Transparent Pricing
Most coding academies ask you to pay upfront for months - before your child has even had a single class. At ItsMyBot, we think that's backwards. You pay month to month, and you're always in control.
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.
Pricing varies based on the teacher, teaching style, and course chosen - because every learner is different. We'll tell you the exact cost after your free trial class, when you know what fits best.
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.
Start for free - no card required
Book Your Free Trial LessonNo commitment. No pressure. Pricing shared after the trial lesson.
Session 1: Transitioning from Programmer to Software Developer
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
Session 2: Advanced Functions & Code Organization
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
Session 3: Advanced Data Structures
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
Session 4: List, Dictionary & Set Comprehensions
Advanced list comprehensions
Dictionary comprehensions
Data filtering techniques
Data transformation workflows
Efficient coding patterns
Functional thinking
Real-world applications
Data Transformation Challenge
Session 5: Lambda Functions & Functional Programming
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
Session 6: Error Handling & Software Reliability
Understanding software failures
Exceptions and runtime errors
Custom exception handling
Debugging strategies
Building resilient applications
Professional troubleshooting
Fault-Tolerant Application Project
Session 7: Regular Expressions (RegEx)
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
Session 8: Object-Oriented Programming Foundations
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
Session 9: Advanced Object-Oriented Programming
Constructors and initialization
Inheritance
Code reuse strategies
Polymorphism concepts
Building scalable applications
Software architecture thinking
Industry use cases
OOP Game Engine Project
Session 10: Professional Project Development with OOP
Multi-file applications
Separating responsibilities
Designing software modules
Building maintainable systems
Team-style development
Refactoring techniques
Software scalability
Interactive Game Project
Session 11: File Handling & Data Persistence
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
Session 12: CSV Data Processing
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
Session 13: Data Analysis with Pandas
Introduction to Pandas
DataFrames and Series
Data exploration
Data filtering and sorting
Aggregation techniques
Statistical insights
Working with real datasets
Data Explorer Project
Session 14: Data Visualization with Matplotlib
Understanding data storytelling
Creating professional charts
Bar graphs and histograms
Data interpretation
Visual communication
Dashboard thinking
Business analytics concepts
Data Dashboard Project
Session 15: Statistics for Data Science
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
Session 16: GUI Development with Tkinter
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
Session 17: Advanced GUI Development
Multi-window applications
Layout management
Form design
User experience principles
Validation systems
Data-driven interfaces
Application workflows
Productivity App Builder
Session 18: APIs & Connected Applications
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
Session 19: Building Data-Driven Applications
Combining APIs and Python
Data collection pipelines
Processing live information
Automation concepts
Information systems
Real-time applications
Software integration
Smart Information Dashboard
Session 20: Image Processing Foundations
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
Session 21: Advanced Image Processing
Resizing and cropping
Image enhancement
Filters and effects
Color manipulation
Batch image processing
Automation with images
Foundations of computer vision
AI Image Processing Project
Session 22: Introduction to Artificial Intelligence
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
Session 23: Machine Learning Foundations
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
Session 24: Software Collaboration with Git & GitHub
Introduction to version control
Why developers use Git
Git workflow fundamentals
Commits and repositories
Branching concepts
Managing project history
Team collaboration
Git Collaboration Project
Session 25: Agile Software Development
Understanding software teams
Agile development principles
Scrum and Kanban concepts
Managing software projects
Iterative development
Team workflows
Product thinking
Agile Project Simulation
Session 26: Capstone Project Planning
Defining project objectives
Project architecture
User requirements
Feature planning
Development roadmaps
Team-style project planning
Professional project documentation
Capstone Design Proposal
Session 27: Capstone Project Development I
Application development
Building core functionality
Testing and debugging
Code reviews
Improving user experience
Feature implementation
Project management
Portfolio Project Build
Session 28: Capstone Project Development II
Advanced feature integration
Data handling
API integration
Visualization components
Application refinement
Optimization strategies
Final testing
Portfolio Project Completion
Software Engineer & AI Developer Challenge
Students independently design, develop, test, document, and present a complete software project demonstrating the skills acquired throughout the course.
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
This course is for students who have completed Python Foundations or have prior Python experience.
Students explore Object-Oriented Programming, Data Science, Artificial Intelligence, Machine Learning, Image Processing, Software Development Workflows, GUI Development, and Professional Coding Practices.
Students work with Python, Pandas, Matplotlib, Tkinter, Pillow (PIL), Git, and GitHub across the 28 sessions.
Yes. Students build real-world projects throughout the course and complete an independent capstone project — planning, designing, building, testing, and presenting a complete software project from start to finish.
Yes. Classes are online, personalised, and live.
Yes. Students receive a certificate on completion.
Students complete the course with a portfolio of sophisticated projects and a foundation for future pathways in: Artificial Intelligence, Machine Learning, Software Engineering, Data Science, Full-Stack Development, Computer Vision, Automation, and Technology Entrepreneurship.
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