Modules
Open a module to see its topics and everything in it.
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Module 1
Python Environment Setup and Language Fundamentals
Introduces the Python programming environment including IDEs, Jupyter notebooks, and package management, then covers core language fundamentals: data types, variables, operators, type conversion, and the distinction between interactive and script execution.
4 items -
Module 2
Control Flow and Python Data Structures
Covers conditional statements, for and while loops, break and continue, and the range() function, then introduces Python's core data structures — lists, tuples, dictionaries, and sets — with guidance on selecting the right structure for engineering problems.
15 items -
Module 3
Functions, Comprehensions, and Functional Patterns
Covers function definition, positional and keyword parameters, default values, *args/**kwargs, scope, and recursion, then introduces list, dict, and set comprehensions alongside lambda functions and the map/filter functional programming patterns.
17 items -
Module 4
Modules, File I/O, and Error Handling
Covers writing and importing custom modules, using standard library modules, reading and writing text and CSV files with the with-statement context manager, working with JSON, and applying try-except patterns for graceful error handling in engineering scripts.
13 items -
Module 5
NumPy for Engineering Numerical Computation
Introduces NumPy arrays including creation, element-wise operations, broadcasting, indexing, slicing, reshape, and transpose, with emphasis on linear algebra, statistics, and mathematical functions as the foundation for all subsequent engineering computation.
15 items -
Module 6
Data Visualization with Matplotlib and Intro to Pandas
Covers matplotlib's pyplot interface for producing line plots, scatter plots, bar charts, histograms, subplots, and publication-quality figures, then introduces pandas Series and DataFrames for reading, filtering, sorting, and performing descriptive statistics on engineering datasets.
9 items -
Module 7
Object-Oriented Programming and AI-Assisted Coding Workflow
Introduces classes, instances, attributes, methods, and inheritance for modeling engineering objects such as beams and sensors, and establishes the AI-assisted programming workflow using GitHub Copilot and ChatGPT for code generation, debugging, and responsible verification.
8 items -
Module 8
Numerical Methods with SciPy and Curve Fitting
Applies SciPy for root finding, numerical integration, and ODE solving with solve_ivp, then covers linear and nonlinear curve fitting using numpy.polyfit and scipy.optimize.curve_fit with residuals analysis on real engineering datasets.
9 items -
Module 9
Engineering Data Analysis and Simulation
Covers the full engineering data workflow — loading, cleaning, and visualizing CSV and time-series data using pandas, NumPy, and matplotlib — then implements time-stepping simulation models for mechanical, electrical, and thermal systems, comparing results with analytical solutions.
9 items -
Module 10
Machine Learning, Automation, Version Control, and Responsible AI
Introduces supervised machine learning with scikit-learn for engineering regression and classification, covers engineering automation and parameter sweep pipelines, establishes Git and GitHub version control practices, and consolidates responsible AI integration including verification of AI-generated code and professional engineering accountability.
6 items