Data strategy is one of the most important parts of any data science project. Unfortunately, it is not very recognised. This is why I have repeatedly written about this topic on this blog: The Data Strategy Canvas DS10 questionnaire for …
“Will blockchain disrupt my industry?” is one of the most asked questions since the technology’s inception in 2008. And rightfully so. With transparency and increased security being at the core of the blockchain concept, solving many of the modern issues …
Data is the new oil. So what makes it valuable? How can companies in the data science/AI space make sure they maximise the value of their data? Learn more on this podcast!
I had the chance to participate on a very interesting discussion organised by Teamleadsky. Teamleadsky provides a systemic approach to building high performance teams. On this talk I explained why culture is a very important topic in data science, and …
Data strategy is one of the favourite topics of this blog. I’ve talked a lot about this topic, and have even come up with frameworks such as The data strategy canvas and The DS10 Questionnaire for Data Strategy. A famous article by …
When you are working in tech, it is quite likely that you will be using open source at some part of your career. I am a data scientist, and most of my stack, actually all of it, is based on …
Getting a job in data science Let me ask you a question, what do the following categories of people have in common: University students STEM graduates Humanities graduates Professionals over the age of 30 Moms returning to work Many of …
How much data you need for machine learning? Collecting data is not easy. A general saying in data mining is “the more data the better”, but sometimes collecting more data is expensive, or just not feasible. Therefore, it becomes very …
Predictive analytics is one of the most popular applications of data science. In this podcast we talk more about this field and discuss about its various applications.
Cohen’s Kappa statistic is a very useful, but under-utilised, metric. Sometimes in machine learning we are faced with a multi-class classification problem. In those cases, measures such as the accuracy, or precision/recall do not provide the complete picture of the …