function call passes a variable as an argument. It takes the place of the function’s parameter. 


Write a function called square that takes a parameter named ‘t’, which is a turtle. It should use the turtle to draw a square. Write a function call that passes bob as an argument to square, and then run the program again. This looks like:

def square(t):
    for i in range(4):
        fd(t, 100)


Adding a parameter to a function is called generalization because it makes the function more general

An interface is like a contract between a function and a caller. The caller agrees to provide certain parameters and the function agrees to do certain work.

data structures are things like lists, dictionaries and tuples

compound data structures are combinations of data structures like lists of tuples, and dictionaries that contain tuples as keys and lists as values

Ajax (an acronym for Asynchronous JavaScript and XML)[1] is a group of interrelated web development techniques used on the client-side to create asynchronous web applications. Ajax is not a single technology, but a group of technologies. With Ajax, web applications can send data to, and retrieve data from, a server asynchronously (in the background) without interfering with the display and behavior of the existing page.   This solves the problem of requiring a page to be re-loaded from the server with each user action.  When it was originally described in the mid-2000s, Ajax commonly used XML as the data interchange format but many developers have also used JSON to pass Ajax updates between the server and the client.[15]

Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in aformat that is both human-readable and machine-readable. Although the design of XML focuses on documents, it is widely used for the representation of arbitrary data structures, for example in web services.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language.

MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Computational processing can occur on data stored either in afilesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data.

  • “Map” step: The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node.
  • “Reduce” step: The master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve

Programming Language Types

Imperative – The focus is on what steps the computer should take rather than what the computer will do(ex. C, C++, Java).

Declarative – The focus is on what the computer should do rather than how it should do it (ex. SQL).

Functional – a subset of declarative languages that has heavy focus on recursion

There are some multiparadigm languages that kind of straddle both sides. For example, C#, Python, and JavaScript are mainly imperative languages that have some functional or declarative elements. There are also logic programming languages (like prolog) that mainly focus on satisfying constraints.


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