Insights … and (career) decisions

I follow developments in High Performance Sport New Zealand with great interest, particularly now that Jacquie Tran is a Senior Insights Researcher there.

Today, Jacquie shared the announcement of the availability of two insights researcher positions in the Knowledge Edge team. The job descriptions are on the HPSNZ web site (link).

The advert has this section:

Some familiarity with (or readiness to learn) the following would be advantageous:

  • Thematic analysis and natural language processing
  • Relational databases (e.g., SQL)
  • Programming languages for working with data (e.g., R, Python, Stata)
  • Visualisation tools (e.g., Tableau, Power BI).

The job purpose notes that the insights researcher “will assist with capturing, analysing, exploring and reporting on qualitative and quantitative data”, “utilise a relational database” and “visualise data patterns and support the investigation of insights … to inform performance decision-making”.

I was delighted to read that the first line in the person specification highlights “curiosity and passion”. Candidates can have completed a tertiary degree with a research component or have experience in a research based field.

I hope they get lots of applications for these opportunities. Their availability signals are growing trend in sport and raises some very important pedagogical and experiential issues.

Photo Credit

Mount Roskill hoto by Bill Fairs on Unsplash

Continuing learning and innocent climbing

I am staggered by the expertise shared openly.

Each day, my inbox delivers treasures that are growing in scale.

Today, thanks to Mara Averick (link), I discovered Danielle Navarro’s personal essay on Bayes factors (link).

Danielle’s post has given me a holiday reading list that will help me redefine my naive Bayes views and thinking.

As I was contemplating the references her post unlocked, I came across these images that I have taken to be the innocent climb of continuing learning and the joy of finding new inclines (aka steep learning curves):

(Source of this idea was from R-Ladies Sydney (link) via Real Python (link))