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ANA BELL: All right.

Let's begin.

As I mentioned before, this lecture

will be recorded for OCW.

Again, in future lectures, if you

don't want to have the back of your head show up,

just don't sit in this front area here.

First of all, wow, what a crowd, you guys.

We're finally in 26-100.

6.0001 made it big, huh?

Good afternoon and welcome to the very first class of 6.0001,

and also 600, this semester.

My name is Ana Bell.

First name, Ana.

Last name, Bell.

I'm a lecturer in the EECS Department.

And I'll be giving some of the lectures for today,

along with later on in the term, Professor Eric Grimson, who's

sitting right down there, will be giving some of the lectures,

as well.

Today we're going to go over some basic administrivia,

a little bit of course information.

And then, we're going to talk a little bit

about what is computation?

We'll discuss at a very high level

what computers do just to make sure we're all

on the same page.

And then, we're going to dive right into Python basics.

We're going to talk a little bit about mathematical operations

you can do with Python.

And then, we're going to talk about Python variables

and types.

As I mentioned in my introductory email, all

the slides and code that I'll talk about during lectures

will be up before lecture, so I highly

encourage you to download them and to have them open.

We're going to go through some in-class exercises which will

be available on those slides.

And it's fun to do.

And it's also great if could take notes about the code just

for future reference.

It's true.

This is a really fast-paced course,

and we ramp up really quickly.

We do want to position you to succeed in this course.

As I was writing this, I was trying

to think about when I was first starting

to program what helped me get through my very

first programming course.

And this is really a good list.

The first thing was I just read the psets as soon

as they came out, made sure that the terminology just sunk in.

And then, during lectures, if the lecturer

was talking about something that suddenly I remembered,

oh, I saw that word in the pset and I didn't know what it was.

Well, hey, now I know what it is.

Right?

So just give it a read.

You don't need to start it.

If you're new to programming, I think the key word is practice.

It's like math or reading.

The more you practice, the better you get at it.

You're not going to absorb programming

by watching me write programs because I already know how

to program.

You guys need to practice.

Download the code before lecture.

Follow along.

Whatever I type, you guys can type.

And I think, also, one of the big things

is if you're new to programming, you're

kind of afraid that you're going to break your computer.

And you can't really do that just by running Anaconda

and typing in some commands.

So don't be afraid to just type some stuff in

and see what it does.

Worst case, you just restart the computer.

Yeah.

That's probably the big thing right there.

I should have probably highlighted it,

but don't be afraid.

Great.

So this is pretty much a roadmap of all of 6.0001 or 600

as I've just explained it.

There's three big things we want to get out of this course.

The first thing is the knowledge of concepts,

which is pretty much true of any class that you'll take.

The class will teach you something through lectures.

Exams will test how much you know.

This is a class in programming.

The other thing we want you to get out of it

is programming skills.

And the last thing, and I think this

is what makes this class really great,

is we teach you how to solve problems.

And we do that through the psets.

That's really how I feel the roadmap of this course

looks like.

And underlying all of these is just practice.

You have to just type some stuff away and code a lot.

And you'll succeed in this course, I think.

OK.

So what are the things we're going to learn in this class?

I feel like the things we're going learn in this class

can be divided into basically three different sections.

The first one is related to these first two items here.

It's really about learning how to program.

Learning how to program, part of it

is figuring out what objects to create.

You'll learn about these later.

How do you represent knowledge with data structures?

That's sort of the broad term for that.

And then, as you're writing programs,

you need to-- programs aren't just linear.

Sometimes programs jump around.

They make decisions.

There's some control flow to programs.

That's what the second line is going to be about.

The second big part of this course

is a little bit more abstract, and it

deals with how do you write good code, good style,

code that's readable.

When you write code, you want to write it such

that-- you're in big company, other people will read it,

other people will use it, so it has

to be readable and understandable by others.

To that end, you need to write code

that's well organized, modular, easy to understand.

And not only that, not only will your code

be read by other people, but next year, maybe,

you'll take another course, and you'll

want to look back at some of the problems

that you wrote in this class.

You want to be able to reread your code.

If it's a big mess, you might not be able to understand--

or reunderstand-- what you were doing.

So writing readable code and organizing code

is also a big part.

And the last section is going to deal with-- the first two

are actually part of the programming in Introduction

to Programming and Computer Science in Python.

And the last one deals mostly with the computer science part

in Introduction to Programming and Computer Science in Python.

We're going to talk about, once you have learned

how to write programs in Python, how do

you compare programs in Python?

How do you know that one program is better than the other?

How do you know that one program is

more efficient than the other?

How do you know that one algorithm

is better than the other?

That's what we're going to talk about in the last part

of the course.

OK.

That's all for the administrative part

of the course.

Let's start by talking at a high level what does a computer do.

Fundamentally, it does two things.

One, performs calculations.

It performs a lot of calculations.

Computers these days are really, really fast,

a billion calculations per second is probably not far off.

It performs these calculations and it

has to store them somewhere.

Right?

Stores them in computer memory.

So a computer also has to remember results.

And these days, it's not uncommon to find computers

with hundreds of gigabytes of storage.

The kinds of calculations that computers do,

there are two kinds.

One are calculations that are built into the language.

These are the very low level types

of calculations, things like addition,

subtraction, multiplication, and so on.

And once you have a language that

has these primitive calculation types, you, as a programmer,

can put these types together and then define

your own calculations.

You can create new types of calculations.

And the computer will be able to perform those, as well.

I think, one thing I want to stress--

and we're going to come back to this

again during this entire lecture, actually--

is computers only know what you tell them.

Computers only do what you tell them to do.

They're not magical.

They don't have a mind.

They just know how to perform calculations really,

really quickly.

But you have to tell them what calculations to do.

Computers don't know anything.

All right.

We've come to that.

Let's go into the types of knowledge.

The first type of knowledge is declarative knowledge.

And those are things like statements of fact.

And this is where my email came into play.

If you read it all the way to the bottom,

you would have entered a raffle.

So a statement of fact for today's lecture

is, someone will win a prize before class ends.

And the prize was a Google Cardboard.

Google state-of-the-art virtual reality glasses.

And I have them right here.

Yea.

I delivered on my promise.

That's a statement of fact.

So pretend I'm a machine.

OK?

I don't know anything except what you tell me.

I don't know.

I know that you tell me this statement.

I'm like, OK.

But how is someone going to win a Google Cardboard

before class ends, right?

That's where imperative knowledge comes in.

Imperative knowledge is the recipe, or the how-to,

or the sequence of steps.

Sorry.

That's just my funny for that one.

So the sequence of steps is imperative knowledge.

If I'm a machine, you need to tell me

how someone will win a Google Cardboard before class.

If I follow these steps, then technically,

I should reach a conclusion.

Step one, I think we've already done that.

Whoever wanted to sign up has signed up.

Now I'm going to open my IDE.

I'm just basically being a machine

and following the steps that you've told me.

The IDE that we're using in this class is called Anaconda.

I'm just scrolling down to the bottom.

Hopefully, you've installed it in problem set zero.

I've opened my IDE.

I'm going to follow the next set of instructions.

I'm going to choose a random number between the first

and the nth responder.

Now, I'm going to actually use Python to do this .

And this is also an example of how just

a really simple task in your life,

you can use computers or programming to do that.

Because if I chose a random number,

I might be biased because, for example,

I might like the number 8.

To choose a random number, I'm going to go and say, OK,