Some error occured while loading the Quick View. Please close the Quick View and try reloading the page.
This book is a practical guide introducing the MATLAB and Python programming languages and their capabilities. It provides real-world examples and exercises to demonstrate Python's practical applications in contrast to MATLAB's.
The book follows a sequential learning approach, starting with the coverage of mathematical concepts using MATLAB, and then transitioning to Python. This approach aims to bridge the gap in developing numerical programs in both languages, ensuring a smooth learning experience.
Additionally, the book covers Symbolic computation techniques required in various analytic mathematical models using both MATLAB and Python.
This book is ideal for mathematics students, researchers who need to incorporate mathematical programming in allied disciplines, data science specialists interested in MATLAB and Python numerical techniques.
The main highlights of the proposed work are:
• How to solve ordinary differential equations via MATLAB and Python
• Dealing with a variety of partial differential equations through MATLAB and Python
• Symbolic computation via MATLAB and Python
• Data visualization through MATLAB and Python in a comparative mode.
Dr. Mamta Kapoor is presently working as an Associate Professor - Research at the Marwadi University Research Center, Department of Mathematics, Faculty of Engineering & Technology, Marwadi University, Rajkot, 360003, Gujarat, India. She is a mathematician and researcher whose research interests include numerical approximation of linear and nonlinear partial differential equations, semi-analytical solutions of fractional partial differential equations, fractional calculus, fuzzy calculus, scientific computing, numerical analysis, and fluid mechanics. She has several publications in reputed national and international journals indexed in Scopus.
In addition to her significant research contributions, Dr. Kapoor is the author of Practical MATLAB and Python: A Comparative Approach to Problem-Solving to Code Like a Pro, a work that exemplifies her dedication to integrating theoretical mathematics with computational and applied problem-solving methodologies. Moreover, Dr. Kapoor has authored the books entitled The Data Science Interview Series – Volume 1: Python, Data Analysis & ML Foundations, The Data Science Interview Series – Volume 2: Advanced ML, Deep Learning & Practice, and Practical Pandas: Data Wrangling, Analysis, and Visualization. Her scholarly efforts consistently emphasize the practical implementation of mathematical concepts in modern engineering and scientific contexts.
Dr. Mamta Kapoor has also pursued several certifications in data science and related fields, demonstrating her keen interest in interdisciplinary applications of mathematics and modern computational techniques.
Dr. Geeta Arora earned his Ph.D. degree from IIT Roorkee, India in 2011. She is working as a Professor in the Department of Mathematics at Lovely Professional University, Punjab, India. She has over twelve years of teaching experience and published around 60 research papers in both international and national Journals. She has authored 15 book chapters in national and international publications. Also, she has written a book on Vedic Mathematics, SCILAB and Essential Statistics.
She has served as an editor in book published by Taylor Francis and is currently working with CRC Press Taylor Francis, Elsevier and IGI Global. Her area of research is the development of numerical methods and statistics.
She has received the research appreciation award in 2017 and 2019 from Lovely Professional University, Punjab, India. She has conducted several workshops on MATLAB and Vedic mathematics for students and faculty members within and outside the university campus. She has supervised eight Ph.D. scholars and is currently guiding six Ph.D. Scholars. She has published around 60 Scopus Indexed publication in International Journal of repute. She is serving as Editor, Associate Editor and reviewer in many International Journals.