31 - The Big Bang theory of data science (rebroadcast)

02/14/21 • 17 min

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.

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.

Previous Episode

30 - The One When Queries Beat Machine Learning

Can you solve a data-intensive business problem with just queries? If so, what is the difference between data science and, say, data analytics?
These are not just theoretical questions. The answers have a practical and significant impact on your daily work and well-being. In this episode, we will share a couple of mental models we use to think about these topics. Enjoy.
BTW, 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.

Next Episode

32 - So you want to become a data scientist at FAANG

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.