Working with people to effect positive change through data-driven insight
Above all, I value the human dimension of work: personal relationships and communication. Data and technology are powerful tools, but it's the people who make things happen.
I've worked in a variety of roles and companies, developing the professional and technical skills to make me an asset for any project or team (CV for details).
So if you're looking for a dynamic and capable DA with a unique blend of skills, why not get in touch?
Project on the way: data enrichment
Starting with source data in a non-standard format, I used Python to extract, clean and transform the data, anonymising PII, then loaded into Power BI for visualisation
A major step forward here is the visual presentation of the Power BI report. The aesthetic-usability effect tells us a good-looking report is perceived as easier to understand, which is essential for communication
I then presented my recommendations for the imaginary company on how they could improve their use of data
Recreating my previous project, using Databricks for the entire pipeline: ingesting the raw text file to a managed volume, extracting the data by adapting my existing python script to return a Spark dataframe into a manifested view, then visualising that data in a dashboard
Databricks' unified workspace makes it very easy to manage the end-to-end data journey
Familiarising myself with Amazon's suite of data tools, I created a workflow using S3 for storage and Glue for transformation
First I uploaded my source file to an S3 bucket. Then I adapted my existing Python script to work as a Glue job, reading the source from S3, transforming the data and writing back to S3 in Parquet format
This pictogram was created using Flourish, based on recent news media discussions
Flourish makes it straightforward to present data with clear and beautiful imagery, interactivity and sequential storytelling
Using ChatGPT to turn an abstract plot of a network into a better-looking and more-engaging visual
The initial graph was created with Python's networkx, matplotlib and pandas modules, then reimagined by ChatGPT
Using Power BI's interactivity to create an engaging report and enable deeper exploration of the data
Created as an assessment for the Generations Data Analytics Programme, this report had to answer 5 questions about the provided star-schema dataset
Evaluating customer reviews from a real e-commerce dataset on a positive-negative scale
I investigated whether the calculated sentiment correlated with the customers' review scores, and looked at how both values on average changed over time (using Python package NLTK for analysis and Power BI for visualisations)