This role involved completing projects to help Butterfly and the clients they serve.• Creation of synthetic data artefacts in Python as part of a project to help with security.• Data needed to be fictional as this data would be used to fulfil security purpose, but the fictional data had to look as real as possible.• Data was generated using a variety of tools.• Python coding allowed data to be made which was used.• PowerBI dashboard showing information on organisation.• Carried out SAS training to understand basics of the SAS language.

This role supported Cafcass during the pandemic with data analysis on a national level. Relevant Tasks included:• Creation and execution of an extensive list of User Acceptance Tests, done to ensure that system crossover has been achieved. Tests were done in manner similar to users, including using calling off tests that took too long• Analysed data by transferring reports from PowerBI to Excel to obtain knowledge on whether a new compliments/complaints system is working to produce more data and better results• Completed multiple analysis on data sets to check cases and trends in critical time periods – such as operations both before and after March 2020• Created a dashboard in PowerBI which allows users to look at the state of the system as it happens. This contains results of Power Queries and information streamed from the business servers• Cleaned incoming case data to requirements. This included corrections, replacements and requesting areas to complete and resend data that was unacceptable. The procedure allowed me to use my data cleaning skills to ensure that data met requirements and was acceptable to be transferred onto a master database

This role supported key academic staff in the development of technological advances:• Analysed a large data set of user responses to a chatbot.• Generated deep and executive summary reports - detailing chat bot data usage using Office.• System development recommendations - highlighting future features enabling staff to access data reports.• Using the chat bot infrastructure - detailed mapping of known answers against user-submitted questions.• Data checking, collating and de-duplicating data to ensure full integrity.• Excel Pivot Tables were used to perform deep analysis for data, and report generation from large datasets.• Conducting further analysis of data, as well as making considerations for incomplete data sets - considerations included waiting for more data and using what was available.• Attention to organisational needs involving choice of methodology and software (e.g. reporting tool handoff)• Frequent communication with project stakeholders on progress and direction.• Stakeholder needs analysis by prioritising tasks and meeting critical outcomes.• Meeting deadlines and using project planning/reporting to manage both academic and non-academic situations.