All Projects → swcarpentry → Python Novice Inflammation

swcarpentry / Python Novice Inflammation

Licence: other
Programming with Python

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Python Novice Inflammation

R Novice Gapminder
R for Reproducible Scientific Analysis
Stars: ✭ 127 (-36.18%)
Mutual labels:  english, programming, lesson, stable, data-visualization
Shell Novice
The Unix Shell
Stars: ✭ 234 (+17.59%)
Mutual labels:  automation, english, programming, lesson, stable
Make Novice
Automation and Make
Stars: ✭ 122 (-38.69%)
Mutual labels:  automation, english, programming, lesson, stable
Python Novice Gapminder
Plotting and Programming in Python
Stars: ✭ 109 (-45.23%)
Mutual labels:  english, programming, lesson, stable, data-visualization
wrangling-genomics
Data Wrangling and Processing for Genomics
Stars: ✭ 49 (-75.38%)
Mutual labels:  programming, english, stable, lesson
R Ecology Lesson
Data Analysis and Visualization in R for Ecologists
Stars: ✭ 218 (+9.55%)
Mutual labels:  english, lesson, stable, data-visualization
Git Novice
Version Control with Git
Stars: ✭ 227 (+14.07%)
Mutual labels:  english, programming, lesson, stable
R Raster Vector Geospatial
Introduction to Geospatial Raster and Vector Data with R
Stars: ✭ 76 (-61.81%)
Mutual labels:  english, lesson, stable, data-visualization
matlab-novice-inflammation
Programming with MATLAB
Stars: ✭ 26 (-86.93%)
Mutual labels:  programming, english, stable, lesson
Python Ecology Lesson
Data Analysis and Visualization in Python for Ecologists
Stars: ✭ 116 (-41.71%)
Mutual labels:  english, lesson, stable, data-visualization
shell-genomics
Introduction to the Command Line for Genomics
Stars: ✭ 54 (-72.86%)
Mutual labels:  programming, english, stable, lesson
cloud-genomics
Introduction to Cloud Computing for Genomics
Stars: ✭ 13 (-93.47%)
Mutual labels:  english, stable, lesson
lc-data-intro
Library Carpentry: Introduction to Working with Data (Regular Expressions)
Stars: ✭ 16 (-91.96%)
Mutual labels:  english, stable, lesson
OpenRefine-ecology-lesson
Data Cleaning with OpenRefine for Ecologists
Stars: ✭ 20 (-89.95%)
Mutual labels:  english, stable, lesson
Data Science Hacks
Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.
Stars: ✭ 273 (+37.19%)
Mutual labels:  data-analysis, numpy, data-visualization
data-analysis-using-python
Data Analysis Using Python: A Beginner’s Guide Featuring NYC Open Data
Stars: ✭ 81 (-59.3%)
Mutual labels:  numpy, data-analysis, matplotlib
Ai Learn
人工智能学习路线图,整理近200个实战案例与项目,免费提供配套教材,零基础入门,就业实战!包括:Python,数学,机器学习,数据分析,深度学习,计算机视觉,自然语言处理,PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
Stars: ✭ 4,387 (+2104.52%)
Mutual labels:  data-analysis, numpy, matplotlib
Python Aos Lesson
Python for Atmosphere and Ocean Scientists
Stars: ✭ 49 (-75.38%)
Mutual labels:  english, programming, lesson
CC33Z
Curso de Ciência da Computação
Stars: ✭ 50 (-74.87%)
Mutual labels:  programming, numpy, data-analysis
Mlcourse.ai
Open Machine Learning Course
Stars: ✭ 7,963 (+3901.51%)
Mutual labels:  data-analysis, numpy, matplotlib

Programming with Python

GitHub release Create a Slack Account with us Slack Status

An introduction to Python for non-programmers using inflammation data.

About the Lesson

This lesson teaches novice programmers to write modular code to perform data analysis using Python. The emphasis, however, is on teaching language-agnostic principles of programming such as automation with loops and encapsulation with functions, see Best Practices for Scientific Computing and Good enough practices in scientific computing to learn more.

The example used in this lesson analyses a set of 12 files with simulated inflammation data collected from a trial for a new treatment for arthritis. Learners are shown how it is better to automate analysis using functions instead of repeating analysis steps manually.

The rendered version of the lesson is available at: https://swcarpentry.github.io/python-novice-inflammation/.

This lesson is also available in R and MATLAB.

Episodes

# Episode Time Question(s)
1 Python Fundamentals 30 What basic data types can I work with in Python?
How can I create a new variable in Python?
Can I change the value associated with a variable after I create it?
2 Analyzing Patient Data 60 How can I process tabular data files in Python?
3 Visualizing Tabular Data 50 How can I visualize tabular data in Python?
How can I group several plots together?
4 Repeating Actions with Loops 30 How can I do the same operations on many different values?
5 Storing Multiple Values in Lists 30 How can I store many values together?
6 Analyzing Data from Multiple Files 20 How can I do the same operations on many different files?
7 Making Choices 30 How can my programs do different things based on data values?
8 Creating Functions 30 How can I define new functions?
What’s the difference between defining and calling a function?
What happens when I call a function?
9 Errors and Exceptions 30 How does Python report errors?
How can I handle errors in Python programs?
10 Defensive Programming 30 How can I make my programs more reliable?
11 Debugging 30 How can I debug my program?
12 Command-Line Programs 30 How can I write Python programs that will work like Unix command-line tools?

Contributing

Travis Build Status

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes!

Maintainers

Lesson maintainers are Trevor Bekolay, Maxim Belkin, Anne Fouilloux, Lauren Ko, Valentina Staneva, and creator of Software Carpentry: Greg Wilson.

Authors

A list of contributors to the lesson can be found in AUTHORS.

License

Instructional material from this lesson is made available under the Creative Commons Attribution (CC BY 4.0) license. Except where otherwise noted, example programs and software included as part of this lesson are made available under the MIT license. For more information, see LICENSE.md.

Citation

To cite this lesson, please consult with CITATION.

About Software Carpentry

Software Carpentry is a volunteer project that teaches basic computing skills to researchers since 1998. More information about Software Carpentry can be found here.

About The Carpentries

The Carpentries is a fiscally sponsored project of Community Initiatives, a registered 501(c)3 non-profit organisation based in California, USA. We are a global community teaching foundational computational and data science skills to researchers in academia, industry and government. More information can be found here.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].