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This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

[ Reading] After Work Introduction To Python Programming

[ Reading] After Work Introduction To Python Programming

Python. An Introduction To Programming ( Chapter 10 Basic Algorithms)

Python. An Introduction To Programming ( Chapter 9 Multimedia)

Python. An Introduction To Programming ( Chapter 9 Multimedia)

Instructor: Ana Bell View the complete course: https://ocw.mit.edu/courses/6-100l-introduction-to-cs-and-programming-using-python-fall-2022/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62A-ynp6v6-LGBCzeH3VAQB This subject is aimed at students with little to no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, feel justifiably confident in their ability to write simple programs that allow them to accomplish useful goals. The class will use the Python 3 programming language. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments .

Python. An Introduction To Programming ( Chapter 7 Graphics)

PDF BOOK

PDF BOOK

Python. An Introduction To Programming ( Chapter 12 How To Write Good Programs)

Python. An Introduction To Programming ( Chapter 6 Classes)

Python. An Introduction To Programming ( Chapter 6 Classes)

This video will give you an introduction to programming, Python, and IDE. For programming, this video will cover "What is computer programming? ",  "My opinion on the best and most efficient way to learn computer programming", "In my opinion things you should learn if you want to be a good programmer", and "My opinion on what you should do when you are overwhelmed by a programming problem". For Python, this  video will cover "What is Python?", "What is Python good for?", and "What application domains is Python used in?". For IDE, this video will cover "What IDE stands for?" and "What IDE is used for?". Video Chapters: 0:00 Intro 0:09 What is computer programming? 0:19 Best way to learn programming 0:42 What to learn to be a good programmer? 0:51 How to solve programming problems. 1:36 What to do if you are stuck? 2:22 What is Python? 2:46 What is Python good for? 3:06 What application domains is Python used in? 3:24 What does IDE stand for? 3:57 What IDE is used for? 4:02 My thoughts on IDE 4:20 Outro ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- Sources used in the video: python.org/doc/essays/blurb/ python.org/about/apps/ docs.python.org/3/ google.com ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- Support me on Patreon:  patreon.com/LittleWildGrass Follow me on Reddit:  reddit.com/user/LittleWildGrass

Bethany Poulin https://www.pyohio.org/2019/presentations/139 ## What is Linear Programming Linear programming is an optimization method with broad utility. Sadly the study of linear programming is often overlooked in favor of sexier machine learning algorithms by both practitioners and data science educators (both bootcamps and graduate programs). However, it is used extensively in many of the fields from which data science evolved and there are useful libraries in most of the major analytical languages. We will spend 5-10 minutes understanding linear programming in general before moving on to code ## Linear Programming in Python In Python, the most common library is Pulp which we will use to look at two separate optimization problems, one to minimize and one to maximize a desired outcome variable. We will go through: System requirements Package Installation Computational Complexity (floating values & discrete number calculations) ## Real World Examples with Code We will be setting up and completing the two problems. In doing so we will learn how to simplify a complex system into a series of linear equations. We will learn about: * Decision Variables - the outcomes we choose to optimize * Objective Function - the series of relationships between decision variables which affect the optimization * Constraints - the limits on our decision variables which allocate resources * How to visualize the problem (it could be a graph or simple illustration) * How to set up the equations defining the problem * How to solve the series of equations * How to interpret the results * How to know when you can, should or should not use linear programming to solve a problem The presentation will be assume a base understanding of python algebra and linear equations (y=mx+b) but it is not going to be presented in a mathematically demanding way. We will look at real-world resource allocation problems and use simple python code to solve them. ### Resources There will be a fully fleshed out GITHUB repository with: * The slides * The code * Brief instructional README.md with links to other useful online sources Linear programming is a useful computational technique for finding minima or maxima of a complex system by breaking it into a series of linear equations which describe the systems. It has many practical applications in industry, medicine, agriculture and in retail environments. Together we will explore simple linear programming problems using Python and the module Pulp. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sat Jul 27 14:00:00 2019 at Cartoon 2

Python An Introduction To Programming Chapter 6 Classes

Python An Introduction To Programming( Chapter 7 Introduction To Graphics Programming)

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

Python An Introduction To Programming ( Chapter 9 Multimedia)

Python An Introduction To Programming ( Chapter 9 Multimedia)

Mathematics; Computer mathematics; Numerical analysis; Computer software; Numerical analysis

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.

Numerical simulations; programming; Python

Python. An Introduction To Programming

Python. An Introduction To Programming

MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016 View the complete course: http://ocw.mit.edu/6-0001F16 Instructor: Dr. Ana Bell 6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language. *** Correction for Lecture_02_exercise_04: The right answer should be print "You got out of the Lost Forest!" *** License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu