Skip to content
View GPN87's full-sized avatar

Block or report GPN87

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this userโ€™s behavior. Learn more about reporting abuse.

Report abuse
GPN87/README.md

Hi ๐Ÿ‘‹, I'm Gavin from Sydney, Australia

Recent Bootcamper | Former policy-writer | Data analyst.
So far my skillset is an inch deep and a mile wide but I want to keep digging
and notch up a few more of those 10,000 hours.


  • ๐Ÿ”ญ Iโ€™m currently working on an exploratory analysis of the Forbes 2022 list of the world's best employers

  • ๐Ÿ“š At the moment I'm reading 'An Introduction to Networking' by Dr. Charles R. Severance

  • ๐Ÿ“ I recently wrote an article an about post-Covid career transitions.

  • ๐Ÿ‘จโ€๐Ÿ’ป All of my projects are available here!

  • ๐Ÿ“ซ How to reach me gavin.payne87@gmail.com

๐ŸŒฑ I've already learnt how to..

  • clean, manipulate and explore data sourced from flatfiles, databases and jsonified API responses using Pandas.
  • visualise and run statistical analyses of data using Matplotlib, numPy and sciPy.
  • perform 'Create, Read, Update and Delete' operations as well as joins, unions, and subqueries to SQL databases.
  • identify data relationships and apply data modelling techniques to database design for e.g. using primary, foreign & composite keys, and through-tables.
  • create and run a server and define an API endpoint using Flask.
  • interact with the MongoDB NOSQL database using either Mongosh or the PyMongo library.
  • create web-based visualisations using a range of javascript libraries including plotly and charts for dashboards and leaflet for geoJSON objects.
  • tell data stories using Tableau, including with dashboards, mapping elements and calculated fields.
  • perform KMeans cluster analyses in Python using scikit-learn, including with optimisation techniques such as principal component analysis.

๐Ÿ”ฎ What's next:

Supervised learning and logistic regression.

Languages and Tools:

Programming Languages

  • Python
  • SQL
  • Javascript

Applications

  • Flask
  • Tableau
  • Knime

Tools

  • Pandas
  • Matplotlib
  • Numpy
  • scikit-learn
  • PyMongo
  • d3
  • plotly
  • leaflet
  • pgAdmin

ย gpn87gpn87

Pinned Loading

  1. Fitbit-ETL Fitbit-ETL Public

    Jupyter Notebook

  2. nba_statscentre nba_statscentre Public

    Web-based visualisations of NBA statistics using range of Javascript libraries. Served locally using Flask.

    Jupyter Notebook

  3. employees_database_SQL employees_database_SQL Public

    Six flat-files modelled with quickDB, loaded and queried using PostgreSQL

  4. Thrive2Drive_website Thrive2Drive_website Public

    A live website for a Sydney Driving School. Uses a Bootstrap template and a contact form.

    HTML

  5. top_100_employers_2022 top_100_employers_2022 Public

    A Pandas EDA that answers the question: 'Do good employers make good investments?'

    Jupyter Notebook

  6. climate_data_in_flask climate_data_in_flask Public

    Flask app that returns three API endpoints from an sqlite database containing climate data from weather stations.

    Jupyter Notebook