Principle #1 — Metalearning

Begin with a plan. Before embarking on an Ultralearning project, investigate the most efficient methods to learn the desired subject or skill. Identify the best strategies for effective learning.

To facilitate this, I've curated a personal curriculum using various online resources. Numerous platforms provide structured curricula that can mirror traditional college courses. The resources below can guide you in understanding a typical Data Science curriculum, allowing you to acquire knowledge equivalent to that found in formal education without the associated costs. Alternatively, you may choose to enroll in online classes for a more structured approach.

Here are some platforms to explore for curricula:

• OpenSourceDataScienceMasters
• Coursera
• Udemy
• 365DataScience

Principle #2 — Focus

Prioritize your time. Designate specific periods for focused learning, ensuring minimal distractions.

Setting aside uninterrupted time is crucial for effective learning. Create an environment free from interruptions by disabling your phone, turning off the TV, and closing notification-heavy applications (like Discord and Twitter). Inform those around you that you need some quiet time to concentrate.

Principle #3 — Directness

Engage in hands-on experiences. Strive to learn by directly practicing the skills you want to master, rather than opting for more convenient alternatives.

While theoretical knowledge is beneficial, practical experience outweighs it. Although embarking on projects may feel daunting at first, the learning gained from project-based work far exceeds that from passive lectures. Regardless of how you gather necessary information, the experience of completing a project will ultimately be more rewarding than simply absorbing information.

Principle #4 — Drill

Address your weaknesses. Decompose complex skills into manageable parts, and focus on improving your weak areas.

This principle can be broad or specific. When studying, whether through reading or watching videos, identify your challenging areas first. Allocate more time—about 60%—to tackle these challenges. Understanding what you don't know allows you to revisit those topics using different sources or even integrate them into your next project. This approach forces you to confront and overcome your weaknesses.

Principle #5 — Retrieval

Test your knowledge. Utilize assessments to reinforce your learning and actively recall information.

As a continuous learner, it's easy to forget material you've encountered previously. Although this might seem acceptable if you don’t plan to use the information soon, it poses a risk if you suddenly need it. To counter this, incorporate regular self-testing into your routine. This will ensure you retain what you've learned and identify gaps in your knowledge.

Principle #6 — Feedback

Seek constructive criticism. Embrace feedback, regardless of how harsh it may seem, and focus on the key insights it provides for your learning journey.

This principle can be intimidating for many. Sharing your work—whether through a blog, website, or GitHub—can provide invaluable learning experiences. As a Data Scientist, collaboration and communication with peers are essential. While it may feel daunting, you might be surprised at how few people actually engage with your content.

Principle #7 — Retention

Prevent knowledge loss. Recognize what you frequently forget and take proactive steps to reinforce your memory.

Acquiring knowledge doesn't guarantee retention. A common scenario is learning enough to pass an exam but forgetting most of it shortly after. To combat this, engage in projects, document your learning experiences, and share your work. These practices will solidify your understanding far better than rote memorization for tests.

Principle #8 — Intuition

Build a deep understanding. Explore and engage with the material to foster intuition rather than relying solely on memorization.

Gaining theoretical knowledge is just the beginning; practical application is crucial. The more you engage with data exploration and programming, the deeper your intuition will grow. This foundational understanding is vital to mastering your subject.

Principle #9 — Experiment

Venture beyond your comfort zone. Embrace the idea that true mastery involves exploring uncharted territories.

Consider your capstone project: create something original that reflects your personal understanding and creativity. While it’s beneficial to draw inspiration from others, by this stage, you should have a clear idea of your passion within Data Science.

Resources to Jumpstart Your Learning

• Kaggle: A vibrant community for machine learning and Data Science competitions.
• Alex The Analyst: Offers insightful videos covering Data Science concepts and project ideas.
• Ken Jee: A leading voice in Data Science providing valuable career guidance and project insights.

Some Math Resources:

• 3Blue1Brown
• ritvikmath

Programming Resources:

• Dave Gray: Covers various programming languages including HTML, CSS, JavaScript, Node.js, React, and MongoDB.
• The Bro Code: Offers extensive tutorials on C, C++, C#, Python, Java, and JavaScript.
• FreecodeCamp: A comprehensive platform for learning programming and technical subjects.