In GEOG 489, you will learn advanced applications of Python for developing and customizing GIS software, designing user interfaces, solving complex geoprocessing tasks, and leveraging open source. The course consists of readings, walkthroughs, projects, quizzes, and discussions about advanced GIS programming concepts and techniques, and a final term project. It complements the material covered in GEOG 485: GIS Programming and Customization. Software covered in the course includes: Esri ArcGIS Pro/arcpy, Jupyter Notebook, Esri ArcGIS API for Python, QGIS, GDAL/OGR. Students will also use of the Git version control software for code management, and learn techniques for distributing Python applications to end users.

Bill Gates is credited with saying he would \hire a lazy person to do a difficult job\" with the justification that \"a lazy person will find an easy way to do it.\" GEOG 485 doesn't teach the lazy way to get the job done, but it does teach the scripting way _ which is arguably even better. You've probably heard the \"give a fish\"/\"teach to fish\" saying? That's the gist of GEOG 485: to equip you, in an ArcGIS context, with the ModelBuilder and Python scripting skills to make your boring, repetitive geoprocessing tasks easier, quicker and automatic _ so you can focus on the more interesting (potentially more valuable) work that you (and your employers) really want you to be doing."

This course will provide a gentle, yet intense, introduction to programming using Python for highly motivated students with little or no prior experience in programming. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language.
The course is designed to help prepare students for 6.01 Introduction to EECS I. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered.
This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

This course will provide a gentle introduction to programming using Python™ for highly motivated students with little or no prior experience in programming computers. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive featuring in-class exercises with lots of support from the course staff.
This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

A Python Lists activity - "Gift Exchange" - for CS0 students. Part of the CUNY CS04All project.

A Python Lists activity - "Hangman game" - for CS0 students. Part of the CUNY CS04All projec

Python is a fun and extremely easy-to-use programming language that has steadily gained in popularity over the last few years. Developed over ten years ago by Guido van Rossum, Python's simple syntax and overall feel is largely derived from ABC, a teaching language that was developed in the 1980's. However, Python was also created to solve real problems and it borrows a wide variety of features from programming languages such as C++, Java, Modula-3, and Scheme. Because of this, one of Python's most remarkable features is its broad appeal to professional software developers, scientists, researchers, artists, and educators. 278 page pdf file.

This interactive book is a product of the Runestone Interactive Project at Luther College, led by Brad Miller and David Ranum. There have been many contributors to the project. Our thanks especially to the following:



This book is based on the Original work by: Jeffrey Elkner, Allen B. Downey, and Chris Meyers

Activecode based on Skulpt

Codelens based on Online Python Tutor

Many contributions from the CSLearning4U research group at Georgia Tech.

ACM-SIGCSE for the special projects grant that funded our student Isaac Dontje Lindell for the summer of 2013.

NSF

The Runestone Interactive tools are open source and we encourage you to contact us, or grab a copy from GitHub if you would like to use them to write your own resources.

This lesson shows how to use Python and skimage to do basic image processing. With support from an NSF iUSE grant, Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA have developed a curriculum for teaching image processing in Python. This lesson is currently being piloted at different institutions. This pilot phase will be followed by a clean-up phase to incorporate suggestions and feedback from the pilots into the lessons and to make the lessons teachable by the broader community. Development for these lessons has been supported by a grant from the Sloan Foundation.

This course explores audio synthesis, musical structure, human computer interaction (HCI), and visual presentation for the creation of interactive musical experiences. Topics include audio synthesis; mixing and looping; MIDI sequencing; generative composition; motion sensors; music games; and graphics for UI, visualization, and aesthetics. Weekly programming assignments in python are included. Student teams build an original, dynamic, and engaging interactive music system for their final project.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and 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.

This introduction to computer programming with Python begins with some of the basics of computing and programming before diving into the fundamental elements and building blocks of computer programs in Python language. From the installation of Python, Python interactive programming, and integrated development environments to raising and handling exceptions, using compound data types to solve problems, and implement divide-and-conquer processes using functions, classes and modules, this textbook will set students up for success in programming and computing study and practice. The included exercises and projects are designed to hone students’ skills.

This open text in progress is designed for use in CS 210 at University of Oregon and in CS 161 at other Oregon colleges and universities.

This workshop aims to help students and teachers of Humanities and Social Science learn the basics of text-mining using Python. It is meant as an introduction to the use of computational techniques for analysing data for Humanists and Social Scientists. It contains a "Jupyter Notebook", which is basically a website where students will be taught how to write and execute code that will help them solve research problems that Humanists and Social scientists face. Additionally, this lesson also contains a video that demonstrates how to use that website. The total expected time to use this resouce is around 2 hours. 

In this lesson, you’ll learn how to use python with the Zotero API to interact with your Zotero library. The Zotero API is a powerful interface that would allow you to build a complete Zotero client from scratch if you so desired. But like most APIs, it works in small, discrete steps, so we have to build our way up to the complicated requests we might want to use to access our Zotero libraries. But this incremental building gives us plenty of time to learn as we go along.

This curriculum was designed for high school students with no prior coding experience who are interested in learning Python programming for data science. However, this course material would be useful for anyone interested in teaching or learning basic programming for data analysis.

The curriculum features short lessons to deliver course material in “bite sized” chunks, followed by practices to solidify the learners' understanding. Pre-recorded videos of lessons enable effective virtual learning and flipped classroom approaches.

The learning objectives of this curriculum are:

1. Write code in Python with correct syntax and following best practices.
2. Implement fundamental programming concepts when presented with a programmatic problem set.
3. Apply data analysis to real world data to answer scientific questions.
4. Create informative summary statistics and data visualizations in Python.
5. These skills provide a solid foundation for basic data analysis in Python. Participation in our program exposes students to the many ways coding and data science can be impactful across many disciplines.

Our curriculum design consists of 27 lessons broken up into 5 modules that cover Jupyter notebook setup, Python coding fundamentals, use of essential data science packages including pandas and numpy, basic statistical analyses, and plotting using seaborn and matplotlib. Each lesson consists of a lesson notebook, used for teaching the concept via live coding, and a practice notebook containing similar exercises for the student to complete on their own following the lesson. Each lesson builds on those before it, beginning with relevant content reminders from the previous lessons and ending with a concise summary of the skills presented within.

Students digitize the path of the Green River from historical imagery, and calculate sinuosity using a Python script. Students then display the changes on a map. Students are introduced to the concept of computer scripting.

This is a seven-problem set to use to practise Python Programming Language basics by solving problems. This set has been used at Izmir Fen Lisesi (A Science High School) since 2019.