We are trying out a different format in this episode. Nima gave me a topic, which is Central Limit Theorem. I spent an hour learning about it. And then we have a little chat. You will hear why we are doing this in the episode. And if you like this format, please send us an email at hello [at] nds.show . That helps us decide if we are going to make more episodes like this in the future.Meanwhile, if you are not a data scientist yet, but want to become one, you should really attend our webinar....

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 politi...

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 discu...

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 ...

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 n...

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 demysti...

One of the reasons why we love data science so much is because of the amazing methods, techniques, and technologies we can use to solve different problems. However, if you only focus on these technical tools, you will fall into the biggest trap in doing data science. In this episode, we will show you why that is the case, and when you should forget that you are doing data science. BTW, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We w...

Data science is deeply rooted in scientific research and scientific thinking. However, applying data science is more like doing detective work, especially if you work in businesses. In this episode, we will talk about the huge difference it makes when you solve data science problems like a detective, and why you shouldn't just report common machine learning metrics. BTW, if you are not a data scientist yet, but want to become one, you should really attend our webinar. We will demystify the tr...

When most people think about data science, they have some sort of Machine Learning in mind. But the truth is many data-intensive problems don't need Machine Learning, even in big tech companies like FAANG. In this episode, Nima will share the reasons why he went from a researcher in Machine Learning to become a data-driven problem solver and give a couple of tips on how you can make that transformation too. BTW, if you are not a data scientist yet, but want to become one, you should real...

If you are still scrolling through your Jupyter notebook when presenting your data science work, you are not giving your work the attention it deserves. And when I say it probably even limits your salary and career, it is not exaggerating. In this episode, we will show you why presenting is not window-dressing, but a key problem-solving skill in data science. We will give you seven practical tips and a presentation template that can drastically improve your next presentation. BTW, ...