One of the reasons I found Customer Insight Leadership such a rewarding career and still find it fascinating to help such leaders, is the diversity. One day your focus may need to be on improving specific Analytics skills, or complex Data problems, the other it could be how to change culture as a leader or summarise key insights from qualitative research. It really is a polymaths’ playground.
Just because you can, doesn’t mean you should; this is perhaps the simplest way to introduce questionable practices.
Last year we posted on the desire amongst many data scientists to achieve social good through their work. As for all disciplines, there is also a potential “dark side“ to the capabilities of data scientists. So firms have or still do seek to use data as a weapon or persist with questionable analytical activities.
In this post we will explore 3 examples that should prompt your own reflections on data ethics and any implications for you.
I’ll share on uses of data science for: employee surveillance; winning elections & proliferating fake news. More encouragingly, I will close with how two organisations are working to advance a code of ethics for data scientists, as a positive response to this challenge.
But first, let’s explore the darker side of Data Science use. (more…)
I hope you’ve found this month’s Data Science content to be useful, as I’d also like to share with you some personal Data Science stories.
To complement the technical resources I’ve shared recently, it’s important to also focus on the people side. Customer Insight Leader exists to encourage and inform more holistic customer insight leaders, covering the disciplines of data, analytics & research, as well as the requisite leadership skills.
In this post I’ll share 3 different perspectives on Data Science careers: from those starting out in tech companies; to an ex-freelancer; plus an experienced data scientist sharing his experience & resources with us.
So, if you are an analyst considering a move into Data Science, or a leader wanting to better understand your Data Scientists or options for your people – I hope this helps. (more…)
Following on from our series on programming languages, I’m delighted to introduce a guest post on Data Science readiness.
Having the strategy or aspiration to make use of Data Science is one thing, executing it effectively is another. Too many businesses I know have hired data scientists without really knowing what to do with them or what they need to succeed.
So, I’m pleased to introduce a new guest blogger. Mark Sellors is Head of Data Engineering for Mango Solutions.
It helps to hear a Data Engineer’s perspective, as that can be such a foundational role to a successful Data Science team.
As promised, I’m returning to our series covering data science programming languages, this time-sharing resources for Julia.
My first introduction to the Julia language, was mentions at R or Python events, that “the cool kids are writing in Julia these days“. Now, bloggers are always in danger of being on the look out for something topical or trendy, but further investigation revealed that Julia is indeed a useful language with growing usage amongst data scientists.
So, to ensure we are not limited to the more familiar R and Python languages, I’m delighted to extend our series to also look at resources for Julia programmers, or those wanting to consider this language. As before, I’ll share a book recommendation for learning Julia, as well as some online resources, cheat sheets and an event to attend.
I hope this proves useful, for Data Scientists and Insight Leaders, who are seeking to expand their repertoire or achieve better performing code.
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 impact made by their Data Science teams and so may limit the lifespan of business willingness to invest.
Like many such business problems, the challenge is not with the technology or innovation itself, but rather how it is managed. Once again, it’s what you do with it that counts. (more…)
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 with analytics teams, there is perhaps a choice between the more statistically grounded R and the easier programming in Python.
But, even that distinction is now less clear, as both benefit from the kind of support/resources ecosystem that I mentioned in my post on R.
So, enough introduction, let me share some resources that I’ve found to help Python coders (and would be coders). Enjoy diving in, at the risk of getting bitten by the coding bug. (more…)