README.md

Data Bench

One data tool as a standard to organize triage, cleaning and modeling of my data projects on GitHub

• Just as you have one toolbox for plumbing and another for electrical
• Version 0.1.9 - This is a prototype some features are broken

Ghantt Project Planning

• I chose Critical Chain for its ease of use, start with the end in mind
  • Supplemental Reading: https://www.simplilearn.com/what-is-critical-chain-project-management-rar68-article
  • Good Explanation: https://www.knowledgehut.com/tutorials/project-management/critical-chain-method
• README.md markdown documents can be your friend as they are multifaceted tools
  • Markdown code allows me to take raw data like this

    gantt
    title A Gantt Diagram
    dateFormat  YYYY-MM-DD
    section Initial Phase
    Topic Selection                     :a1, 2021-09-19, 7d
    Suitabile/Practical                 :after a1  , 7d
    section Implement
    Topic Formulation                   :b1, 2021-09-26, 7d
    Debugging/Cleaning                  :after b1  , 7d
    section Test/Debug01
    Scope Document                      :c1, 2021-10-03, 7d
    Suitable vs Practical               :after c1  , 7d
    section Test/Debug02
    API access changes                  :c2, 2021-10-10, 7d
    API data triage                     :after c1  , 14d
    section Implement
    Upskill Team Members on GitHub      :e1, 2021-10-17, 28d
    Define and adapt critical path      :after a1  , 49d
    section Implement
    BiWeek Scrum Review               :f1, 2021-10-17, 35d
    Trial Run 1                        :after e1  , 7d
    section Implement
    Functional Triage 1               :f2, 2021-11-14, 7d
    Presentation Dry Run                :after e1  , 14d
    Trial Run 2                        :after f2  , 7d
    section Draft Doc
    Finalize Design, Data, Functions and write tests          :f1, 2021-10-24, 42d
    Trial Run 3                        :after g1  , 7d
    section Revised Doc
    Functional Triage 2               :g1, 2021-11-21, 7d
    section Revised Doc 2
    Functional Triage 3               :h1, 2021-11-28, 7d
    section Final Doc
    Trial Run 4                       :after h1  , 7d
    Functional Triage 4               :i1, 2021-12-05, 7d
    Record & Revise                   :after i1  , 7d
    Final Run 3                       :after i1  , 7d

• Then we can use Mermaid to autogenerate a ghantt chart
• Next step is load Mermaid, click here for a tutorial http://mermaid-js.github.io/mermaid/#/n00b-gettingStarted
• I'm partial to Mermaid see below for the Gantt chart was created from the code above

• While this is not perfect it is a good start to align Critical Chain Project Management to Data Science project coding and organization

Critical Chain Providers

Provider,type,usage,pro,con

1. Local, Hosting, testing/learning
2. Github, Hosting, code link, free, no dymanic
3. Heroku, Hosting, starter, cheap simple, not huge demand/unknown
4. AWS, Hosting
5. Google Cloud, Hosting - future
6. Tableau visuals - future
7. PowerBI - future
8. QLI - future
9. Salesforce - future

Errata

• Any sufficently useful code will create errors
• Creating a bug database
  • DebugDB.py will be a database in sqlite using

Requirements

Installation - Pycharm

• git clone Data_Bench and press enter in your terminal of choice
• add 'pyproject.toml' to access your black connect settings
• you will need to add your .env variables as needed
• update 'streamlit_dashboard.py' file as needed

Features

• /data is a central data folder for testing ML and NLP models
• /output will recieve output of ML and NLP model outputs
• /test will contain the generated tests for each model
• /future will contain features in dev for upgrades and updates as needed
• /src contains files when we publish this to pypi

Future Features

• Automated dashboard for csv, tsv, par
• Automated dashboard for SQL
• Semi automated vector database
• Active database monitoring for testing
• Database DDL, DCL and DML schema monitoring option
• Auto abstraction of user name or passwords in code to .env file
• Future Feature is to have Mermaid pull flowchart function from my python code description string :param:
• Data Bench will have storage for ML, NLP, Ai toolboxes
• Data Bench will detect compatible filetypes and have a place to stream data vs downloading it
• Data Bench will remember your past projects and suggest models or compatibility issues

LICENSE

The Commons Clause is a License Condition drafted by Heather Meeker and contributed by FOSSA.

“Commons Clause” License Condition v1.0

The Software is provided to you by the Licensor under the License, as defined below, subject to the following condition.

Without limiting other conditions in the License, the grant of rights under the License will not include, and the License does not grant to you,  right to Sell the Software.

For purposes of the foregoing, “Sell” means practicing any or all of the rights granted to you under the License to provide to third parties, for a fee or other consideration (including without limitation fees for hosting or consulting/ support services related to the Software), a product or service whose value derives, entirely or substantially, from the functionality of the Software.  Any license notice or attribution required by the License must also include this Commons Cause License Condition notice.

Software: [Data_Bench]
License: [i.e. Apache 2.0]
Licensor: [datatalking]

About

Read about the Commons Clause and its purpose on the public FAQ

License

This repository is under the Public Domain.