{"id":21567239,"url":"https://github.com/richarizardd/scientific-computing-in-python-for-bmes","last_synced_at":"2025-03-18T05:31:44.548Z","repository":{"id":128976298,"uuid":"71761225","full_name":"Richarizardd/Scientific-Computing-in-Python-For-BMES","owner":"Richarizardd","description":null,"archived":false,"fork":false,"pushed_at":"2017-05-09T05:28:28.000Z","size":14666,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-24T12:22:19.455Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Richarizardd.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-10-24T07:04:44.000Z","updated_at":"2016-10-24T08:17:59.000Z","dependencies_parsed_at":"2023-06-15T03:15:30.380Z","dependency_job_id":null,"html_url":"https://github.com/Richarizardd/Scientific-Computing-in-Python-For-BMES","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Richarizardd%2FScientific-Computing-in-Python-For-BMES","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Richarizardd%2FScientific-Computing-in-Python-For-BMES/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Richarizardd%2FScientific-Computing-in-Python-For-BMES/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Richarizardd%2FScientific-Computing-in-Python-For-BMES/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Richarizardd","download_url":"https://codeload.github.com/Richarizardd/Scientific-Computing-in-Python-For-BMES/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244164707,"owners_count":20408975,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-24T10:29:38.505Z","updated_at":"2025-03-18T05:31:44.524Z","avatar_url":"https://github.com/Richarizardd.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction to Python for BMEs\n## 1. Python and Jupyter Notebook Installation\nPlease go to this website and download the **Python 2.7** installer for your operating system: https://www.continuum.io/downloads\n\nOnce you have downloaded the installer and installed Anaconda, open up the **Anaconda Navigator** program.\n\nClick on the 'Launch' button for Jupyter notebook.\n\n## 2. Python Tutorial Instructions\nDownload the .zip file of this Github repository by clicking on the green 'Clone or download' button in the top right.\n\nUnzip this file in your documents, desktop, or other preferred directory.\n\nFrom the Jupyter notebook console in your browser, navigate to this directory.\n\nClick on one of the 6 notebook modules to get started!\n\n## 3. Table of Contents\n\n1. An Introduction to Python - This module is for beginner programmers who have little or no prior programming experience. It is also for experienced programmers who are new to Python.\n\n2. Lists - This module is an overview of lists, one of the most important Python data structures, and their applications.\n\n3. Loops - This module teaches how to use `while` and `for` loops to perform calculations and run biological simulations.\n\n4. Computational Genomics - This module explores how Python programming can be used to perform genomics related tasks.\n\n5. Image Processing - This module shows how to analyze images and identify specific features in images.\n\n6. K-Means Clustering - This module teaches how to use `for` loops to implement basic machine learning.\n\n## 4. Assignments\n\n1. Exercises (points) - \n\n2. Lists (points) - \n\n3. Loops (points) -\n\n4. Computational Genomics (points) - \n\n5. Image Processing (points) - \n\n6. K-Means Clustering (points) - \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricharizardd%2Fscientific-computing-in-python-for-bmes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fricharizardd%2Fscientific-computing-in-python-for-bmes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricharizardd%2Fscientific-computing-in-python-for-bmes/lists"}