helping you master customer insight leadership

Getting the most out of your Data Science team, pitfalls to avoid

your data science team

During our month focussed on Data Science programming languages, my thoughts have turned to getting the most out of your Data Science team. This topic has arisen, because of what I’ve observed in a number of organisations seeking to implement Data Science. As I’ve worked with more clients and talked to other leaders at Data Science events, it has struck me how many fall into common pitfalls. These limit the.. Read More

Data Science programming languages: (2) Resources for Python

As promised in our previous post, for the R programming language, this one will focus on resources for Python. Although R may have a longer heritage within the Statistics and Data Science community, Python could be described as a more complete programming language. In my conversations with clients and Data Science leaders, I’ve also heard a number praise Python as much quicker to learn. So,although both languages are proving popular.. Read More

Data Science programming languages: (1) Resources for R

This month, let’s turn our attention to Data Science programming languages; today, resources for R. Ever since the rise of R as an alternative to traditional statistical packages (like SAS, IBM Analytics etc), there has been a growing focus on coding. In the past I have tended to avoid these programming languages as a topic for this blog, as I have some concerns. Namely that the role of insight analysts,.. Read More