Category: Learning, Education, & Training

An interactive version of the How to Think Like a Computer Scientist book

The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.

The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem solving skills.

Source: Table of Contents — How to Think like a Computer Scientist: Interactive Edition

When researchers get the message that they better not produce data that might offend the powerful, they end up telling us not what is true, but what we want to hear. Policy separates from reality, and we end up with waste and poor outcomes in education, healthcare, economics, and the justice system. Good policy cannot be built on comfortable fantasies.

Source: Universities need tenure to protect intellectual freedom — Quartz

Campus life is too diverse at most schools for dorms to serve as a place of respite from uncomfortable ideas.

Source: A College Is a Community But Cannot Be a Home – The Atlantic

Math Myth Conjecture: If one restricts one’s attention to the hardest cases, namely, graduates of top engineering schools such as MIT, RPI, Cal. Tech., Georgia Tech., etc., then the percent of such individuals holding engineering as opposed to management, financial or other positions, and using more than Excel and eighth grade level mathematics (arithmetic, a little bit of algebra, a little bit of statistics, and a little bit of programming) is less than 25% and possibly less than 10%.

Source: The Math Myth, Bryan Caplan | EconLog | Library of Economics and Liberty

We’d all agree that to teach a subject, you must know the subject. So you’d think that experts would be the best teachers, but they’re not…

In order to teach efficiently, experts try to cut right to the chase. They teach the Abstract Model. Why? Because, they’re trying to save you all the hassle of learning it, “The Hard Way”.

The problem is, as seen by our made up model, without Concrete experiences and many of them, it’s very difficult to understand the model.

Source: Why Experts Make Bad Teachers