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MIT OpenCourseWare at PROFESSOR: Good morning. This is 600. So I hope any of you who thought
this was a different course find where you
really belong. My name is John Guttag. And I’ll be lecturing the
course all semester. OK. Today I want to accomplish
several things. Cover some of the administrative
details. Talk about the goals
of the course– what I hope you’ll learn. And then begin talking
about the conceptual material in the course. It will seem a little bit slow,
because it will be a little bit slow. I promise starting on Thursday
we’re going to pick up the pace considerably. So let’s start with the
strategic goals of the course. The official introduction
to course 6 is 601. Historically, students who
arrive at MIT with little or no programming experience
find 601 an ordeal. And the point of this is
to prepare freshman and sophomores for entering
course 6– that’s the Electrical
Engineering Computer Science department– in a gentler, kinder way. So that 601 is not so
much of a problem. I want to help students
feel justifiably– and I want to emphasize
the word justifiably– confident in their ability
to write small and medium sized programs. So at the end of the term, you
should all feel comfortable writing programs. The real theme of the course,
and what I spend most of the time on, is how to map
problems into a computational framework. There’s going to be an emphasis
on scientific problems, rather than, say,
commercial problems. But there will be some talk
about some non-scientific problems as well. How to take a problem that may
not at first blush appear to be attackable with the program,
and show you how to formulate the problem in such
a way that you can use computation to get insight
into the problem. It should not take
you very long. All of the problem sets involve
programming in Python programming language, which I’ll
say a little bit about later today. The first problem set,
basically, is getting Python installed on your
own computer. Most of the people will want to
just use whatever their own laptop is to do the
problem sets on. Don’t get fooled by the first
two problem sets into thinking this is a gut course. It’s not. It starts out gently
to lure you in. And then life gets pretty
hard pretty quickly. So don’t be fooled. The quizzes– and there will be two evening
quizzes and a final– are open book open notes. When you get to be my age, you
get very sensitive about how difficult it is to
remember things. And so, we won’t be asking
you to memorize stuff. The course is about solving
problems, knowing how to solve problems, not how much
you can remember. For many of you who are majoring
in biology or course 20, it’s going to be kind of a
shock that this course isn’t about how much you can remember,
but it’s about how well you can solve problems. And that’s what the quizzes
are really going to be focused on. Probably the most unusual thing
about this course is the collaboration policy, which
is liberal in the extreme. You can collaborate with anybody
you want on any of the problem sets. Not on the quizzes. But on any of the
problem sets. You can work with each other,
which is what I recommend. You can work with your parents,
if one of them happens to be a software
engineer. You can work with friends
in course 6. Whatever you want to do. The goal of the problem sets
is to help you learn. What we’ve seen in the past is
people who are a little, shall I say too collaborative, i.e.
they just copy the problem set from somebody else, live in a
fool’s paradise which comes crashing down at
the first quiz. People who don’t spend enough
time thinking about the problem sets themselves cannot
take the quizzes successfully. So it’s a fine line. But our goal is not
to be policemen. I tell my TAs, their job is to
help you learn, not to prevent you from, quote, cheating. So to solve that problem, we’ve
eliminated the concept of cheating on problem sets. There is no way to cheat
on problem sets. So just go and do them. There’s no textbook. We will be posting readings
on the web. For the most part, these will
be pointers to websites. Occasionally we’ll
post readings that we wrote ourselves. Doesn’t mean you shouldn’t
buy a textbook. In fact, there are a number
of Python texts. We’ll recommend a few of them. It might make sense to buy one
and bring it to a quiz, because it will have an index
that will let you look things up quickly, that
sort of thing. But again, a lot of students
never buy a text and do just fine. We will not be handing
out class notes on a regular basis. A lot of studies have indicated
that students learn more when they take their own
notes than when they are handed out. And so, as a matter of what I
think is good pedagogy, we don’t hand out detailed
lecture notes. We will be using, after today, a
lot of handouts with code on them, which we’ll
make available. But it’s not intended to make
any sense outside the context of lectures. It’s not self-contained. The main purpose of this course
is to help you become skillful in making the
computer do what you want it to do. Once you acquire this skill,
your first instinct, when confronted with many tasks, will
be to write a program to do that task for you. I always tell people I became
a computer scientist in part because I’m lazy. And there was a lot of stuff
that I found it was easier to write a program to make
the computer do it, rather than do it myself. So I do that a lot. If I need to do something, I
say, can I just write a quick program to do it? And I’d like you to be able
to acquire that skill. And remember, that programming
is actually a lot of fun. So I should say that, in
addition to learning a lot in this course, I hope most of
you will find it fun. Kind of a strange thought. MIT course, fun. Maybe it’s an oxymoron. But I don’t think so. I really do think you can have
a lot of fun writing programs for this course. There are many people
who believe that– how shall I say this? Programming is the most
fun you can have with your clothes on. It really can be a lot of fun. So think of it that way. All right. So the primary knowledge you’re
going to take away is computational problem solving. So to start with, we might
ask the question, what is computation? And to think about that– this is interesting. Ah, there’s where
the chalk is. I was afraid I was going to be
confronted with a sea of black boards and erasers
and no chalk. But there is chalk. So if we think about it,
there are essentially two kinds of knowledge. Declarative– and you’re going to see, I’m
not a great speller. And imperative. Declarative knowledge
is composed of statements of fact. For example, a good health care
plan improves the quality of medical care while
saving money. As we know from doings in
Washington, it’s a lot easier to state that goal than to know
how to achieve that goal. So the key thing about
declarative knowledge is, it says something that is true. Otherwise it wouldn’t be
knowledge, it would be misinformation. But doesn’t tell you
how to do it. In a more mathematical sense,
say y is the square root of x if and only if y times
y equals x. All right? Perfectly clear statement of
what it means to be the square root, but it doesn’t tell you
how to find the square root. Interestingly enough, it does
tell you how to test whether or not you have the answer
to the square root. And so if you had some way of
generating guesses, you can at least check whether
they’re correct. And in fact, starting in the
next lecture, we’ll talk about the fact that a lot of
computational techniques involve something called
guess and check. Where you have a way to generate
guesses and a way to check whether they’re right. Imperative knowledge, in
contrast, tells you how to solve a problem. How to accomplish something. So you could think of it as like
a recipe in a cookbook. So it’s one thing to say a
chocolate cake is something that tastes delicious
and is bad for you. That’s declarative knowledge. But you can open a cookbook and
get a recipe that tells you how to make a
chocolate cake. That’s imperative knowledge. Now, below, we have a recipe for
finding not a square root necessarily, but an approximation to a square root. And one of the themes of this
course is that a lot of problems we cannot solve
precisely, but we can find answers that are good enough
for practical purposes. And those are called
approximation algorithms. So here’s a way– this is a very old method for
finding the square root. In fact, it’s believed
that Heron was the one who did this. Heron of Alexandria. In the news much today,
Alexandria was the capital of ancient Egypt. He was the first one to
write this method down a long time ago. Though it’s believed that even
before Heron, the Babylonians know how to do it. So you start with a guess, g. Any old guess will do. Then you say, is g times
g close enough to x? If so, you stop. Say OK, I’ve got a good enough
approximation of the answer. If it’s not, you create a new
guess by averaging g and x divided by g. So g new is going to be
g old plus x divided by g old, over 2. And then using this new guess,
you go back to step 2. So let’s quickly run through
an example of this. We can start with this
pretty easily. We’ll take a guess. Let’s say g equals 3. So we look at three times 3. 9. And we say, is that
good enough? Well, let’s say we’re looking
for the root of 25. I guess I should have started
with the problem statement. Sorry about that. Well, 9 is probably not close
enough to 25 that we’re happy. May be good enough for
government work, but not for most other purposes. So we’ll reset g, and we’ll
set g to 3 plus 25 over 3. All of that over 2. Which equals 5.6666 et cetera. All right. So now we’ll multiply
that by itself. And that gets to
be about 32.04. Close enough to 25? Probably not. So we’ll take another step. And we’ll set g equal to– well, when we’re done with it
all, I’m not going to bore you with writing the
formula again. It’ll be 5.04. If we square that, it’s 25.4. We decide that’s close enough
to 25, and we’re done. What we say at this point
is that the algorithm– and that’s an important word. An algorithm is a description
of how to perform a computation. We say that the algorithm
has converged. Which is a fancy way
to say it’s halted. What we’ve got here, if you
think about it, is a set of instructions. Steps that can be executed
and a flow of control. The order in which
we execute them. So if we look at this, there’s a
default order of execution– 1, 2, 3, 4. But then there’s the go back
to step 2 and start over. And there’s a termination
condition. It tells us when to stop. And of course, that’s
important. I’ve always been amused, if you
look at a shampoo bottle, you’ll see an algorithm that
says something like lather, rinse, repeat. And if you follow it
literally, you never get to stop. Which I suppose make sense if
you’re selling shampoo, because people use
a lot of it. But really, there
ought to be some termination condition there. OK. So now, how do we capture this
idea of a recipe in a mechanical process? One way would be to design a
machine specifically to do square roots. So if I knew how to design
circuits, which I don’t, I could sit down– probably many
of you could sit down– and design a circuit that would implement this algorithm. And in fact, that’s more or
less what you’ll find in a cheap four function calculator
that does square roots. Not quite this algorithm, but
a similar algorithm is just part of the circuitry
to go compute that. And in fact, this used
to be the way that all computers worked. So the initial computers
were what are called fixed program computers. They were designed to do very
specific things, and that’s what they did. So for example, one of the very
first computers, designed in 1941, by Atanasoff and
Berry, solved systems of linear equations for the purpose
of plotting artillery trajectories. And that’s all it did. If you wanted to balance your
bank account with this computer, you couldn’t do it. But you could figure
out how to drop an artillery shell somewhere. Also during World War II, Alan
Turing built a machine specifically designed
for breaking the German enigma code. Actually a fascinating story of
science how that was built. But again, that was
all it could do. These computers were useful,
but only in a very limited way. The big breakthrough, the thing
it made computation really important to society,
was the invention of the stored program computer. It took people quite a while
to figure this out. But once they did,
it seems obvious. The basic notion of a stored
program computer is that the instructions are the
same as data. So now, there is no distinction
between the program that implements the
algorithm and the data on which that program operates. So there’s no difference between
the input of 25, part of the data, and the steps of
the algorithm used to do that. Once that was possible,
the machines became infinitely flexible. You could change the program
anytime you wanted. And furthermore, programs
could produce programs. Because programs can
produce data. And if program and data are
the same thing, that means programs can produce programs. And we were off and running. And that’s really what made
computers what they are today. Once this became clear as the
paradigm for computers, people began to think of the computer
itself as a program. And in particular, as a kind
of program called an interpreter. And we’ll get to more
on this later today. An interpreter is a program that
can execute any legal set of instructions. And consequently, can be used
to describe and accomplish anything you can do
with a computer. So roughly speaking, this is
what a stored program computer looks like. This is 6004 in 40 seconds. It’s got memory. Lots of it today. A control unit that basically
tells it what to do. For example, fetch some data
from memory, put some data into memory, send some output
to a screen, all of those kinds of things. What for historical reasons we
call the arithmetic logic unit, this is, in some sense,
the brains of the computer. The thing that actually
does computations. An accumulator, which
is part of the ALU that stores results. And a bunch of input
and output devices. The things that we actually see
when we use a computer. And that’s it. And again, the key thing to
notice is, there’s only one kind of memory. There’s not a memory for program
and a memory for data. There’s just the memory. The nice thing to think about
here is, given a small set of instructions, you can
then build any kind of program you want. So typically, the computers have
a very small number of built-in instructions. Order of dozens,
and that’s it. And by combining those
instructions in very clever ways, you can do arbitrarily
complex things. In much the same way a good
chef can take a very small number of ingredients, and from
those, produce a variety of interesting edibles. Alan Turing, in the 1930s– very famous British
mathematician of whom you will hear more– showed that, in fact, there
were six primitive instructions. Each of which operated on
one bit of information. And with those six primitive
instructions, you could do anything that could be
done with a computer. Kind of amazing. It was six instructions. There were things like
read, write, plus– I don’t know, maybe minus. I forget what they were. And that was it. That’s all you needed. We will not make you write
programs using only six instructions. We will give you a
much larger set. But still, it’s really
quite remarkable. It’s what makes programming
such an amazing endeavor. OK. So what instructions
will you be using? Well, that’s what a programming
language does. So a programming language
provides a set of primitive instructions. A set of primitive control
structures. So instructions and mechanisms
for controlling the order in which they get executed. And that’s all. And then you can do whatever
you want with them. And what distinguishes one
programming language from another is what these
things are. What are your instructions? What of your flow of control? And how do you combine them? What are the combining
mechanisms? And in fact, it’s the combining
mechanisms more than anything else that separate
one language from another. The most amazing thing
about programming– and this has its good side and
its bad side, and it’s something you need to remember
as you do the problem sets– is that the computer will always
do exactly what you tell what to do. It’s remarkable. You don’t have any friends
who will do whatever you tell them to do. I can tell you my children
certainly don’t do whatever I tell them to do. And my wife doesn’t either. Sometimes she probably
thinks I do whatever she tells me to do. But a computer will do what
you tell it to do. So that’s very empowering. It’s also very annoying. Because it means if your program
doesn’t work, it’s your own darn fault. You got nobody else to
blame but yourself. Because it’s not the
computer’s fault. You may want to curse the
computer, but you shouldn’t. It’s just doing what
you told it to. So be careful what
you wish for. All right. The programming language we’re
going to use in 600 is Python. It’s a relatively recent
addition to the universe of languages. I want to emphasize that
this course is not about learning Python. I will spend relatively little
time in the lectures telling you about Python. It’s about computational
methods, is what this course is really about. And Python is merely
a teaching tool. Once you learn to program in
Python, it’s easy to learn to program in another language. It’s a very easily transferable
skill. If we think about what defines
any programming language, it’s got a syntax, a static
semantics, and a semantics. Are any of you here linguistics
majors? Not a one. All right. Then I can make up whatever I
want about these terms and maybe you’ll believe me. All right. So the syntax tells us which
sequences of characters and symbols constitute a
well-formed string. So it would tell us, maybe, that
we could write something like x equals 3 plus 4. And that’s syntactically
correct. It’s well-formed. It might also tell us that
x equals 3 blank 4 is not syntactically correct. It’s not a legal string. So by analogy with English,
the syntax describes which strings of words constitute
well-formed sentences. Well-formed. Not necessarily meaningful. So it would tell you that some
sentence like Susan is building is syntactically
well-formed. It may not be very sensible. The static semantics tells
us which well-formed strings have a meaning. That are which strings
are meaningful. So you can think about that
as also making sense. So in Python, it might tell us
that some strings which are syntactically fine don’t
mean anything. So for example, it might tell
us that the string 3 divided by the character string abc is
syntactically well-formed because it’s value
operator value. Sort of like noun verb
noun is syntactically well-formed in English. But it would tell us
that there’s no real meaning to this. Dividing a number by a string
doesn’t mean anything. And so you would get an error
message saying the syntax is OK, but the static semantics
is broken. So for example, in English ,
the sentence I are big is somehow syntactically
well-formed– noun verb noun– but we might say it fails the
static semantic test. We don’t want to assign
a meaning to it. The semantics of the language
looks only at the strings that are both syntactically correct
and static semantically correct, and assigns a
real meaning to them. In natural language, sentences
can be ambiguous. So one of my favorites, when I
have to write a recommendation letter for a student that maybe
I don’t think is so good, I might say something
like I cannot praise this student too highly. Well, you can interpret
that however you want. It keeps me from getting sued,
but I can also claim, well, I don’t like the student at all. And English is full
of those things. Programming languages, in
contrast, are designed so that every well-formed program
has exactly one meaning. There’s no ambiguity. So you can’t typically
talk of a program as having a semantic error. If it is well-formed, it
means something, and that’s what it means. On the other hand, it’s easy
to talk about a program meaning something other than
you wanted it to mean. And you will discover in the
problem sets, most of the time the programs don’t mean what
you want them to mean. That is to say, when you run
them, they don’t give you the correct answer. And then you will go through
this process of debugging them and learning how to do it. So what might happen when we
write a program that doesn’t do what we want it to do? It might crash. By that, we mean stop running
and produce some palpable indication that it
has done so. So you’ve all used programs
that have crashed, right? You sat there using your email
program or Word, or PowerPoint, or something. And suddenly, it
just goes away. And you get a message on your
screen and an invitation to send Apple or Microsoft a file
explaining what went wrong so they can fix it. In a properly designed computing
system, when one program crashes, it does not
damage the overall system. So you’d like it to
just be local. What else might it do? It might never stop. Now, if you have no idea how
long a program is supposed to run, this can be hard
to diagnose. But again, I’m sure you’ve
all run into this. I’ve certainly run into it. Every once in a while I’ll
say, try and write a PowerPoint file. And it’ll just sit there. Or I’ll try to read a file and
it’ll just sit there and never finish the job. Or I don’t have enough
patience. But probably it would never
have finished it. Again, you will all write
programs that do this. It’s a good idea to know how
long you expect your programs to run, so that you can
recognize this. Typically, we say that these
programs have in them an infinite loop. And we’ll talk about that when
we get to flow of control. Finally, a program might run to
completion and produce the wrong answer. These problems are kind of in
ascending order of badness. If it crashes, at least
you know that something has gone wrong. An infinite loop can be very
annoying, because you just wait for a long time. But the worst thing that happens
is when you think everything is good
and it’s not. There have been lots of
examples of this. This is the sort of thing
that costs lives. There was a radiation therapy
machine that produced the wrong dosage of radiation and
actually killed quite a few people, because they put in
the correct input, and it would dose the patient
with radiation. And a fatal dose of radiation. That’s a really bad mistake. There are buildings that
collapse because people run programs that do the structural
engineering, and the programs give the
wrong answer. Lots of bad things can happen. So one of the things we’re going
to spend time on this term is, what you can do to
avoid writing programs that have this rather unpleasant
property. How do you test them? How do you write them in such
a way that this is the least likely event? That’s not what you
want to happen. OK. Some programming languages
give you a lot of help in avoiding these things. Python is kind of mediocre
in that respect. It’s not the best. It’s not the worst. It’s somewhere in the middle. Because what you’d like is a
program with very rigorous static semantics, such that if
you pass those tests, it has a high probability of behaving
as expected. So for example, it’s a good
thing that Python doesn’t allow you to do this. Because who knows what
that’s going to do? Something weird. You’d rather be told no,
you can’t write that. And then you have to write
something that’s more obviously meaningful. Rather than it just making
up an interpretation. As we will see going forward,
Python is not, for example, as good as Java is at weeding
out meaningless things. Or things that have surprising
meanings. On the other hand, it’s better
than C. So kind of in the middle as these programming
languages go. Why do we use Python in this
course if it’s not the best in that respect? It’s got several
good features. One of them is, it’s
easy to learn. It’s much less complicated
than, say, Java. So the learning curve
is much steeper. That’s a good thing. You get up to speed faster. It’s very widely used today in
a lot of areas of science, particularly the
life sciences. It has probably become the
most popular language in biology and the other
life sciences. And therefore, for those of you
who have careers in that area, it’s the most useful
language to know. It’s also widely used in
other areas as well. It is easier to debug
than most languages. And the reason it’s easier to
debug than most languages, or than many, is it’s an
interpreted language. So you’ll remember, I talked
about a computer as an interpreter. Something that you feed in a
bunch of instructions, called the source code. You do some checking. And then it executes the
instructions, including the flow of control instructions. Produces some output. The nice thing that goes on
there is if something untoward happens, the interpreter can
describe in the language of the source code what
went wrong. The source code is the
code that you wrote. On the other hand, the way a
compiler works is, you take the source code, you check it,
but then you translate it into another language called
the object code. This is a language closer
to the language that the computer, the hardware, knows
how to interpret. Then the hardware interpreter
interprets the compiled code, the object code, and
produces output. And the problem here is if
something goes wrong, it wants to give you an error message in
terms of the object code, which you’ve never seen
in your life. And that can make
it very obscure. So the advantage? Why do we have compilers? Typically, compiled languages
are more efficient. Because they go through this
extra step, they take less time to run those programs. You can compile Python as well,
if you want to get an efficient version. But it’s not designed under
that assumption. And so, it works well when
it’s interpreted, which is why we use it.

Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

100 thoughts on “Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

  • September 3, 2013 at 4:36 am

    Good thing there weren't any linguists in the room. "I are big" is not semantically correct or syntactically correct. ^^'

  • September 5, 2013 at 12:56 pm


  • September 7, 2013 at 6:14 pm

    I have to make this comment. I tried unsuccessfully to take Java twice at a community college. Don't get me wrong, I had A's both tries, but also withdrew both times. Why? I knew I wasn't learning the proper way to code by using computational interpretation. I knew that my instructors were missing an important link, which was showing students how to write code and algorithms before even touching the language. I knew I didn't have the foundation…I just found it with this course. THANKS.

  • September 15, 2013 at 9:39 pm

    Makes the cs program I am in obsolete, time to transfer to a better program

  • September 17, 2013 at 12:42 pm

    Many thanks to MIT.

  • September 20, 2013 at 5:20 pm

    Someone in that classroom was asleep.

  • September 23, 2013 at 4:14 am

    you get an A in a class you're paying MONEY for then withdraw because you're unhappy with the teaching method…twice?

  • October 4, 2013 at 10:58 am

    Quick, question: What does the "SC" mean at the end of the course code? I've noticed it at the end of several course codes. How is it different from courses without the SC?

  • October 26, 2013 at 10:41 am

    Thanks for : MIT & Youtube ! Awesome courses I never seen before !
    La maitrise totale des nouvelles technologies est assurée avec ces merveilleux cours et ce qui constitue un PLUS c'est que nous pouvons (En traduisant en Français) avoir les deux Versions du Cours: Anglaise & Française ! Merci !

  • November 17, 2013 at 12:03 pm

    Great project. Solves a lot of inadequate tutions

  • November 17, 2013 at 9:48 pm


  • December 9, 2013 at 9:05 pm

    I'm 13 years old looking to get into Gloucester County Institute Of Tech. and looking to also get into MIT, I understand every he is saying. I started programming when i was 6 years old and am taking a full class in C++ and Assembly

  • December 13, 2013 at 7:37 pm

    I think that is was until this decade that the original goal of Steve Jobs and Apple when they came up with personal computing really came true, computers are cheap enough, mature enough, are taken seriously, they are everywhere (even in your phone), and of course the Internet has everything you may need so that people have access to this technology as early as 6 years old and they can learn programming instead of watching tv or riding a bike, I really envy the kids nowadays, happy programming, and don't learn how to be a prick.

  • December 27, 2013 at 4:02 am

    Thank you very much for offering this course for free.  This is the first class I'm attempting in this format and I've got say that I'm very excited to give it a try.

  • January 4, 2014 at 10:29 pm

    This platform for delivering a lecture is not good in a browser…
    no ability to rewind a few seconds, skip a minute, or play at faster speeds… 
    this needs improvement.

  • January 20, 2014 at 12:13 pm

    very good in this program

  • February 13, 2014 at 2:16 am

    The professor sounds just like Michael Bloomberg.

  • March 22, 2014 at 1:07 am

    Hi, i want go trought this course, but which is better this or this one?

  • March 27, 2014 at 3:28 am

    Piotr Pogo: This one is better because it's made for online learning, check the mit ocw site for more material. They are both the same class but SC is supposedly made for self/online learning. 

  • April 20, 2014 at 5:16 pm

    I'm teaching my self programming and am trying to learn foundational concepts and things. I need to brush up on grade school math though lol.

  • May 16, 2014 at 7:31 am

    MIT-fun isn't oxymoron , but IIT- fun is.

  • May 28, 2014 at 11:27 pm

    Noun verb adjective, not noun verb noun

  • June 1, 2014 at 7:40 pm

    How about "John Guttag chides Will V"? Perhaps "Will V becomes dunderhead". He's trying illustrate the value-operator-value, cut the dude some slack, stop being a doofus.

  • June 7, 2014 at 10:29 pm

    video look vintage as fuck

  • June 9, 2014 at 3:47 pm

    Hey I love the lectures

  • June 28, 2014 at 2:17 am

    Man… As much as I love computers and as much as I LOVE programming, the rest of computer science scares me…… I may have to switch majors..

  • June 30, 2014 at 7:04 pm

    Fellow in 2008. Fall semester was nice john

  • July 12, 2014 at 7:38 am

    I just love how professors know how to make lectures about science fun XD

  • July 18, 2014 at 1:24 pm

    I noticed that there are two courses with the name "Introduction to Computer Science and Programming". One is the course 6.00 and the other is 6.00SC.
    Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011
    Lec 1 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008
    Can anyone tell me the difference between the two?

  • August 6, 2014 at 9:26 pm

    Why is the volume so low?

  • October 22, 2014 at 11:48 am

    Can anyone tell me how to write a FORTRAN program to calculate factorial of a number gteater than 12 in a 64 bit computer.(I wrote a recurssive program but it gave correct restlt upto 12!.
    But frem 13! it gave wrong result…
    My program is,

    write(,)'enter the number'
    do i=1,n
    write(,)'value of factorial=',fact

    What correction should be made to get correct values if factorial of numbers greater than 12?

  • December 28, 2014 at 6:27 am

    Was the "most fun with your clothes on" part really necessary?

  • January 18, 2015 at 1:11 am

    if compiled programming takes an extra step shouldn't the program take longer to run?

  • February 28, 2015 at 3:06 pm

    Once again we see something promoted as "Introductory"-and it is not. This course presumes no small amount of prior knowledge. As such it is anything but-"introductory".

  • April 8, 2015 at 1:28 pm

    Coursera, edX, udacity, MS Virtual Academy, plenty of courses & languages

  • May 20, 2015 at 4:17 am

    I did some research and saw that this prof got his bachelors in English. That's really neat and unexpected.

  • June 6, 2015 at 9:50 am

    Programming is the most fun you can have with your clothes on…

  • July 14, 2015 at 5:14 am

    im twelve, i love this!!! am i normal?
    nope, im most certainly not normal

  • July 20, 2015 at 9:07 pm

    In his cookbook example which is imperative knowledge?
    Knowing to look in the cookbook,
    or the knowledge that has been written in the cookbook.

  • August 14, 2015 at 6:52 am

    Good stuff!

  • September 5, 2015 at 7:33 pm

    Programming is the most fun you can have with your cloths on!!!

  • October 14, 2015 at 4:22 pm

    I have some Python programming tutorials for the noobs. I thought it will be good supplement in learning Python programming.

  • October 17, 2015 at 5:39 am

    Best teacher I have found on the basics for computer science/ programming, and best of all he makes it exciting.

  • October 24, 2015 at 10:34 pm

    this guy is slaying my life rn <3 it's really fun

  • November 11, 2015 at 12:59 am

    Good intro, thanks MIT. – x2 really helps.

  • November 11, 2015 at 2:21 am

    this man is having a nightmare

  • December 22, 2015 at 2:21 am

    Did the volume somehow increase…quality seems better now

  • January 24, 2016 at 5:01 pm

    I think it would be helpful to suggest the students look up Wiki on Assembly Language so they can see instructions and data within a computer word.

  • January 24, 2016 at 5:04 pm

    In particular in wiki, they should look at
    Example listing of assembly language source code
    where they can see the actual hex instructions and how a word in memory is organized.

  • March 13, 2016 at 4:23 am

    how legit is this …. as a total high school dropout . thank you God bless you

  • March 16, 2016 at 2:48 pm

    thank you MIT

  • March 23, 2016 at 9:44 am

    +MIT OpenCourseWare what is the resoning behind using Python as opposed to C/C++?

  • March 24, 2016 at 12:46 am

    Dropouts… dropouts everywhere.

  • March 26, 2016 at 2:30 pm

    What is the textbook used in this course?

  • April 2, 2016 at 3:34 am

    It didn't seem like the lecture was quite over at 41:28.

  • April 27, 2016 at 5:09 am

    Is there a instructional course set for learning Java?

  • May 5, 2016 at 2:36 pm

    which programming is best one!

  • May 5, 2016 at 3:09 pm

    Hello MIT OpenCourseWare! I want to Learn Programming From start at home no matter how long it takes.
    Sequence I have in my mind is Introduction to programming, OOP, Data Structures and Algorithms.

    There are two many different courses without sequence.
    Can you list them in Sequence.
    Thanks a lot!

  • May 25, 2016 at 11:09 am

    What is the difference between 6.00 and 6.00SC?

  • June 22, 2016 at 1:45 am

    LMAO! Tough crowd! I love this guy!!!

  • June 28, 2016 at 3:00 pm

    Thanks MIT!

  • July 27, 2016 at 7:23 am

    I am using linux, what python version should i use?

  • August 25, 2016 at 10:46 pm

    Thank you for posting this series! Very interesting!

  • October 11, 2016 at 10:07 pm

    Excellent! its very usefull course

  • October 13, 2016 at 6:06 pm

    It would be great if we could move this video lecture series to Python 3.
    These problem sets are stuck in Python 2, material that is depreciated.
    It was not possible for me to fix Problem Set 5 to work in Python 3.
    I will thankfully refer to these lectures when I need extra help, but I have moved to codecademy python.

  • November 1, 2016 at 1:00 am

    MIT , Please wanna know what math courses should I study before programming courses

    question is in general ! you got me ?

  • December 1, 2016 at 11:30 am

    the most fun you can get with your cloths on

  • December 9, 2016 at 5:11 am

    I think the lectures 1 – 19ish is pretty interesting. talks about some problems and ways to solve them (short and concise). but from 19 on, just dreadful. i know the topic of optimization is hard. how about go straight to the point and talk about how to solve it. i wish there are just some good notes that emphasize the point in a short manner, and i can get to the interesting aspect asap. if i were really interested in clustering problems i would just pick up an algorithm reference book. nevertheless thank you for sharing these videos. I look forward to the datastructure algorithm and operating system course 🙂

  • December 9, 2016 at 5:25 am

    after scanning through the topics covered in the 2008 version. i would have to say, the 2008 version seems to cover much more "intro to computer science" topics than, this 2011 version, which is more similar to "intro to solve science problems through computer "

  • December 27, 2016 at 6:45 am

    What's the first course to study, 6.00 or 6.00SC???'

  • January 15, 2017 at 5:49 pm

    i want to get into cyber security field is this the best course to start with hope you replay ..

  • January 18, 2017 at 9:14 am

    thank you guys so much for courses like these.

  • February 3, 2017 at 3:31 am

    Thank you for uploading, appreciate it!

  • March 4, 2017 at 11:02 am

    What are the prerequisites for this course?

  • May 31, 2017 at 2:51 pm

    Dope upload 😍

  • December 17, 2017 at 2:38 pm

    where can i find videos of course Introduction to Programming in Java? course no 6.092

  • February 24, 2018 at 12:16 pm

    I got accepted into Devry. This course should help.

  • February 26, 2018 at 4:48 pm

    Is this discreet and

  • March 28, 2018 at 12:43 am

    Wow. 🎓 this gentleman is an excellent professor and person. His teaching styles are elite.

  • April 22, 2018 at 9:53 am

    As a non-MIT student (someone just learning at home), is this the right version of the course to learn from in 2018, or should we
    take 6.0001 which seems to be more recent (taught in 2016)?
    Link to course 6.0001 page:
    Really appreciate you guys making this content freely available.

  • May 17, 2018 at 1:19 pm

    Dont worry teacher we from the opencourse ware are crying out loud with your complex jokes

  • May 20, 2018 at 9:52 pm

    Am I the only person who likes to know this stuff as a hobby? 0-0

  • May 23, 2018 at 6:27 am

    9:38 Sees " y=sqrt(x) iff y^2 = x ". Leaves the course playlist.

  • May 28, 2018 at 4:28 pm

    26:31 – 27:00 "I'm sorry Dave, I'm afraid I can't do that"

  • June 23, 2018 at 9:11 am

    Thanks you are back. It was showing content is not available in your country.What was the problem.

  • August 2, 2018 at 4:39 pm

    i have yet to experience the joy of being a lazy computer programmer. all i ever experience is multi-thousand page technical manuals and outdated instruction that don't actually work due to a wonderful patch to fix something that correspondingly broke and equal or greater number of things…. and so-called 'support' representatives that don't know how to install their own software.

  • August 11, 2018 at 2:54 am

    I feel dirty, programming with my clothes on, now.

  • September 17, 2018 at 4:05 am

    (Reproduction/Feed/Reasoning) decanted selfover hexagon…

  • September 17, 2018 at 4:05 am


  • October 26, 2018 at 5:28 pm

    Anybody knows any course like this, but in spanish? Is a little hard pause to read subtitles…

  • November 10, 2018 at 3:30 pm

    very interesting

  • November 11, 2018 at 2:52 am

    'big' = adj, nigga

  • November 15, 2018 at 5:18 pm

    Kya Hindi m bi video dubbing Ho sakti

  • December 12, 2018 at 3:29 pm

    Enigma decryptor was built thanks to cooperation with Polish mathematicians. 🙂

  • January 18, 2019 at 1:49 am

    Man I wish I was good enough for this school.

  • February 8, 2019 at 3:58 am

    Trying not to be offensive, but he is so bỏing to watch😭.

  • March 11, 2019 at 1:42 pm

    I am new to programming. Is this information outdated?…I certainly appreciate its credibility and weighting but don't want to spend time assimilating lectures if the landscape has shifted..? thoughts

  • June 4, 2019 at 1:38 pm

    This Professor is superb in his lectures- wonderful! Thanks for your lectures Prof.

  • July 21, 2019 at 7:40 pm

    Thanks for providing this material for free

  • July 22, 2019 at 2:29 pm

    20:05 why is that last R capital. I've done the same thing before, but I don't understand why.


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