Books  Physical Sciences  Mathematics 

Applying Maths in the Chemical and Biomolecular Sciences: An example-based approach

By: Godfrey Beddard

512 pages, 456 figures

Oxford University Press

Paperback | Sep 2009 | #170245 | ISBN-13: 9780199230914
Availability: Usually dispatched within 6 days Details
NHBS Price: £40.99 $52/€46 approx

About this book

Increasingly, chemistry departments are offering introductory maths courses to give students the maths skills they need to be competent. Beyond these introductory courses, however, students need to understand how maths and numerical skills are used in an applied way, by practising chemists. Therefore, many chemistry departments (and particularly those based at more research-active institutions) offer a second, more advanced maths course, which focuses not on the maths principles, but on the use of maths by chemists in the 'real world'.

Chemistry is an increasingly interdisciplinary subject - with strands running into the biosciences and physics (biophysical chemistry; biochemistry for example). This book will appeal not just to 'straight' chemistry students, but also biophysical chemistry and biochemistry students, whose degree contents will tend to be the same as that of mainstream chemists in certain areas.

It is particularly useful for Scientists applying mathematical techniques to their analyses. However, professionals in other fields (e.g. Economics and Finance) will also find the mathematical techniques highly useful and relevant. I myself am an Economist, and found the mathematical content very good, and the scientific applications very illuminating. reviewer on Very well written and the perfect resource for self study. The Higher Education Academy Physical Sciences Centre


1. Numbers, basic functions and algorithms; 2. Complex numbers; 3. Differentiation; 4. Integration; 5. Vectors; 6. Matrices and determinants; 7. Matrices in quantum mechanics; 8. Summations, series and expansion of functions; 9. Fourier series and transforms; 10. Differential equations; 11. Numerical methods; 12. Monte-carlo methods; 13. Statistics and data analysis

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