DataCamping the Ultralearner Way

In this article, I will show you how the teaching methods of DataCamp can be combined with the principals of ultralearning, to make you a more successful data scientist.

First, it is necessary to define what is meant by „ultralearning“. Ultralearning is a term made popular by the book with the same title, written by Scott H. Young. In his book, Scott Young describes 9 principles to define what ultralearning is and what learning strategies an ultralearner uses.

The 9 principals described by Scott Young are the following:

  1. Metalearning – Learn how to learn
  2. Focus – Sharpen your ability to learn
  3. Directness – Go Straight Ahead
  4. Drill – Attack Your Weakest Point
  5. Retrieval – Test to Learn
  6. Feedback – Don’t Dodge the Punches
  7. Retention – Don’t Fill a Leaky Bucket
  8. Intuition – Dig Deep Before Building Up
  9. Experimentation – Explore Outside Your Comfort Zone

Below I will describe the 9 principals of ultralearning in a nutshell and how you can use them when you are learning data science on DataCamp. If you are interested in gaining further insight into what ultralearning is, I recommend to read the book. If you don’t know what DataCamp is, please check out the site here:


This is where you find out how to learn the subject you want to master. It is suggested to spend about 10 percent of your time you want to put into your learning project, on preparing how you will learn. Typical questions to ask yourself are: What are the typical ways to learn this subject or skill?  What kind of concepts, principals and facts should I master? Often, it is helpful to talk to people who are already experts in the field and ask them what concepts, facts and skills are important to master to be successful in the specific field.

Here DataCamp is already doing the job for you! The reason is, DataCamp already has defined career and skill tracks, that give you the necessary knowledge and programming skills you need to become a successful data scientist. Also, DataCamp has some of the best data scientist offering courses. They come both from academia (theory) and from companies (practice).

For you as a data scientist or somebody that wants to become a data scientist, DataCamp already shows you which skills you need. Of course, it is always good to use additional learning resources (what I highly recommend anyway), but DataCamp already gives you good ideas where to look and shows you who the top people working in the field of data science are.


How focused are you when you are learning? How can you improve your focus? What changes should you make to your environment, to help you concentrate and lower distractions? Does my learning schedule make sense? Am I procrastinating, and if so, why am I procrastinating? These are all questions you can ask yourself and try to solve to become more focused when learning.

Learning to become better at focusing is something that you have to practice by yourself. Nevertheless, I think that DataCamp helps you focus because of the XP System it uses. Gamification – which is basically used by DataCamp – helps you to focus because you want to achieve a certain goal in a certain amount of time.


Ask yourself, if what you are learning will really add value to your everyday tasks. If what you are learning does not have usage in real life, you are probably wasting your time and not improving on, or learning skills that you want to use to solve important problems.

DataCamp offers different approaches to make sure that what you learn is practical and has a direct usage in your everyday data science problems.  By using actual data sets and real world problem solving approaches, you can directly use what you have learned in your own projects. You also have the possibility of picking courses that teach exactly what you want to learn. It gives you the chance to pick the subject you are currently interested in. You don’t have to follow a defined career or skill track; you can jump right into a course that teaches you the concepts and tools you need to solve a problem you are currently facing.


When you drill, you try to improve on an aspect in your skillset that you are very weak at. You isolate the subject that you have trouble with and focus your cognitive energy on that single aspect. By doing so, you will reduce the mental load on your brain and be able to improve your weakest points with laser focus. Drilling helps you to remove learning bottlenecks that could keep you from getting better. You break down complex subjects into smaller parts and concentrate on them alone. Once you fully understand how the isolated subjects work, you increase the complexity.

DataCamp offers several opportunities to drill what you have learned, so coding becomes second nature. This will help you to move faster from a beginner to a skilled programmer. One of the easiest ways to drill what you have learned, is to use the Daily Practice exercises that DataCamp offers. These are also available on mobile devices. You practice what you have learned and can concentrate on areas that are the most difficult for you to understand. You can also download the scripts of the courses and go through the parts step by step, that are the most challenging to you.


Be careful not to spend most of your time reading passively. Try to recall what you studied to keep it in your long term memory! Challenge yourself to solve problems and retrieve information without going through your notes or googling the answer.

By doing the exercises after you have been taught the subject by the instructor, you retrieve what you have learned. This helps to keep the information in your long term memory. It is not enough to retrieve what you have learned once. You need to retrieve (use) what you have learned on a regular basis. Good practice is to start working on a project where you need the skills that you have learned. It is also useful to print out the scripts and go through them on a regular basis.


The good thing about how the exercises are done in DataCamp is that you get instant feedback on your performance. You have to code first and then click on the “submit answer” button, after which you get instant information on your performance. If you can’t find the solution by yourself, you can ask for a hint. Even if the hint doesn’t get you on track, you can ask for the answer.


It is important to use what you have learned, so you will keep it in your long term memory. Constant and regular exposure to what you have learned is important. Use what you have learned and make sure it sticks. Use it or lose it!

This is where the DataCamp projects come in handy. They test your skills and force you to use what you have learned in order to be able to answer the questions.


Do you have a deep understanding of what you have learned? Do you understand the core concepts and not just the facts. If you can only repeat information without understanding the underlying concepts and principals, you will not be able to build intuition. If you want to test yourself if you have gained a deeper understanding, then teach it to others our try to explain a concept you are learning to yourself, in your own words.

This is a principal that you have to implement by yourself. Nevertheless, DataCamp helps you to gain intuition by presenting a lot of concepts and ideas that you can then explore further. It gives you a lot of challenging questions to think about and shows you methods you can use to solve them.    


Experimentation is the final principal of ultralearning. This is the one principal where you have to leave DataCamp to further improve!

Experiment with what you have learned. Use the skills you have acquired and try to solve different problem sets with them. Figure out how to solve problems more efficient and effective by looking for more applications. Think outside the box.  Apply your newly acquired skills in the real world.

For example, I successfully use regression to build cost models. I use these in negotiations with suppliers to establish objective criteria for price negotiations. Here, I am able to combine a hybrid set of unrelated skills. I use my domain expertise of supply chain management and combine it with my newly acquired data science skills.


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