"...the correct analogy for the mind is not a vessel that needs filling, but wood that needs igniting"
     - (Plutarch Essays)

Make progress towards independence

• Define problems rather than have them handed to you
• Gain comfort in reading research articles
• Take control over your own education

Learn to build your own insights

• Distinguish between observations and mere assertions
• Go through the slow process of constructing an insight in a way that fits your mind
• Use concepts in their natural contexts

Find your life's calling

• Learn what you're good at (and what your not good at)
• Learn what you love to do (and what you don't love to do)
• Learn where the world needs your particular talents

Look at biology from the perspective of bioinformatics

• Quantitative thinking -- use powerful tools knowledgably to distinguish meaning from random fluctuation
• Algorithmic thinking -- visualize what you need to do to solve a problem
• Computational thinking -- extend your powers over more than can fit in your mind

Make progress towards independence

Arguably, the most important function of a college education isn't to get a degree, nor to get you a job next year, but to help prepare yourself for the rest of your life. Just as learning to read and do arithmetic many years ago made you a more capable person in all aspects of your life, so can many higher order skills that few already possess upon entering college.

• Define problems rather than have them handed to you
  If you ever find yourself working a job where all the decisions are made for you, then it's probably a short term job. Prepare for automation to eventually push you to the unemployment line. Machines beat humans in well-defined repetitive tasks, while humans beat machines in fuzzy problems, where insight is required to figure out what the problem actually is. Like any other skill, finding sense in ill-defined problems takes practice. You will have it.
   
• Gain comfort in reading research articles
  The primary literature, for example research articles, is where you can find out for yourself what is known and make your own judgement as to what is true. But research articles are difficult to read. It's much easier to have someone else read them and tell you what is true. Textbooks do this. So do most web pages. Then why do you need to read research articles?
  
  I could write for hours on this... In fact, I have. In brief, whatever you end up doing, you will be called upon to make independent judgments (you'll also have the option of ignoring the call). Independent judgment requires separating observation from prior belief. Reading research articles gives you practice in doing this, useful even if you never again have contact with bioinformatics or even with science.
   
• Take control of your education
  Leave school, and you'll find nary a syllabus, nary a multiple choice exam, nary even a grade except in the most basic sense. Whatever you learn will be your choice (learning nothing is also a choice). Can you build your own syllabus? Compose your own exams? Grade yourself? If not, it's time to learn how. What you learn is of lesser importance than gaining the ability to do it yourself.

Learn to build your own insights

(Yes, this is pretty much the same objective as the previous, but it's important enough to state a second time with different details)

• Distinguish between observations and mere assertions
  You might think this distinction is obvious: the first shows you, the second tells you. However, people in this course have had great difficulties in keeping these two straight, no doubt because education as it is practiced generally guides students to have faith in Authoritative Sources. But authority makes no difference in scientific truth, and authorities are constantly in conflict. Making the distinction between observations and assertions is essential in doing science. You can argue that it is also essential for participants in an effective democracy.
   
• Go through the slow process of achieving an insight
  Most of you have already learned so much from school... and forgotten so much, never having really learned much of it in the first place. Those that learned it best did so by starting from confusion and slowly piecing together observations that led to a personal integration -- things making sense in your own way. We won't cover many concepts found in typical bioinformatics courses, but what is covered will be covered slowly, with your own hands.
   
• Use concepts in their natural contexts
  If you can't use what you know, then what you know is of little importance (or you don't really know it). As much as possible, you'll be given the opportunity to apply concepts of bioinformatics in new contexts, sometimes experiments, sometimes hypothetical problems.

Find your life's calling

Arguably, your most important task at hand is to figure out how to best spend the years available to you. Every course you take is an opportunity to go a step further, tasting some new field of endeavor and adding or subtracting it from your list of candidates. This is possible only if you get close enough to see clearly the actual activity typical of the field.

A major goal of this course is to put you in a position where you can participate in a research project, enabling you to grasp what it is about, and to tell whether this is something for you.

Look at biology from the perspective of bioinformatics

"Introduction to Bioinformatics"... You've no doubt taken many courses called Introduction to X where you've learned the basic concepts of X, enabling you to move on to Advanced X. You have a pretty good idea what X is going into the course, perhaps through colleagues who are X majors or a high school X course or perhaps even a hard-hitting TV show with a dashing Xologist as the main character.

That may be fine when X is a mature field with a well-defined body of knowledge.
X is not bioinformatics.

Some courses focus on how to use state-of-the-art (also known as soon-to-be-extinct) tools.
Not this one.

Some focus on the timeless precepts behind bioinformatic tools.
That's a different course.

We will focus instead on something you can take away and use now and twenty years from now the active perspective of bioinformatics. How to gain that perspective? The only way I know to help is to put you in situations where you can discover it for yourself, through problem-solving and through an actual scientific project. So that's what we'll do.

Much of the first half of the course will be devoted to getting you to a position where you can work productively on the Project. Most of the second half will be devoted to doing it. Throughout the semester and (if all works out) especially during your research project, you'll be practicing:

• Quantitative thinking
  Nature sets traps for the unwary, and in bioinformatics, she sets BIG traps. Quantitative thinking is our best defence. I'm not talking about fancy math. I've probably had fewer classes and know less higher math than most of you in this class. I mean simple high school math, applied daily to the world around you. Probability (i.e., creative counting) will pop up time and again through our bioinformatic travels.
   
  I don't count statistics as simple math, but many statistical tests can be understood intuitively, and they can protect us from idiocy. Unfortunately, without an intuitive understanding of them, they often produce idiocy themselves. Periodically we'll examine common statistics to try to understand what they really mean, then we'll use them to our advantage.
   
  Want more arguments as to why people interested in biology need to be able to use simple math?

  Cohen JE (2006). Mathematics is biology's next microscope, only better; biology is mathematics' next physics, only better. PLoS Biology 2:e439.

• Algorithmic thinking
  Problems are often difficult to think about until you break them down into smaller parts. And if you want to recruit a computer to help, there is no escaping this, as a computer knows only simple instructions, not global objectives. Thinking at an atomic level about things we may do routinely at a much higher level is an unfamiliar burden for most, one that must become more familiar to you.
   
• Computational thinking
  Without understanding how the psychologist's contraption works, a dog paws the lever (or maybe paws the air), hoping for a reward. Without understanding how computer programming works, biologists paw glitzy applications, hoping for results. Without the ability to program the computer to do what you want it to do (rather than allow it to program you), you're pretty much stuck on the sidelines to cheer.

  Those of you who have become comfortable in programming a computer will find it as difficult to imagine not being able to do so as to imagine not being able to do long division. Those of you who have never programmed will be delighted to discover that learning long division is the more difficult task. You who do not know a programming language will know one (BioBIKE) by the end of the course.

  The combination of a digital computer and a creative human can do amazing things that neither can do alone. In particular, it makes possible the exploration of massive amounts of information, which is at the heart of bioinformatics and increasingly at the heart of biology.

  However, this is not a course in computer programming. To learn how to make a computer work reliably, efficiently, and beyond reach of the idiocies of foolish humans is the subject of long study, a study few want to undertake. But just as you all learned how to write (shopping lists, if not great poetry), in this course you will all learn how to create for yourself basic but extremely useful shopping-list computer programs.
   
  Want more arguments as to why people interested in biology need to be able to program a computer?

  Elhai J (2010). Humans, computers, and the route to biological insights: Retaining our capacity for surprise. Journal of Computational Biology 18:857-878.