To A/B or not to A/B
Introduction # This is the story of a project that started off as a straightforward A/B test but soon revealed more than we expected. Along the way, we uncovered additional insights that added depth to our understanding and expanded the scope of the analysis. It’s been quite a while now, since I worked as an independent data and analytics consultant. I went freelance after many years working on data systems, BI, and MIS for a large, multinational education company. I realised that I had discovered an outsize passion and enthusiasm for data science while leading projects using applied statistics, data mining and algorithmic forecasting during my tenure in the early 2010’s. In that role, the longer term opportunities to develop this skill set further were not available. So, with a little trepidation and renewed sense of clarity over my career goals, I took a decisive step to cut myself loose and find more such project work on my own initiative.
Tallinn Ride Hailing App
Introduction # I acquired a fun little data set for a well-known ride-hailing app recently. I performed a pretty detailed analysis at the request of my source, including some clustering of the ride start locations. The idea was to help drivers plan ahead to get into position before times of peak demand. There’s no NDA and this data is no longer very fresh, so I thought it would be nice to show the results in an interactive Tableau viz.
Educational Attainment in England - A Deeper Dive
Introduction # On the 25th July 2023, the UK Office for National Statistics produced a wonderful piece of data journalism with open access to their dataset measuring educational attainment across England. The article’s title “Why do children and young people in smaller towns do better academically than those in larger towns?” hides a bold claim in the form of a question. I like to assume that their research question(s) did not start from such a knowledge claim but rather the title emerged from the themes they discovered during their investigation.
Tech Stock Stories - The Pandemic Winners
As we breath a collective sigh of relief that the COVID-19 pandemic is behind us, it’s interesting to think about the winners and losers of this unique period of living memory. Judging by the stock performance of US tech giants, I’d say their shareholders did rather well while the rest of the world switched to remote work and spent all our time indoors. Please enjoy my Tableau Viz below, which is a candle chart of the stock performance of US tech giants during the pre- and post-pandemic period. We can see the month average highs, lows, opens and closes, and you can pick the company of interest from the drop down list on the right.
What is Analytics Engineering?
The well-known roles of the data team are Data Analyst/Scientist and Data Engineer. Yet, in recent years, there has been a growing demand for data-driven decision making to be distributed throughout the organisation, with a traditional data team at risk of becoming a bottle neck as the growing need for insights and analytics cannot be fully met. With this increased demand has come the emergence of a new role - the Analytics Engineer - for the typical data team to better encompass these evolving responsibilities, and specialist technologies. This evolution has led to a paradigm shift in data processes from Extract-Transform-Load (ETL) to Extract-Load-Transform (ETL), which facilitates a more self-service oriented Business Intelligence (BI) operating model where data analysts and data scientists can be more embedded with domain teams.