Real science education teaches skills not just facts

Science is a verb not just a noun.
This idea is expanded upon in detail in the introduction to Phil Nelson's beautiful new textbook Physical Models of Living Systems.

“Science is not just a pile of facts for you to memorize. Certainly you need to know many facts, and this book will supply some as background to the case studies. But you also need skills. Skills cannot be gained just by reading through this (or any) book. Instead you'll need to work through at least some of the exercises, both those at the ends of chapters and others sprinkled throughout the text. Specifically, this book emphasises  

Model construction skills: It's important to find an appropriate level of description and then write formulas that make sense at that level. (Is randomness likely to be an essential feature of this system? Does the proposed model check out at the level of dimensional analysis?) When reading others' work, too, it's important to be able to grasp what assumptions their model embodies, what approximations are being made, and so on. 

Interconnection skills: Physical models can bridge topics that are not normally discussed together, by uncovering a hidden similarity. Many big advances in science came about when someone found an analogy of this sort. 

Critical skills: Sometimes a beloved physical model turns out to be. . . wrong. Aristotle taught that the main function of the brain was to cool the blood. To evaluate more modern hypotheses, you generally need to understand how raw data can give us information, and then understanding.  

Computer skills: Especially when studying biological systems, it's usually necessary to run many trials, each of which will give slightly different results. The experimental data very quickly outstrip our abilities to handle them by using the analytical tools taught in math classes. Not very long ago, a book like this one would have to content itself with telling you things that faraway people had done; you couldn't do the actual analysis yourself, because it was too difficult to make computers do anything. Today you can do industrial-strength analysis on any personal computer.  

Communication skills: The biggest discovery is of little use until it makes it all the way into another person's brain. For this to happen reliably, you need to sharpen some communication skills. So when writing up your answers to the problems in this book, imagine that you are preparing a report for peer review by a skeptical reader. Can you take another few minutes to make it easier to figure out what you did and why? Can you label graph axes better, add comments to your code for readability, or justify a step? Can you anticipate objections?  

 You'll need skills like these for reading primary research literature, for interpreting your own data when you do experiments, and even for evaluating the many statistical and pseudostatistical claims you read in the newspapers.  

 One more skill deserves separate mention. Some of the book's problems may sound suspiciously vague, for example, ‘Comment on. . . .’ They are intentionally written to make you ask, ‘What is interesting and worthy of comment here?’ There are multiple ‘right’ answers, because there may be more than one interesting thing to say. In your own scientific research, nobody will tell you the questions. So it's good to get the habit of asking yourself such things.“

Unfortunately, most courses [including ones taught by me!] don't do the above.
I am slowly reading through the book. [Phil kindly sent me a free copy].
It confirms my earlier suggestion that this is a course that every science undergraduate should take.