university levels of education Scientific Python I — VIII
Scientific Python, Mathematics, Computing and Chemistry — handmade, no ads, no sign-ups. Every lecture is kept whole and shown whole: nothing is summarised, so what you see is the lecture as it was written. 201,396 words across 51 lectures, from Python as a mathematical laboratory to quantum chemistry and molecular simulation. Twenty-one ways through the same text, forty-six palettes on a white default with dark a click away, twelve layouts and colours you can set yourself. Every lecture arrived split across four messages and is stitched back into one here, with the part headings kept as sections so the structure stays visible. The closing lecture on computational chemistry is carried over from an earlier draft of the course, being the one subject the rebuild did not cover.
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Python and the Scientific Stack
Lectures 1 to 10. Introduction to Python, Variables, Numbers, Expressions, and Scientific Computing, Functions, Parameters, Return Values, Variable Scope, and Modular Programming, Introduction to NumPy, Arrays, Vectorized Computation, and Scientific Computing, Introduction to Matplotlib: Line Graphs, Scientific Visualization, and Publication-Quality Figures, Introduction to SciPy: Scientific Computing, Numerical Integration, Root Finding, and Optimization and 5 more, ending on Advanced Scientific Visualization: Matplotlib Fundamentals, Figure Design, and Publication-Quality Graphics. 29,602 words across 10 lectures.
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Numerical Mathematics
Lectures 11 to 15. Numerical Linear Algebra: Matrix Fundamentals, Matrix Operations, and Efficient Computation with NumPy, Ordinary Differential Equations (ODEs): Initial Value Problems, Euler's Method, and Numerical Integration, Partial Differential Equations (PDEs): Introduction, Heat Equation, and Finite Difference Fundamentals, Numerical Optimization: Introduction, Objective Functions, Gradients, and Gradient Descent and Scientific Data Analysis: Descriptive Statistics, Exploratory Data Analysis (EDA), and Statistical Thinking. 15,756 words across 5 lectures.
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Machine Learning and Computing
Lectures 16 to 20. Machine Learning for Scientific Computing: Introduction, Supervised Learning, Feature Engineering, and Model Training, Neural Networks and Deep Learning: Artificial Neurons, Perceptrons, Activation Functions, and Forward Propagation, Scientific Computing Projects: Project Planning, Software Engineering, Reproducible Research, and Scientific Workflows, High-Performance Scientific Computing: Parallel Programming, Vectorization, GPUs, and Computational Scaling and Quantum Computing for Scientists: Qubits, Superposition, Quantum Gates, and Quantum Circuits. 16,459 words across 5 lectures.
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Advanced Numerical Methods
Lectures 21 to 27. Advanced Numerical Linear Algebra: Matrix Factorizations, Eigenvalue Algorithms, Krylov Subspace Methods, and Sparse Computation, Computational Optimization II: Convex Optimization, Constrained Optimization, and Interior-Point Methods, Computational Geometry: Points, Lines, Polygons, Convex Hulls, and Geometric Algorithms, Scientific Data Engineering: Data Pipelines, Data Provenance, Storage Systems, and Reproducible Research, Advanced Scientific Software Engineering: Software Architecture, Project Organization, and Professional Python Development and 2 more, ending on Advanced Computational Linear Algebra: Sparse Matrices, Matrix Storage Formats, and Large-Scale Scientific Computation. 23,654 words across 7 lectures.
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Scientific Machine Learning
Lectures 28 to 35. Scientific Machine Learning: Introduction, Physics-Informed Machine Learning, Neural Networks for Scientific Computing, and Hybrid Numerical Methods, Computational Optimization and Optimal Control: Foundations of Optimization, Convex Analysis, and Numerical Optimization Algorithms, High-Performance Scientific Computing: Performance Analysis, Algorithmic Complexity, CPU Architecture, and Scientific Benchmarking, Computational Geometry and Scientific Visualization: Geometric Foundations, Coordinate Systems, Spatial Data Structures, and Mesh Generation, Scientific Software Engineering and Research Computing: Software Architecture, Modular Design, Version Control, and Reproducible Research and 3 more, ending on Advanced Scientific Machine Learning and Physics-Informed AI: Physics-Informed Neural Networks (PINNs), Differentiable Programming, and Scientific Deep Learning. 28,998 words across 8 lectures.
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Performance and Research Software
Lectures 36 to 44. Quantum Computing with Python: Qubits, Quantum States, Superposition, Linear Algebra, and the Mathematics of Quantum Information, Scientific Software Project Development with Python: Project Architecture, Package Design, Code Organization, and Professional Development Practices, High-Performance Python for Modern Hardware: CPU Architecture, Memory Hierarchy, Cache Optimization, and Performance Engineering, Advanced Numerical Methods and Scientific Algorithms: Nonlinear Systems, Root Finding, Advanced Iterative Methods, and Scientific Optimization, Computational Geometry and Scientific Visualization with Python: Geometric Algorithms, Coordinate Systems, Meshes, and Spatial Computation and 4 more, ending on Advanced Python Performance Engineering: Profiling, Benchmarking, Algorithm Analysis, and Performance Optimization. 33,717 words across 9 lectures.
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Capstone and Mastery
Lectures 45 to 50. Capstone Projects in Scientific Python: End-to-End Scientific Software Development, Project Planning, Architecture, and Research Workflows, Advanced Visualization and Scientific Communication with Python: Data Visualization, Publication-Quality Figures, Interactive Graphics, and Scientific Storytelling, Advanced Numerical Linear Algebra with Python: Matrix Theory, Matrix Factorizations, Numerical Stability, and High-Performance Computation, Advanced Optimization with Python: Mathematical Foundations, Unconstrained Optimization, Convex Analysis, and Numerical Algorithms, Advanced Scientific Machine Learning with Python: Scientific Neural Networks, Physics-Informed Learning, Scientific AI, and Computational Discovery and Scientific Python Masterclass: Integrating Mathematics, Chemistry, Physics, Artificial Intelligence, and Scientific Computing. 20,497 words across 6 lectures.
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Computational Chemistry
Lecture 51. Computational Chemistry with Python. 23,154 words across 1 lecture.
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The whole course
All 51 lectures, opened without a filter. 191,837 words, 79,051 blocks, 11 tables and 0 paired examples, with a glossary of 49 entries and a search index of 7,127 words. Twenty-one ways through the same text. Six read it — one lecture entire, an index of every lecture number down the left, a reader that hands you a single section at a time, a continuous scroll, an outline of the section headings, and a syllabus of the whole course. Eight take it apart — every Spanish line beside its English, the glossary lifted out of the tables, the tables on their own, flashcards Spanish side first, the practice questions, a drill that pulls a random example in either direction, and the objectives and closing summaries. Seven keep track — homework, cultural notes, an accent-insensitive search across every block, starred lines, a page of your own notes per lecture, a progress board, and a printable handout. Forty-six colour palettes on a white default with dark one click away, eight colour roles editable by hand, twelve page layouts from a narrow reading column through ruled notebook paper and two magazine columns to a monospaced terminal, seven typefaces with a separate face for the Spanish, and sliders for text size, leading and line length. The English can be hidden to read the Spanish cold, or the Spanish hidden to work back the other way. Progress, stars and notes autosave, with JSON export and import.
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