In 2022, Stephen Lynch was named a National Teaching Fellow, which celebrates and recognises individuals who have made an outstanding impact on student outcomes and teaching in higher education. He won the award for his work in programming in the STEM subjects, research feeding into teaching, and widening participation (using experiential and object-based learning). Although educated as a pure mathematician, Stephen's many interests now include applied mathematics, cell biology, electrical engineering, computing, neural networks, nonlinear optics and binary oscillator computing, which he co-invented with a colleague. He has authored 2 international patents for inventions, 8 books, 4 book chapters, over 40 journal articles, and a few conference proceedings. Stephen is a Fellow of the Institute of Mathematics and Its Applications (FIMA) and a Senior Fellow of the Higher Education Academy (SFHEA). He is currently a Reader with MMU and was an Associate Lecturer with the Open University from 2008-2012. In 2010, Stephen volunteered as a STEM Ambassador, in 2012, he was awarded MMU Public Engagement Champion status, and in 2014 he became a Speaker for Schools. He runs national workshops on "Python for A-Level Mathematics and Beyond," and international workshops on "Python for Scientific Computing and TensorFlow for Artificial Intelligence." He has run workshops in China, Malaysia, Singapore, and the USA.

Section I. An Introduction to Python. 1. The IDLE Integrated Development Learning Environment. 2. Anaconda, Spyder and the Libraries NumPy, Matplotlib and SymPy. 3. Jupyter Notebooks and Google Colab. 4. Python for AS-Level (High School) Mathematics. 5. Python for A-Level (High School) Mathematics. Section II. Python for Scientific Computing. 6. Biology. 7. Chemistry. 8. Data Science. 9. Economics. 10. Engineering. 11. Fractals and Multifractals. 12. Image Processing. 13. Numerical Methods for Ordinary and Partial Differential Equations. 14. Physics. 15. Statistics. Section III. Artificial Intelligence. 16. Brain Inspired Computing. 17. Neural Networks and Neurodynamics. 18. TensorFlow and Keras. 19. Recurrent Neural Networks. 20. Convolutional Neural Networks, TensorBoard, and Further Reading. 21. Answers and Hints to Exercises.