Naked Data Science

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. 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.

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.

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.

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 doing any Machine Learning work, it is essential that you have a solid understanding of the most fundamental concepts and the most frequently used methods in Machine Learning. Because you will make better decisions, you will interpret results correctly, and you will know how to debug your work. That's why we made a quiz for you to test your fundamental Machine Learning knowledge. Check it out now.

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 doing any Machine Learning work, it is essential that you have a solid understanding of the most fundamental concepts and the most frequently used methods in Machine Learning. Because you will make better decisions, you will interpret results correctly, and you will know how to debug your work. That's why we made a quiz for you to test your fundamental Machine Learning knowledge. Check it out now.

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, we are trying out a new format for our podcast based on audience feedback. This episode is shorter than previous episodes and focuses on a more specific topic. Enjoy.If you like this episode, you will like our insider's guides on how to solve data science problems like a detective. We are also sharing new materials and training every week. Get these free insider's guides today.

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. If you like this episode, you will like our insider's guides on how to solve data science problems like a detective. We are also sharing new materials and training every week. Get these free insider's guides today.

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 affect your work, you can easily waste weeks if not months chasing after the wrong things. In this episode, we will talk about some common cognitive biases that affect the data science work, and how you can deal with them. By the way, if you like this podcast, you will like our free guides. We take the most popular topics from past episodes - this can be fixing projects that are not going well, receiving the recognition you deserve, building intuitions on different types of models and machine learning methods. We condense each topic into a short PDF, which you can use as a quick reference in your daily work to practice skills and develop strong intuitions on these topics. You can get these free guides at www.nds.show.

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 still requiring high effort to manage, So obviously, it would be great if you and your company can always find work that is interesting to you. In this episode, we are going to show you some simple ways to do exactly that.By the way, if you like this podcast, you will like our free guides. We take the most popular topics from past episodes - this can be fixing projects that are not going well, receiving the recognition you deserve, building intuitions on different types of models and machine learning methods. We condense each topic into a short PDF, which you can use as a quick reference in your daily work to practice skills and develop strong intuitions on these topics. You can get these free guides at www.nds.show.

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 scientists out of their jobs, or anything in between. In this episode, we talk about the state of the art AutoML, what is hype versus what is reality, how to think about it practically, and how you can get started with AutoML in your team. If you like this podcast, you will like our free guides. We take the most popular topics from past episodes - this can be job hunting misconceptions for data scientists, top mistakes in data science team communications, understanding AutoML in plain English, and many other topics. We condense each topic into a short PDF, which you can use as a quick reference in your daily work to practice skills and develop strong intuitions on these topics. You can get these free guides at www.nds.show.

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 challenges. Some practical ethic codes for data scientists.

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 in writing and when not to. Handling difficult conversations.

Systems thinking to make sense of your data science work. 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 to change a system without breaking your back.

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.

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 question. How to think like a detective.

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.

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 instead of errors.

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 experience in building tech, product teams, and big data architectures. He is the Chief Technology & Product Officer at ScaleForce, previously head of software engineering, head of product, and lead of innovation lab at trivago, as well as CTO and founder of venture-backed start-ups and scale-ups.Access 40 years of combined SaaS experience at: https://www.scaleforce.services/

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 questions are, why it makes sense to fight questions with questions, and how you can use them to unstuck your team and yourself.

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.

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-making in their organizations.

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.

How to use the Puzzle Mapping technique to lead project kickoff meetings effectively, so that you can come up with concrete and feasible plans that everyone is happy about. You can download an example Puzzle Map here. It is much easier to understand this technique when you see the example. Enjoy the episode.

Why you should try five sub-optimal solutions instead of aiming for the optimal solution, why it is often better to write lower quality code at the beginning, and the importance of having discipline when you take shortcuts.

How to evaluate new versus baseline when you already have an existing solution, how to use tracer bullets when there is no existing solution, and how to build accurate intuitions on both data science and business sides.

Why it is important to avoid simplistic labels of maturity, how to measure competencies, the two natural ways to give feedback to data scientists, and the four key factors for creating development opportunities for your team.

Min's journey from an individual contributor to a team lead, the importance of being explicit about uncertainty, how to get the most value out of offline evaluations, and other lessons she learned along the way. This is a guest interview episode with Min Fang. Min was trained as a computational linguist, worked as a data scientist, and became a team lead of data scientists and software engineers. She is interested in data-driven problem solving by applying natural language processing, machine learning, and statistical analyses. She also enjoys building strong teams that deliver these data-driven solutions.

How to apply it to find common language between business and data science people, how to avoid the pitfall of shiny solutions, translating complex business needs to tangible requirements, and making your work more meaningful.

Why you can't only rely on existing methods to evaluate your work, what to do when you don't have evaluation data, what to do when there is no ground truth, why some mistakes are much more important than others, and the importance of ongoing evaluations.

The state of data science and data engineering job market in Amsterdam at the start of 2020. What profiles are the hardest to find, career development path, why data scientists and engineers leave their current jobs, ideal team size for tech leads that want to stay hands-on, how many years of experience you need to become a senior data scientist, and advantages of becoming a hybrid.This is a guest interview with Angus Mackintosh from Orange Quarter. Check out their tech and digital recruitment services at https://orange-quarter.com/

"Am I doing real data science work?" That is a question we hear too often from data scientists. And that is a problem because as long as you are not sure yourself, you can be easily distracted from doing what is really important. In this episode, we share with you what we see as real data science work. It is not a popular definition, because it gets outside the bubble of data science blog posts. But it will show you a clear direction on how to create meaningful impacts in the real world.If you like this episode, you will like our insider's guides on how to solve data science problems like a detective. We are also sharing new materials and training every week. Get these free insider's guides today.

The role of data scientists and how businesses approach data science are changing rapidly. Meanwhile, the gap between data science tutorials and real-life problems is getting bigger and bigger. What this means is that if you only focus on developing technical skills and theoretical knowledge, there is a chance that your job won't be there a few years from now. In this episode, we talk about how you can make yourself future proof and some other questions that people are too afraid to ask. If you like this episode, you will like our insider's guides on how to solve data science problems like a detective. We are also sharing new materials and training every week. Get these free insider's guides today.