Merge pull request #56 from aakankshaduggal/master

Add _toc.yml and _config.yml

_config.yml

@@ -0,0 +1,37 @@
+title: Data Science Workflows
+author: AIOps
+# logo: logo.png
+execute:
+  execute_notebooks: false
+
+# Only build files specified by the _toc
+only_build_toc_files: false
+
+# Exclude any additional file types being built
+exclude_patterns: [.github/*, manifests/*,  CHANGELOG.md, docs/images/*, public/assets/*]
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/aicoe-aiops/data-science-workflows  # Online location of your book
+  path_to_book: ""  # Optional path to your book, relative to the repository root
+  branch: master  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+  use_issues_button: true
+  use_repository_button: true
+  google_analytics_id: ""  # A GA id that can be used to track book views.
+
+# Launch button settings
+launch_buttons:
+  notebook_interface        : jupyterlab  # The interface interactive links will activate ["classic", "jupyterlab"]
+  binderhub_url             : " "
+  jupyterhub_url            : https://jupyterhub-opf-jupyterhub.apps.smaug.na.operate-first.cloud  # The URL of the JupyterHub
+  thebe                     : false  # Add a thebe button to pages (requires the repository to run on Binder)
+
+sphinx:
+  config:
+    html_js_files:
+    - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
+

_toc.yml

@@ -0,0 +1,17 @@
+format: jb-book
+root: README
+parts:
+  - caption: Getting started
+    chapters:
+    - file: docs/getting-started
+    - file: docs/how-to-contribute
+  - caption: AIOps projects
+    chapters:
+    - file: docs/aiops-projects
+    - file: docs/project-document-template
+    - file: docs/project-structure
+  - caption: Best Practices and Guidelines
+    chapters:
+    - file: docs/guidelines-for-reviewing-datascience-code.md
+    - file: docs/data-visualization-best-practices.md
+    - file: docs/create_and_deploy_jh_image.md

docs/data-visualization-best-practices.md

@@ -1,6 +1,6 @@
-# **Best Practices for Data Science Visualizations**
+## Best Practices for Data Science Visualizations
 
-## **Purpose**
+### **Purpose**
 
 _Visualizations are meant to supplement and provide clarity and insight into the claims being made about the data. People are generally not analytical and are naturally better at interpreting information visually. Thus, as a data scientist, it is important to present this information in a way that accurately provides a clear visual representation of the data, and what the story behind the data is._
 

docs/getting-started.md

@@ -1,4 +1,4 @@
-## **Getting Started**
+## Getting Started
 
 Please use the following outline to get started with a new Data Science Project. If you run into any problems or need additional support, please join our [slack](https://operatefirst.slack.com/ssb/redirect) and post a message in the `#support` channel, or open an issue [here](https://github.com/operate-first/support/issues).  
 

docs/how-to-contribute.md

@@ -1,10 +1,10 @@
-# **How to Contribute**
+## How to Contribute
 
 Please refer to this guide for information on how to contribute to our data science projects.
 
 We encourage contribution to each of our [Data Science Projects](https://www.operate-first.cloud/data-science). In order to start contributing to our projects you can check out the "How to Contribute" section for each project under https://www.operate-first.cloud/data-science/.
 
-## **GitHub Workflow**
+### **GitHub Workflow**
 
 1. Create an issue for every task you plan to work on.
 2. Add the issue to the *New* column of the corresponding project board.
@@ -16,7 +16,7 @@ We encourage contribution to each of our [Data Science Projects](https://www.ope
 6. Create a PR for the issue and reference the issue in the PR description.
 7. Let somebody from the team review the PR - never merge your own PRs.
 
-## **Contributing via Pull Requests**
+### **Contributing via Pull Requests**
 
 Contributions to another data science project take the form of pull requests (PRs) against that project’s GitHub repository. Here are the steps for contributing using PRs:
 
@@ -30,7 +30,7 @@ Contributions to another data science project take the form of pull requests (PR
 8. Wait for someone to review your PR. They may suggest or request changes to your PR before approving it.
 9. Once your PR is approved and merged into the repository, you can delete your branch. 
 
-## **Reviewing Pull Requests**
+### **Reviewing Pull Requests**
 
 Code reviews are common practice in the software engineering field and have been for some time. They provide developers with a well defined approach to sharing knowledge, ensuring exposure to new ideas and technologies, enforcing quality assurance of code and standards for the engineering process across both the code base and the team itself. As members of the AI CoE in the office of the CTO, we believe that having a well defined approach for how to do the same thing with data science and machine learning code is critical. Although, code is code, and we approach data science work as a subset of software engineering, there are some data science specific challenges associated with the code review process that should be addressed. Below are the 10 principles we use in our group to review data science work.
 

docs/project-document-template.md

@@ -1,4 +1,4 @@
-# Title - Project Description 
+# Project Outline Template
 
 
 ## _Authors,  20xx-Month-Day vx.x.x-dev_

docs/project-structure.md

@@ -1,4 +1,4 @@
-## Data Science Projects Structure for AI Ops -
+## Data Science Projects Structure for AI Ops
 
 The purpose of this document is to provide Data Scientists in the AI CoE with a template for structuring their projects as well as encouraging a more consistent workflow across projects that promotes an “operate first” mentality; testing and taking advantage of the capabilities the AI CoE has been developing, as well as to ensure that as data scientists, focused on developing intelligent applications for the cloud, we are working from a cloud-native data science mindset from the start.