A Re-Introduction to Fundamentals
In this series of courses, we don’t just revisit the basics. We redefine them. Whether you've studied mathematical, computational, or statistical thinking before, chances are the fixed-mindset design of your learning environment limited your potential to truly understand the material as well as your appreciation for its beauty. These courses are built from the ground up to facilitate genuine growth, using research-backed learning and teaching techniques that focus on fostering deep understanding, not just memorization.
Every part of these courses, from the structure of each lecture to the final exam, has been meticulously designed to create an environment where you can thrive. Rather than simply teaching formulas, code snippets, or statistical methods, we dive into the why—the intuition behind how these ideas were originally devised. You'll engage in problem-solving that reflects the creativity of the original thinkers, allowing you to reconstruct concepts like linked lists and statistical models, rather than just recalling facts for a test. It’s a transformation in the way you think. By the end, you'll approach challenges with the mindset and tools needed for real-world success.
This approach draws from my own journey. Even at a top institution like Carnegie Mellon, I often found myself chasing grades instead of understanding. Now, having rediscovered my passion for growth, I’ve designed these courses to provide the learning environment I wish I had. It's a space that nurtures curiosity, creativity, and real progress.
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Mathematical Thinking
Mathematics is more than just numbers and formulas—it's a way of thinking that sharpens your ability to solve complex problems with abstract and rigorous approaches. Traditional courses often reduce it to rote memorization, but this course is designed to help you rediscover the power of mathematical intuition and problem-solving, focusing on...
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Computational Thinking
Computational thinking focuses on understanding the principles behind algorithms and data structures, not just coding. It teaches us to break down problems, create efficient solutions, and apply these concepts across different contexts. This mindset is essential for leveraging technology to solve real-world issues.
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Statistical Thinking
Statistical thinking helps us interpret and analyze data, understanding probability distributions and spotting misleading statistics. This skill is vital in a data-driven world, enabling us to make informed decisions and avoid common pitfalls in data interpretation across various fields.
Why these topics?
Mathematical, computational, and statistical thinking each bring unique strengths to your problem-solving toolkit. Mathematical thinking sharpens your ability to approach problems rigorously and abstractly, offering tools like proofs and logical frameworks to ensure precision and clarity in reasoning. Computational thinking equips you with the mindset to break down complex tasks into manageable steps, optimizing processes and leveraging the power of algorithms. Statistical thinking, meanwhile, empowers you to navigate uncertainty, making informed decisions based on data and probabilities. Together, these three types of thinking enhance your ability to tackle a wide range of challenges, from abstract theory to practical, real-world problems.
This is especially important in the world of STEM, where trying to learn subject material without peak fundamentals is like trying to get better at soccer while having a terrible diet and exercise plan. No matter what you’re learning, revisiting and mastering these fundamentals is crucial for every single person and will go a lot further in your career than say, practicing leetcode problems.