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terça-feira, 4 de setembro de 2012

quarta-feira, 22 de agosto de 2012

A Gentle Introduction to Programming Using Python

site MIT
How to Think Lyke a Computer Scientist by Allen Downey, (on-line), (pdf livro). (pdf 2)

Lectures

LEC #	TOPICS	HANDOUTS AND EXAMPLES
1	Introduction	Getting started (PDF) raw_input_example.py (PY)
2	Conditionals, loops	How to comment code properly (PDF) height_example.py (PY) conditional_examples.py (PY) loop_examples.py (PY)
3	Defining functions	lecture3.py (PY) functions.py (PY) check_for_vowels.py (PY)
4	Strings, lists, list comprehensions	string_examples.py (PY) list_examples.py (PY) comprehension_examples.py (PY) Additional Material Two examples of a rock-paper-scissors program: rps_example1.py (PY) rps_example2.py (PY) How to use while-else loops (suggestion: don't use them at all, but if you do be aware they work differently than you might think): while_else.py (PY)
Optional lecture	Recursion	Recursion notes (PDF) Recursion examples (PY) Optional problems (PDF) Solutions to optional problems (PY)
5	Tuples, dictionaries, common Python mistakes	tuple_examples.py (PY) Remember that the keys of a dictionary must be immutable objects, but thevalues of a dictionary can be either immutable or mutable objects. Common Python mistakes and misconceptions (PDF)
6	Classes	point.py (PY)
7	More about classes	wheel.py (PY)
8	Inheritance	inheritance_examples.py (PY)

2. Built-in Functions

The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.

		Built-in Functions
`abs()`	`dict()`	`help()`	`min()`	`setattr()`
`all()`	`dir()`	`hex()`	`next()`	`slice()`
`any()`	`divmod()`	`id()`	`object()`	`sorted()`
`ascii()`	`enumerate()`	`input()`	`oct()`	`staticmethod()`
`bin()`	`eval()`	`int()`	`open()`	`str()`
`bool()`	`exec()`	`isinstance()`	`ord()`	`sum()`
`bytearray()`	`filter()`	`issubclass()`	`pow()`	`super()`
`bytes()`	`float()`	`iter()`	`print()`	`tuple()`
`callable()`	`format()`	`len()`	`property()`	`type()`
`chr()`	`frozenset()`	`list()`	`range()`	`vars()`
`classmethod()`	`getattr()`	`locals()`	`repr()`	`zip()`
`compile()`	`globals()`	`map()`	`reversed()`	`__import__()`
`complex()`	`hasattr()`	`max()`	`round()`
`delattr()`	`hash()`	`memoryview()`	`set()`

segunda-feira, 13 de agosto de 2012

Python - Introduction to Computer Science and Progamming

MIT

Introduction to Computer Science and Programming

References

Zelle, John M. Python Programming: An Introduction to Computer Science

Lectures:

Lecture 1: Introduction to 6.00

Topics covered: Purposes of the course, declarative and imperative knowledge, flow of control, algorithms, fixed program and stored program computers, termination conditions, interpretation, compilation, syntax, static semantics, semantics, and types of errors.
Lecture slides (PDF)

Lecture 2: Core Elements of a Program

Topics covered: IDLE, types of objects, operators, overloading, commands, variables, assignment, input, straight line and branching programs, looping constructs, turing completeness (w), conditionals, nesting.
Lecture code handout (PDF) (PY)
Recitation 1: Introduction to Coding Concepts

Topics covered: Syntax, semantics, object types, comparison, loops, coding.

Lecture 3: Problem Solving

Topics covered: Termination, decrementing functions, exhaustive enumeration, brute force, while loop, for loop, approximation, specifications, bisection search.
Lecture code handout (PDF) (PY)
Loops. An Introduction to Python.

Lecture 4: Machine Interpretation of a Program

Topics covered: Decomposition, module, function, abstraction, formal parameter, actual parameter, argument, assert, scope, mapping, stack, last in first out, LIFO, strings, slicing.
Lecture code handout (PDF) (PY)
Recitation 2: Loops, Tuples, Strings and Functions

Topics covered: Loops, tuples, concatenating tuples and strings, string operations, immutability, range function, slicing, types of data structures, decrementing function, global and local variables, global keepers.

Lecture 5: Objects in Python

Topics covered: Tuples, lists, dictionaries, methods, identifiers, modifying objects, aliasing, mutability.
Further Study

8. Lists. How to Think Like a Computer Scientist.
9. Tuples. How to Think Like a Computer Scientist.
10. Dictionaries. How to Think Like a Computer Scientist.
5.1 More on Lists. he Python Tutorial.
5.3 Tuples as Sequences. The Python Tutorial.
5.5 Dictionaries. The Python Tutorial.

Lecture 6: Recursion

Topics covered: Dictionaries, modular abstraction, divide and conquer, recursion, tower of Hanoi, base case, Fibonacci sequence.
Recitation 3: Lists and their Elements, Sorting, and Recursion

Topics covered: Tuples, lists, iteration, list elements, sorting lists, mutability, keys, dictionaries, chain method, recursion, base case, Tower of Hanoi (w).

Further Readings:

4.9 Recursion. How to Think Like a Computer Scientist.
Recursion. An Introduction to Python.
Comparing Recursion and Looping. An Introduction to Python.

Lecture 7: Debugging

Topics covered: Binary, float, floating point, approximations, debugging, runtime error.
Further Readings

8. Errors and Exceptions. Python v2.7.2 Documentation.
Appendix A: Debugging. How to Think Like a Computer Scientist.

Lecture 8: Efficiency and Order of Growth

Topics covered: Efficiency, problem reduction, RAM, best case, worst case, expected case, growth, exponential growth, polynomial growth, logarithmic growth, global variables.

Lecture 9: Memory and Search Methods

Topics covered: Memory, storage, indirection, sorting.

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