Data Review Lesson

Data

This unit focuses on how data is represented in the computer. To identify new information through data, it may need to be labeled to make it easier to find, sometimes compressed for ease of sharing, and stored, secured, and processed to make sense of it for solving problems or for identifying new findings.

Key Terms

~Abstraction

~Analog & Digital Data

~Bias

~Binary number system

~Bit & Byte

~Classifying, Cleaning, Filtering data

~Lossless and Lossy data compression

~Metadata

~Overflow error

~Round-off or rounding error

~Scalability

Intro to Data

Data can be stored in variables or lists and can be input to programs or output from them. Binary is the number system used in Computer Science. Computers read machine code, which is made up of 0's and 1's. A "0" represents no electrical charge and a "1" represents a charge. The binary digits are referred to as bits. Eight bits make up a byte. Programs are used to translate data into a representation more easily understood by people. You will learn how to convert binary numbers in this lesson.

Abstraction

Abstraction is the process of hiding irrelevant details to reduce complexity and allows one to focus on the idea. An example of abstraction is number representation. We can represent the number five as: 5, five, cinco, or V (Roman numerals). Each way simplifies details, each way represents the same amount. All digital data is represented by binary digits.

Number Systems

Decimal

Number Systems

Binary

Converting Decimal Numbers to Binary

~~Always start from the left

1.  Write down the decimal number.

2.  Subtract the largest number from the binary table that is the same or less.

3.  Mark a 1 in the column on the table for the power of 2 you subtracted.

4.  Mark an 0 in the columns that could not be subtracted and were skipped.

5.  Repeat steps 2 to 4 until your decimal value reaches zero.

  Note: Use leading 0’s on the left to make a byte (8 bits).

Example: Convert 21 to Binary

Example: Convert 119 to Binary

Converting Binary to Decimal

Example: 00011011

Converting Binary to Decimal

Example: 11011001

Binary Numbers (continued)

Binary numbers can also represent letters for text and colors. Colors are represented by using red, green, and blue (RGB). Computers use one byte for each color. Compiler software is used to translate (compile) code to binary for the computer's CPU to understand.

Types of Errors

• Overflow errors: occurs when the integer to be represented needs more bits than the programming language can represent.

• Rounding or Round-off errors: occurs because of the way numbers with decimal points are stored in the computer.

Analog vs. Digital

Analog data is a continuous stream of data values. Digital data is made up of binary numbers. Analog data can be closely approximated digitally using a sampling technique, which means measuring values of the analog signal at regular intervals called samples. The samples are measured to figure out the exact bits required to store each sample. The conversion of analog to digital is an abstraction.

Data Compression: Lossless & Lossy

Data compression: decreases size in files (such as large image files) to reduce the amount of space needed.

Lossless compression: allows the original image to be restored, no data is lost.

Lossy compression: loses some data and the original can never be restored, this allows for greater compression.

Data

Computers enable us to process data to turn it into information for identifying trends, decision making and research. It can often identify patterns in data that individuals would not be able to find. While humans can interpret small amounts of data, computers are needed to process large amounts of data so humans can make sense of it. Also, we would get tired and distracted and begin making errors.

How Computers Process Data

~Cleaning: removing corrupt & incomplete data, replaces data, verifying ranges and dates, removing and flagging data. Example: Dr could stand for drive or doctor.

~Filtering: Identify and extract different subsets of data to help people understand it better.

~Classifying: help make meaning of large datasets by grouping data with common features and values.

~Bias: occurs when data collected does not represent all the possibilites of available options.

~Patterns: can be identified that people would not recognize, this is called data mining. Artificial Intelligence (AI) is similar to data mining, but uses data to make predictions.

Data (continued)

Digitally processed data may show correlation

between variables. A correlation found in data

does not necessarily indicate that a causal

relationship exists. Additional research is

needed to understand the exact nature of

the relationship. Scalability of systems is an important consideration when working with data sets, as the computational capacity of a system affects how data sets can be processed and stored.

Metadata

Metadata is data that describes data and can help others find the data and use it more effectively. It isn't the content of the data, but includes information about it, such as:

~Date

~Time stamp

~Author/owner

~File size

~File type

Metadata allows us to organize and add structure to data, in addition to making it easier to find (example: tags).

Data Tools

Tables, diagrams, text, and other visual tools can be used to communicate insight and knowledge gained from data. Search tools are helpful in finding data. Software tools such as spreadsheets and databases can be used to filter, organize, and search data. Search tools and filtering systems are needed to help analyze data and recognize patterns.

Extracting Information from Data

Some processes that can be used to extract

or modify information from data include the

following:

~transforming every element of a data set,

such as doubling every element in a list, or

adding a parent’s email to every student

record

~filtering a data set, such as keeping only the

positive numbers from a list, or keeping only

students who signed up for band from a

record of all the students

Extracting Information from Data (continued)

~combining or comparing data in some way,

such as adding up a list of numbers, or

finding the student who has the highest GPA

~visualizing a data set through a chart, graph,

or other visual representation