How To “Ultralearn” Data Science, Part 1

What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.

By Benthecoder, Developer, Writer, Machine Learning and AI enthusiast.

Elon Musk, Bill Gates, Warren Buffet, and Mark Cuban. What is one thing that all these ultra-successful people have in common?

They are all ultralearners.

Elon Musk is a voracious learner who utilizes a semantic tree to remember all the things he reads. And he can absorb information from others.

Though the success and breakthroughs he achieved might seem impossible, we can apply his framework to our personal lives as well and realize dreams.

“One bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to.” — Elon Musk

Bill Gates and Warren Buffet are both incredibly curious and have an insatiable desire to learn.

Warren Buffett invests 80% of his time in reading and thinking in his career. Bill Gates reads a book a week and takes two-week vacations just for reading.

“Intellectual capital will always trump financial capital.” — Paul Tudor Jones, self-made billionaire entrepreneur, investor, and philanthropist.


What is ultralearning?

Ultralearning is a vigorous, self-motivated methodology for learning that enables people to quickly and efficiently master difficult skills.

With a structured approach to learning, they become ultralearners and achieve extraordinary feats that might seem implausible at first, but in fact, anyone can adopt this ultralearning strategy and realize unrealistic ideas and dreams.


Principles of Ultralearning


Metalearning — The process of learning how to learn.

Metalearning is an imperative concept when it comes to learning. It is the first step every person must take before initiating the learning process.

Learning is harder than it sounds, as many people think learning is just absorbing information at random, then spewing it out in a test.

But in reality, learning is tough. I say this because schools and colleges have turned into a business, and the act of learning has become a lost art. Students are merely studying to pass a test and no longer learning for the sake of learning. Ask a student “why” instead of “what” and they’ll likely struggle.

Concerning data science, watching hours of YouTube videos, reading dozens of programming books and registering for expensive courses online alone is not learning. A structured framework is required.

A deep understanding of the fundamentals is needed. And a big-picture understanding of the field.

Big Picture Understanding

Metalearning is all about looking for the big picture, then using it to devise your optimal learning strategy.

Big-picture thinking essentially means the complete, overarching story or idea. This phrase usually implies that one should think of the future, or think of other parallel factors, and not emphasizing the small details.

To grasp the concept of big-picture understanding, let’s apply this concept to data science. Data Science is the sexiest job of the 21st century. But most data scientists jump into the field without thinking clearly about what data science is all about.

In a nutshell, data science is uncovering the hidden value of data.

Data is growing exponentially, and data scientists are imperative to make use of all this data. From intuition-based decision-making, we are transitioning into fact-based decision-making.

Data scientists have the arduous task of helping organizations make the right decision with the insights they gather from the data.

Thus, using the right skills and the right datasets, a data scientist engenders a data-driven world.


Strategies for Metalearning Data Science

Photo by Tabea Damm on Unsplash

(1) Create a metalearning map

Break down your topic into three categories:

  • Concepts
  • Facts
  • Procedures


Concepts in data science would be training and education which are prerequisites for data science.

These include a solid foundation in mathematics (statistics, probability, linear algebra, and calculus), programming, machine learning, and AI, and business analysis.


Facts in data science will then be the textbook stuff involved in data science, such as the facts in mathematics and machine learning that you have to deeply understand to the point where you can teach it to other people.

Facts involved should not be memorized as formal education has brainwashed us to do but should be comprehended at the atomic level, where you can translate jargon into an easier language for the masses to understand.


Procedures involved are the fundamentals in data science — business understanding, data acquisition, and preparing (mining and cleaning), deployment, modeling, and visualization.

These fundamentals have subfields as well, and each is crucial in the procedure of discovering the value of data.

(2) Identify challenging aspects of learning

Data science is not an easy field. One has to have business acumen and great communication to be able to translate data into something that people can understand and comprehend well. To be a data scientist is almost like a translator of the messy data in the world.

A few challenging aspects of learning are mathematics, especially statistics that play a huge role in data science, machine learning concepts (which usually requires masters and doctoral degrees), and excellent communication.

This part is ambiguous and arbitrary as everyone struggles with different things. So, the above is just my opinion and does not apply to everyone else.

(3) Techniques for overcoming them

The Feynman Technique is highly effective when learning something as vast and intricate as data science. Machine learning concepts and facts in mathematics can be easily stored in your brain with this technique.

For communication, there is no one simple way to suddenly be better at communicating and becoming a world-class communicator. Some of us have the innate capabilities to convince people with our speech, and some of us learn it throughout life.

Communication, as with knowledge, is a skill we accumulate as we go through the ebbs and flow of life. One piece of advice for communication is to just use simple words instead of jargon so that all can understand the results of your data.

Also, be sure to personalize your presentation according to the audience, as data engineers have a better understanding of data science compared to a manager or stakeholder.

A framework you can use for communicating data

  1. Your understanding of the business problem.
  2. How to measure business impact — which business metrics do your model results align to?
  3. What data is or was available — if appropriate, reference which data would be helpful to collect.
  4. The initial solution hypothesis.
  5. The solution/model — use examples and visualizations.
  6. The business impact of the solution and clear action items for stakeholders.

(4) Establish your learning methodology

With the internet, learning data science is no longer expensive and limited. There are vast amounts of resources that have democratized data science for all kinds of people.

Akin to the problems faced when learning, this part is also dependent on the individual. Some might prefer to learn with videos on YouTube and others with online courses such as Udemy and Coursera.

Nonetheless, having a learning methodology is imperative so that you can personalize the way you learn, based on your preference and ability.

A good tip for learning data science is to just start with hands-on projects such as linear regression, logistic regression, decision trees, etc.

This is because many people make the mistake of focusing on mathematics and take in tons of facts but fail to acquire hands-on skills from the vital start.


Action Plan

Now that you have brief exposure to ultralearning, metalearning, and big-picture understanding. Spend 5–10 minutes reflecting on whether you have been learning with these concepts in mind. If not, there no better time to change than now. Formulate your metalearning strategy according to by following the 4 steps below and start learning the right way today!


Original. Reposted with permission.