Naked Data Science

This is the episode where we are going to risk our career, our wellbeing, and all the professional reputations we have built over the years to talk about this ultra-sensitive taboo topic: office politics in data scienceSeriously though, we have seen many data scientists who don't want to hear or learn about politics. And as result, they often hit invisible walls in their careers and become very frustrated. That's why we are sharing some mental models we use to think about and deal with politics so that you won't go down that path. Meanwhile, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We will demystify the transition into data science. We will show you the most effective way to build your skills. And we will advise you on the four possible options you can take to go from where you are to landing a data science job in as little as 9 months. Find out more here.

When we talk to people who want to transition into data science, we hear this question popping up more and more: what is the difference between a data scientist and a machine learning engineer, and which one should I choose? In this episode, we talk about why the separation between these two roles is ambiguous at best, why many people have switched between these roles, how we speculate the roles to evolve in the future, and some tips on how you can plan your career based on what we discussed. Meanwhile, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We will demystify the transition into data science. We will show you the most effective way to build your skills. And we will advise you on the four possible options you can take to go from where you are to landing a data science job in as little as 9 months. Find out more here.

If you are a data scientist, or someone who wants to become a data scientist,  chances are that you dream about joining a leading tech company, like Google, Facebook, and Amazon.  However, depending on your situation and personality, that might not be the best career goal for you. In this rebroadcast episode, we will talk about the number one pitfall for highly specialized roles in those companies, some hidden reason why they publish a lot of papers, and why you shouldn't just blindly copy how they do data science.Meanwhile, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We will demystify the transition into data science. We will show you the most effective way to build your skills. And we will advise you on the four possible options you can take to go from where you are to landing a data science job in as little as 9 months. Find out more here.

Having a Big Bang is one of the most common causes of data science project failures. And you probably have done it, at least a couple of times. In this episode, we will show you why it is often better to aim for sub-optimal solutions at the start of a project, and how you can avoid the Big Bang problem by following an ancient Japanese philosophy. By the way, we are rebroadcasting this episode because it is one of our favourite early episodes. And the content can be very valuable to our new listeners. Meanwhile, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We will demystify the transition into data science. We will show you the most effective way to build your skills. And we will advise you on the four possible options you can take to go from where you are to landing a data science job in as little as 9 months. Find out more here.