault Project
12 CSV Data Processing Student Analytics Project
  • Working with CSV datasets
  • Reading and writing structured data
  • Data import and export workflows
  • Processing tabular information
  • Dataset preparation
  • Data cleaning concepts
  • Business data applications
  • Student Analytics Project
13 Data Analysis with Pandas Data Explorer Project
  • Introduction to Pandas
  • DataFrames and Series
  • Data exploration
  • Data filtering and sorting
  • Aggregation techniques
  • Statistical insights
  • Working with real datasets
  • Data Explorer Project
14 Data Visualization with Matplotlib Data Dashboard Project
  • Understanding data storytelling
  • Creating professional charts
  • Bar graphs and histograms
  • Data interpretation
  • Visual communication
  • Dashboard thinking
  • Business analytics concepts
  • Data Dashboard Project
15 Statistics for Data Science Statistics Challenge Project
  • Understanding data trends
  • Mean, median, mode
  • Data distributions
  • Correlation concepts
  • Data-driven decision making
  • Real-world analytics examples
  • Foundations of predictive thinking
  • Statistics Challenge Project
16 GUI Development with Tkinter Desktop Utility Project
  • Introduction to graphical applications
  • Windows and widgets
  • Labels, buttons, and user inputs
  • Event-driven programming
  • User interface design
  • Interactive applications
  • GUI architecture
  • Desktop Utility Project
17 Advanced GUI Development Productivity App Builder
  • Multi-window applications
  • Layout management
  • Form design
  • User experience principles
  • Validation systems
  • Data-driven interfaces
  • Application workflows
  • Productivity App Builder
18 APIs & Connected Applications Live Data Application Project
  • Understanding APIs
  • Request-response architecture
  • Working with online services
  • API authentication concepts
  • Consuming live data
  • JSON fundamentals
  • Real-world technology integration
  • Live Data Application Project
19 Building Data-Driven Applications Smart Information Dashboard
  • Combining APIs and Python
  • Data collection pipelines
  • Processing live information
  • Automation concepts
  • Information systems
  • Real-time applications
  • Software integration
  • Smart Information Dashboard
20 Image Processing Foundations Image Studio Project
  • Introduction to computer vision concepts
  • Working with digital images
  • Image formats and representations
  • Understanding pixels
  • Introduction to Pillow (PIL)
  • Image transformations
  • Practical image workflows
  • Image Studio Project
21 Advanced Image Processing AI Image Processing Project
  • Resizing and cropping
  • Image enhancement
  • Filters and effects
  • Color manipulation
  • Batch image processing
  • Automation with images
  • Foundations of computer vision
  • AI Image Processing Project
22 Introduction to Artificial Intelligence AI Explorer Project
  • What is AI?
  • AI in everyday technology
  • Understanding intelligent systems
  • Pattern recognition concepts
  • Data and decision making
  • AI ethics and responsible technology
  • AI career pathways
  • AI Explorer Project
23 Machine Learning Foundations Predictive Analytics Challenge
  • What is machine learning?
  • Supervised vs unsupervised learning
  • Training data concepts
  • Features and predictions
  • Understanding models
  • Real-world AI examples
  • Data-driven intelligence
  • Predictive Analytics Challenge
24 Software Collaboration with Git & GitHub Git Collaboration Project
  • Introduction to version control
  • Why developers use Git
  • Git workflow fundamentals
  • Commits and repositories
  • Branching concepts
  • Managing project history
  • Team collaboration
  • Git Collaboration Project
25 Agile Software Development Agile Project Simulation
  • Understanding software teams
  • Agile development principles
  • Scrum and Kanban concepts
  • Managing software projects
  • Iterative development
  • Team workflows
  • Product thinking
  • Agile Project Simulation
26 Capstone Project Planning Capstone Design Proposal
  • Defining project objectives
  • Project architecture
  • User requirements
  • Feature planning
  • Development roadmaps
  • Team-style project planning
  • Professional project documentation
  • Capstone Design Proposal
27 Capstone Project Development I Portfolio Project Build
  • Application development
  • Building core functionality
  • Testing and debugging
  • Code reviews
  • Improving user experience
  • Feature implementation
  • Project management
  • Portfolio Project Build
28 Capstone Project Development II Portfolio Project Completion
  • Advanced feature integration
  • Data handling
  • API integration
  • Visualization components
  • Application refinement
  • Optimization strategies
  • Final testing
  • Portfolio Project Completion
29 AI-Assisted Data Analysis & Insight Evaluation Mini Project: AI Data Analyst Challenge
Learning Focus

Using AI as a Data Analysis Partner — While Keeping Human Reasoning at the Centre

  • Introduction to AI-assisted data analysis
  • How AI is changing modern data workflows
  • AI as a data-analysis partner
  • Julius AI for dataset exploration
  • ChatGPT for questioning and interpreting findings
  • NotebookLM for organising learning resources and evidence
  • Comparing human and AI-generated insights
  • AI-generated patterns and hypotheses
  • Asking effective analytical questions
  • Evaluating AI-generated conclusions
  • Identifying unsupported assumptions
  • Checking AI-generated insights against the underlying data
  • Statistical reasoning and AI
  • Data visualization and AI interpretation
  • AI limitations and potential errors
  • Responsible use of AI in data analysis
  • Human-in-the-loop decision making
Mini Project: AI Data Analyst Challenge
Data Analysis AI Assistance Verification Decision
A Assessment & Portfolio Evaluation (5–8 Hours) Software Engineer & AI Developer Challenge

Students independently design, develop, test, document, and present a complete software project demonstrating the skills acquired throughout the course.

Projects may incorporate:
  • Object-Oriented Programming
  • GUI Development
  • Data Analysis
  • Data Visualization
  • APIs
  • Image Processing
  • Git Version Control
  • AI-Inspired Features
  • Software Engineering Practices
Portfolio Project Examples
  • Personal Finance Dashboard
  • AI-Powered Study Assistant
  • Data Analytics Platform
  • Weather Intelligence App
  • Image Processing Toolkit
  • Smart Inventory Manager
  • Student Performance Analytics System
  • Habit & Productivity Tracker
  • API-Powered Information Hub
  • AI-Inspired Recommendation System

Outcome

Students complete the course with advanced Python programming expertise, practical software engineering experience, exposure to AI and data science concepts, collaborative development skills, and a portfolio of sophisticated projects that prepare them for future pathways in:

  • Artificial Intelligence
  • Machine Learning
  • Software Engineering
  • Data Science
  • Full-Stack Development
  • Computer Vision
  • Automation
  • Technology Entrepreneurship
Career Pathways

Career Connections

The skills developed in Level 2 connect to multiple technology pathways.

Software Engineering

Students develop experience with OOP, modular design, debugging, Git, APIs, project planning and software architecture.

Data Science

Students learn to work with real datasets, analyse information, use statistics and communicate insights through visualization.

Artificial Intelligence

Students develop foundational understanding of data, machine learning, pattern recognition and intelligent systems.

Machine Learning

Python, data preparation, statistics and predictive thinking provide foundations for later machine learning studies.

Computer Vision

Image processing introduces students to the programming concepts behind computer vision applications.

Automation

Python, APIs, data processing and structured programming can be applied to automate repetitive processes.

Full-Stack Development

API integration, software architecture and Python programming provide useful foundations for later full-stack development.

Robotics & Intelligent Systems

Python programming, automation, data and AI concepts can support future robotics and intelligent-system pathways.

Career-Ready Learning Starts Here

Students are not expected to choose a technology career at this age. Instead, Level 2 gives them exposure to the shared foundations behind many technology careers.

These are transferable skills that can continue into higher-level Computer Science, AI, Data Science and Software Engineering studies.

Your Learning Journey

Where Level 2 Takes Students Next

Senior Coders Level 2 sits at the bridge between programming foundations and specialised technology development.

  1. Level 1

    Python Programming Foundations

    Learn to program and develop computational thinking.

  2. You are here Level 2

    Advanced Python Programming & AI Foundations

    Build software, work with data, understand AI and develop professional development skills.

  3. Level 3

    AI-Powered Software Development with Python

    Progress into advanced AI, modern AI development and intelligent software systems.

THE ITSMYBOT ADVANTAGE

Why Parents Love Our Programs

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Highly selected instructors focused on creativity and engagement.

Personalized Live Classes

Personalized live classes tailored to each child's learning pace.

Progress Reports & Parent Updates

Progress reports and regular parent updates after sessions.

Flexible Scheduling Options

Flexible scheduling options including evenings and weekends.

Project-Based Learning

Project-based learning designed to keep children motivated and confident.

Safe & Encouraging Learning Environment

Safe, encouraging learning environment that supports creativity and innovation.

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What you get
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Enrol and pay one month at a time. No annual lock-ins, no large upfront amounts. Your investment stays proportional to the value your child receives.

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Life happens. If your needs change after any month, you have complete freedom to adjust. We'd love for you to stay - but we'll never make you feel stuck.

Why don't we show fixed prices? Because we don't believe in one-size-fits-all. Different teachers bring different expertise, teaching methods, and experience - and that's reflected in how we price. The best way to find your fit is to take the free trial lesson first, meet your instructor, and then decide. Pricing is shared after the trial - transparently, with no pressure.

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How it works

3 Easy Steps to Get Started:

1

Book a free trial

Fill in the form. An academic counsellor will schedule a free session and assess your child's current level and interests.

2

Get a personalised roadmap

Based on the trial, counsellors build a custom learning path — right pace, right projects, right instructor for your child.

3

Build, learn, and earn

Live sessions, progress reports after every class, parent feedback calls, and a certificate on completion. Your child keeps all their projects.

FAQs

Common Questions From Parents

It's Senior Coder Level 2, for ages 10–15+. Across 55 live hours plus 5–8 hours of assessment, your child uses the tools of real software engineers, data professionals and AI developers — advanced Python, OOP, data science, GUIs, image processing, AI foundations, and AI-assisted data analysis using tools like Julius AI, ChatGPT and NotebookLM.

Ideally, yes. This course is built for students who've completed Python Foundations or already have prior Python experience, since it builds directly on those skills.

Your child works with real developer tools — Pandas, Matplotlib, Tkinter, Pillow, Git, and GitHub — across 29 hands-on sessions. In the course's AI-assisted data analysis session, they also learn to work with Julius AI, ChatGPT, and NotebookLM — not to replace their own analysis, but to question it, compare results, and evaluate whether AI-generated conclusions are actually supported by the data.

Your child creates intelligent desktop applications, data analysis dashboards, API-powered applications, computer vision projects, object-oriented applications, data-driven applications, AI-inspired applications, and an independent capstone project.

Yes — your child explores AI and ML foundations including pattern recognition, supervised and unsupervised learning, and computer vision, then goes further: in a dedicated AI-assisted data analysis session, they learn to use AI tools like Julius AI and ChatGPT to generate insights, then question, verify, and decide whether those insights actually hold up against the real data — before ever accepting an AI-generated conclusion.

Object-Oriented Programming, version control with Git and GitHub, data analysis, error handling, and Agile development workflows — genuine professional practices.

It's 55 hours of live instruction plus 5–8 hours of assessment, across 29 structured sessions ending in a full capstone project.

Yes — 100% live and one-on-one with a dedicated instructor. Your child learns in real time, asks questions freely, and follows a flexible schedule that fits around school and family. Nothing is pre-recorded.

Yes! Your child earns a Certificate of Completion along with a portfolio of sophisticated software projects.

This course opens pathways into Artificial Intelligence, Machine Learning, Software Engineering, Data Science, Full-Stack Development, Computer Vision, Automation, and Technology Entrepreneurship.

Yes. Book a free trial, and a counsellor will check your child's Python level and recommend the right starting point.

From Writing Code to Building Technology

Senior Coders Level 2 is designed for students ready to move beyond beginner programming.

They don't just learn more Python.

They learn to:

  • Build software.
  • Work with data.
  • Understand AI.
  • Use professional tools.
  • Question results.
  • Solve complex problems.
  • And think like a technology professional.

Advanced Python. Real Data. AI-Aware Thinking.

The next step in the Senior Coders journey.

Book a Free Trial Class

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Ready To Start Building Real Software With Python & AI?

Senior Coders Level 2 takes students from learning Python to thinking like software engineers, data professionals and AI developers.

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