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Naked Data Science
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Naked Data Science
Naked Data Science
A weekly Technology podcast
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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 t
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
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
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
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 sta
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 yo
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
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 mak
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 r
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 t
There were cognitive biases in the data science work you did. And there will be more cognitive biases in all the future work you will ever do. They are just part of being human. But if you don't pay attention to HOW these cognitive biases affec
What happens when you are not working on interesting work? It is boring, you feel stuck, and your skills and career stop developing. But it is also very bad for your company: they now have an employee who is not delivering good outcome while st
Unless you have been living in a cave in the past 2 years, you have heard of AutoML. And depending on where you have heard it from, it can be the best thing ever happened to data science, the evil invention that will put thousands of data scien
What can you do about about the ethics of AI, Machine Learning, and other data science solutions in your daily work. Why it is important to think about implications first, not technologies. The four principles we use to address ethical challeng
Why data science team communication is so difficult. Analytics Translator is not the solution. Role of PM in a data-intensive solution team. Why you shouldn't rely on everyone's notes. What to do when you receive a long text. When to put things
Systems thinking to make sense of your data science work. The similarity between dead fishes and recommender systems. Effect of time and feedback loop on your models. Look beyond your dataset. Applying systems thinking to people and teams. How
How domain knowledge can supercharge your data science work. The half-life of truth at three levels of business domain knowledge. Why it is important to follow the money in data science work. Three ways to acquire new domain knowledge fast.BTW,
How thinking in questions can help you communicate your work effectively, especially to non-data-scientists. Avoid getting lost when finding your path to a solution. Three reasons why you should always ask more questions when you hear a questio
How data science is done in three different types of organizations. Three common mistakes people make when borrowing ideas. How we created our own agile methodology. The importance of finding your own answer.BTW, if you are not a data scientist
The three types of errors in data science and how to deal with them. Why intelligent people make mistakes. How not to surprise yourself by errors you knew. The art of not making errors personal. The importance of thinking and talking trade-offs
How data-intensive technologies have changed in the past five years, the best way for data scientists to stay on top of technologies, and the three timeless data roles.This episode is a guest interview with Wilco. Wilco has 20 years of experien
When do you stop looking at the data, make a decision, and move on? We dive deep into this audience question. But instead of giving an answer, we think that the best answers come from asking four more questions. We will show you what these que
The number one pitfall of highly specialized roles, the consequence of premature optimization, the garden of many low hanging fruits, the hidden reasons why these giants publish more papers, and why you shouldn't blindly follow them. BTW, if y
Three common mistakes about uncertainty in business, the idea of just enough uncertainty for decision making, pitfalls of p-value in AB testing, and how leaders can benefit from fostering conversations about uncertainty and data-driven decision
Why you don't need a perfect CV before applying, why you shouldn't try to answer all questions during interviews, the right mindset to think about hiring companies, and also some unsolicited relationship advice. Enjoy.BTW, if you are not a data
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