Edwin Hubble examined the spectra of many galaxies, looking for the red (longer wavelengths) or blue (shorter wavelengths) shifts in the spectra, indicating relative motion. To his surprise, not only did all of the galaxies appear to be moving, but all were moving away from us, no matter the direction of the galaxy. In addition, he found most galaxies exhibited a redshift, and the redshift was larger the further it was from our galaxy.Distant Nature: Astronomy Exercises 2016 by Stephen Tuttle under license "Creative Commons Attribution Non-Commercial Share Alike".

Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).

Este libro está dirigido, principalmente, a Estudiantes y Docentes que quieren aprender a programar como forma de fortalecer sus capacidades cognoscitivas y así obtener un beneficio adicional de su computador para lograr un mejor provecho de sus estudios. Dada la orientación del libro respecto a programar para resolver problemas asociados a las Ciencias e Ingenierías, el requisito mínimo de matemáticas que hemos elegido para presentar el contenido del mismo se cubre, normalmente, en el tercer año del bachillerato. No obstante, el requisito no es obligatorio para leer el libro en su totalidad y adquirir los conocimientos de programación obviando el contenido matemático.

If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you?

In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to:

Search for text in a file or across multiple files
Create, update, move, and rename files and folders
Search the Web and download online content
Update and format data in Excel spreadsheets of any size
Split, merge, watermark, and encrypt PDFs
Send reminder emails and text notifications
Fill out online forms

Step-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.

Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in Automate the Boring Stuff with Python.

"A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you. There are many translations of the book available in different human languages.

Students are presented with information relating to stand alone Python programs, stdin, stdout, and command line arguments. This is a lab exercise. After completion students should be able to create executable Python programs which can accept input from stdin or command line arguments.

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.
An official course Web site and Wiki is maintained on OpenWetWare: 20.181 Computation for Biological Engineers.

A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in one and a half days (~ 10 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

This is a desktop application for visualization of data with convenient plotting functions. It is intended for educational/scientific use and teaching data analysis skills such as aggregation, merging data and getting statistics.
Though the program has a spreadsheet like interface, the focus is on data manipulation with very configurable 2D/3D plotting rather than data entry. Though basic cell editing and row/column changes are supported. The program is open source., licensed under the GPL.
This program is suitable for use by secondary school and third level students wishing to learn about data manipulation and advanced plotting.

This is a textbook for teaching data science using the Python programming language at a general first year undergraduate level.

A Python IF-ELSE activity - "The Dating Equation" - for CS0 students. Part of the CUNY CS04All project.

The tutorial that the "DjangoGirls" initiative is using for all of its workshops. It's a very beginner-friendly tutorial with introductions to the command line, Python, Django, HTML and CSS. No previous programming experience is required.

Once participants have finished the tutorial, they will have a small working web application: their own blog. The tutorial will show them how to put it online, so others will see their work.

The tutorial is available in English, French, Chinese and Ukrainian. "beta" versions of translations to other languages are also available. (The English version is considered the "original" and is usually the most maintained, complete and up-to-date one.)

A Python Functions activity - "Drawing with Turtle" - for CS0 students. Part of the CUNY CS04All project.

This book was written to accompany the Tufts University introductory engineering course, "Engineering in the Kitchen". The book covers foundational topics in the course, including circuit analysis, Python programming, microcontroller I/O using MicroPython, and computer networking.

Asking the question, “What is human-computer interaction - artificial intelligence? Students come to understand what artificial intelligence is in most everyday life, discussing the privacy, pros and cons of this topic and exploring with artificial intelligence activities online. This lesson plan includes using the Google Vision Kit to explore various pre-loaded facial recognition programs and advance programming students can access the Python code, manipulate the code and test the changes.

This course lays the foundation for data science education targeting psychological and brain science students. No previous coding experience is required. The students are introduced to basic concepts and tools for data analysis. The focus is on hands-on practice and enjoyable learning. The course uses python as the programming language, and Jupyter Notebooks as the development environment (our “home base”) for the examples, tutorials, and assignments. The course uses Jupyterlab Notebooks because they are both the industry standard and a nice way to load, visualize, and analyze data as well as describe our findings in one environment. The course teaches how to use git and GitHub.com to document changes and backup our work and, eventually, for use as a collaboration tool.