Finals

analog vs digital

analog:

* infinite values (sine waves)
* continuous signal, one time-varying quantity represents another time-based variable
* measures natural/physical values, suitable for audio/visual transmission

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digital:

* discrete/separate values (square waves)
* represents data as a sequence of discrete (separate) values at any point in time versatile
* easy to process & transmit

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analog to digital

lossless vs lossy compression

* lossless loses none and compresses at a lesser extent, moves slower than lossy because data file is larger bc it cannot be compressed as big


* lossy compresses at a greater extent but loses the og so not good for text compression cuz u literally cannot read it how its supposed to be read

B, KB, MB, GB, TB

data storage metrics, in order increasing by 1000x bits

match


1. analog
2. compression
3. digital
4. heuristic
5. lossless
6. lossy
7. RGB

a. color model that adds different shades of light to reproduce a variety of colors

b. compressed data can be reconstructed back to the original; perfectly reversible

c. laser thermometer

d. mercury thermometer

e. reducing file size by reducing the number of bits used to store the information

f. uses inexact approximations and partial data discarding to represent the content; irreversible

g. technique for finding an approximate solution quickly when an exact solution is too slow

1. d
2. e
3. c
4. g
5. b
6. f
7. a

Digital Divide

The gap between people with and without access to the internet, creates a disparity in knowledge and societal participation.

valid IP address

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A valid IP address must be in the form of A.B.C.D, where A,B,C and D are numbers from 0-255.

redundancy

backup/extra components incase a line is down or broken to ensure packets are sent to their destination; providing fault tolerance.

packet routing / TCP

creates packets at sending to be sent simultaneously but in different routes and reassembles them at the receiving. Ensures packets are sent to their destination, if a packet is lost or not sent than TCP requests that specific packet back to be sent. 

DNS

turns domain names into computer readable IP addresses

ex: google turns into 33.44.51.126

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algorithm

finite set of instructions that accomplish a task; is the answer to a problem

linear vs binary search

* binary (logarithmic):
* needs to be a sorted list
* number of inputs and number of steps are equal
* in a list from 1-10 trynna get the target number, binary search goes for the middle number each time then halves the list and continues until getting the target number. If the number is 6 then binary picks 5 and removes numbers 1-5 then the list is now 6-10 chooses 8 and removes 8-10, now the number is 6-7 and binary chooses 7, remove s7 and chooses 6 aka the target number.
* more efficient than linear

\
* linear:
* doesn’t need to be a sorted list
* number of steps is the binary equivalent to the number of inputs (5 inputs has 3 steps because 101 is the binary of 5)
* in a list from 1-10 trynna get the target number, linear checks each one in a linear/orderly process until getting to the target. if the number is 6 then linear search checks starting from 1 to 6 and stops at 6 because 6 is the target number and linear has reached it.

Efficiency

a measure of how many steps are needed to complete an algorithm

\
reasonable time versus unreasonable time

reasonable time: linear, polynomial, binary/log efficiency

unreasonable time: exponential & factorial efficiency

unreasonable vs undecidable

the difference between undecidable problems and unreasonable time algorithms is that an undecidable problem is a problem that no algorithm can be made because it loops forever, this is always capable of providing a yes or no answer, while an unreasonable time algorithm is an algorithm with exponential efficiencies and cannot create an answer in a reasonable amount of time.

* unreasonable:
* an algorithm with exponential efficiencies and cannot create an answer in a reasonable amount of time
* undecidable:
* a problem for which no algorithm can be constructed that is always capable of providing a correct yes-or-no answer
* loops forever

heuristic

Provide an approximate solution that’s good enough to solve a problem when an actual solution is impractical or impossible

traveling salesperson problem

* The Traveling Salesperson Problem is an **optimization** problem. We are trying to find the shortest path to visit all homes/cities.
* Is unreasonable because there is not an algorithm that can solve the problem in a reasonable amount of time.
* Needs to use a **heuristic** to come up with a solution that is "good enough" for most instances of the problem.

parallel processing & speedup

Parallel processing is where programs are broken into small pieces, some of which are run simultaneously. Speedup is the time used to complete a task sequentially divided by the time to complete the task in parallel. Speedup is never equal to the number of processors. \n Eventually the speedup reaches a limit.

distributed computing

data is distributed among multiple computers and each computer runs the program on its own section of the data

\-provides heightened processing speed and memory to handle big datasets

one column charts

* bar chart
* few values

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* histogram
* many values
* all numbers in a range or "bucket" are grouped together.  For example, a chart with the a bucket size of 20 so the numbers 41, 48, and 53 would all be placed in the same bucket between 40 and 60.
* can only be created with numeric data but can be useful when a normal bar chart may be difficult to read.
* Information we can get out of histograms:
* What range of value(s) are most common in this column?
* What range value(s) are least common in this column?
* What ranges of values do or do not appear in this column?

two column charts (x, y)

* crosstab
* few values, usually finite data put in

\
* scatterplot
* many values, continual data being put in

big/open/crowdsourced data

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Big data: Collect huge amounts of data so we can learn even more from it, big huge lots of data

Open data: sharing data with others so they can can analyze it, data is OPEN to be shared w/ others

Crowdsourcing: collecting data from others so you can analyze it, sourcing data from the crowd/people to analyze

cleaning data

making data consistent, removing useless and corrupt data

algorithmic bias: face detection

AI better recognizes white faces and least black faces

algorithmic bias: cropping photos

AI more likely to crop out black faces than white faces

if values on a chart are too unique

then theres little point in doing a chart cuz charts are for finding correlations and connections

data analysis process

algorithm search types

most efficient: binary

least efficient: exponential (or factorial if a choice in q) both being unreasonable

sum()

finds the sum of numbers in an array(s)

countif()

finding how many times a # appears:

countif(range of data beginning:range of data ending, #)

ex: countif(A2:A13, 5) indicates array A value beginning with cell 2 and ending with cell 13, trynna find how many times the number 5 is a part of this array

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countif(range of data beginning:range of data ending, ‘ ‘)

ex: countif(A2:A13, ‘Green’) indicates array A value beginning with cell 2 and ending with cell 13, trynna find how many times the color Green is a part of this array

Bunk or Valid?


1. Regular bitrate upgrades protect you against cyber attacks.
2. Antivirus programs aim to protect computers against malware.
3. The FBI developed ransomware to defend against hackers encrypting other people’s data.
4. A brute force attack tries all possible letters, numbers, and special characters in an attempt to reverse an encrypted password. 
5. The health industry uses antivirus software to track the spread of communicable diseases.
6. Data backups are often encrypted in a distant, secure location, often in an underground climate-controlled “tomb.”
7. Hackers phish for unsecure computers using automated tools.
8. In a spear phishing attack, a thief contacts a user pretending to be their manager or close acquaintance.

1. bunk
2. valid
3. bunk
4. valid
5. bunk
6. bunk
7. bunk
8. valid

Define


1. computing innovation
2. personally identifiable information (PII)
3. phishing
4. keylogging
5. malware
6. encryption
7. decryption
8. symmetric key encryption
9. public key encryption
10. password security math

1. innovation in CS
2. passwords, IP address, sensitive & personal info
3. feigning legitimacy by attaching to a valid file to get a person to reveal their PII
4. tool to record a victims every keystroke and sends to the attacker
5. malicious software intended to compromise a computer system
6. converting info and data into code
7. converting code back to its original
8. private key encryption, uses the same key to encrypt and decrypt information
9. assymetric encryption, uses a public key to encrypt and a private key to decrypt information, the most secure & reliable form of encryption.
10. 26^number of letters in a word

phishing indicators

* Spelling errors.
* Unusual requests.
* Strange email content.
* Personal information solicitation.
* Unfamiliar email addresses.

average()

finds the average number for a list(s)

=average(letter#:letter#)

=average(B2:E2)

stdev()

how widely varied your range of numbers are from the average (central point aka the mean)

=stdev(letter#:letter#)

=stdev(A2:A10)

unique()

returns a list of unique values in an array/list

T or F?

correlation calculation needs numeric responses

True, must be numbers. Letter numbers don’t count and numbers with ‘‘ or something similar won’t work. Must be a clean # like 5 not ‘5’ or five.

T or F?

correlation calculation needs clean data

True, data must be consistent and free from corruption and mistakes. EX: In a list of numbers they should all be #’s so some boxes shouldn’t have letter numbers like four or ‘‘ in them like ‘4’, all should be like 4 5 6 7

– 2 types are…

button and text

z-index

where an element is in relation to other elements, basically the depth.

ex: u have image1 and image2

image1 has a z index of 0

and image2 has a z index of 1

image2 will be in front of image1

because the z index is higher

attribute

css element to put an image in

attributes are:

src- adds the source/link of the image, and

alt- if the image cannot be shown then shows text as an ALTERNATIVE

width- width of the image

height- height of the image

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CSS ID elements must have what 3 components?

be written in camelCase and have a # before and { after

\#theImage {

how to open and close HTML code

open & close:

\
self-closing:

-

biggest heading level

smallest heading level

vs

includes the meta elements i.e info abt the content closes the head has the contents of the code closes the body

T or F

both tags and ID’s must have a # before them in CSS

false, ID’s must because ID’s are particular, but tags don’t

CSS

JavaScript

HTML

position: absolute;

makes the position of an element absolute aka set in the top and left locations

position: absolute;

top: 410px;

left: 120px;

CSS rules: property + value

property + value is the declaration of a CSS property name and its value

ex:

\#theParagraph {

position: absolute;

top: 400px;

color: red;

z-index: 0;

height: 100%;

}

attribute

tag is basically a code line with a hyperlink of a url/web page, href (most important attribute) makes the hyperlink clickable If the  tag has no href attribute, then the link is basically just a string and cannot be clicked

Math.random() output

outputs a random number larger from the given specified range, is bigger than 0.0 but smaller than 1.0, is effectively a percentage

addEventListener()

attaches an event handler to an element

creative commons license

international ngo designed to determine what rights you want to preserve for your intellectual property when putting it on the internet, point of creative commons is to get creators to grant public permission of their stuff to expand the amount of creative works on the internet

putting styles on element IDs vs every element created by an HTML tag such as

etc

putting styles on element IDs means the attributes which personalize elements like color and size. elements created by an HTML tag are opened and closed by said tags with contents inside involving the attributes, making the whole line an element.