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GPN87/README.md

Hi 👋, 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.

  • 🔭 I’m currently working on an exploratory analysis of the Forbes 2022 list of the world's best employers

  • 📚 At the moment I'm reading 'An Introduction to Networking' by Dr. Charles R. Severance

  • 📝 I recently wrote an article an about post-Covid career transitions.

  • 👨‍💻 All of my projects are available here!

  • 📫 How to reach me [email protected]

🌱 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.

🔮 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

 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