Lesson 2.1 Binary Numbers

- Binary: A number system that uses only 2 digits (0 and 1) to represent values.

- Binary is the simplest form of number system that uses only 2 digits.

- This number system is used in computing for representing data and performing operations at the fundamental level of electronic circuits.

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Lesson 2.1 Binary Numbers

- Computing devices represent data digitally, meaning that the lowest-level components of any value are bits.

- Bit is shorthand for binary digit

- 0000 = 4 bits

- 00000001 = 8 bits

- 8 bits is called 1 byte

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Lesson 2.1 Binary Numbers
- The position of each bit in a binary number represents a power of 2
- Starting from the rightmost bit and moving to the left, the positions represent increasing powers of 2.

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Lesson 2.1 Binary Numbers

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(0 x 8 ) + (0 x 4) + (1 x 2) + (1 x 1)


0011 = 0 + 0 + 2 + 1 = 3

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Lesson 2.1 Binary Numbers

-Computing devices represent data digitally, meaning that the lowest-level components of any value are bits.

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Lesson 2.1 Binary Numbers

-A byte is 8 bits

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Lesson 2.1 Binary Numbers

-Abstraction is the process of reducing complexity by focusing on the main idea.
- By hiding details irrelevant to the question at hand and bringing together related and useful details, abstraction reduces complexity and allows one to focus on the idea.

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Lesson 2.1 Binary Numbers

-Bits are grouped to represent abstractions. These abstractions include, but are not limited to, numbers, characters, and color.

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Lesson 2.1 Binary Numbers

-Analog data have values that change smoothly, rather than in discrete intervals, over time. Some examples of analog data include
pitch and volume of music,
colors of a painting, or position of a sprinter during a race.

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Lesson 2.1 Binary Numbers

-The use of digital data to approximate real-world analog data is an example of abstraction.

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Lesson 2.1 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.

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Lesson 2.1 Binary Numbers


The samples are measured to figure out the exact bits required to store each sample.

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Lesson 2.1 Binary Numbers
The consequences of using bits to represent data

- Integers are represented by a fixed number of bits, which limits the range of integer values and mathematical operations on those values.
- This limitation can result in overflow or other errors.

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Lesson 2.1 Binary Numbers


How does increasing the number of bits in binary representation affect the range of values that can be represented?

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Lesson 2.1 Binary Numbers


Explain how the sound waves from live music can be recorded and converted to digital data using a sampling technique.

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Lesson 2.2 Data Compression

Data compression is the process of reducing the size of data files while maintaining as much information as possible.

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Lesson 2.2 Types of Data Compression

1. Lossless Data compression: A compression technique that allows for perfect reconstruction of the original, uncompressed file.

best used when preserving the quality of a file is most important.

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Lesson 2.2 Types of Data Compression

2. Lossy Data compression: The compression technique that generally reduces the file size the most

- Best used when transmission speed is most important.

A compression technique that discards some data

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Lesson 2.3 Extracting info from Data
By examining data closely, we can identify trends, make connections and address problems

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Lesson 2.3 Extracting info from Data
Metadata is Data about Data

Example: the piece of data may be an image, while the metadata may include the data of creation or the file size of the image.

Changes and deletions to metadata don't change the primary data

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Lesson 2.3 Extracting info from Data
Cleaning data is a process that makes the data uniform without changing its meaning.

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Lesson 2.3 Extracting info from Data
identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It involves tasks like removing duplicate entries, handling missing values, and standardizing formats to ensure data quality.

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Lesson 2.3 Extracting info from Data
Problems of bias are often created by the type or source of data being collected

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Lesson 2.3 Extracting info from Data
Data provides opportunities for identifying trends, making connections, and addressing problems.

Digitally processed data may show a 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

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Lesson 2.3 Extracting info from Data
Often, a single source does not contain the data needed to draw a conclusion. It may be necessary to combine data from a variety of sources to formulate a conclusion

The size of a data set affects the amount of information that can be extracted from it

Large data sets are difficult to process using a single computer and may require parallel systems

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Lesson 2.3 Extracting info from Data

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.

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Lesson 2.3 Extracting info from Data

Data Filtering: Data filtering is the process of selectively extracting or removing specific pieces of data from a larger dataset based on certain criteria or conditions.


It allows you to focus on relevant information while excluding irrelevant or unwanted data

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Lesson 2.3 Extracting info from Data

Data: Raw, unorganized facts or information.

Information: Data that has been processed, organized, and interpreted to provide meaning.

Computational Tools: Programs, algorithms, and other tools used to analyze and transform data.

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Lesson 2.3 Extracting info from Data

Trends and Patterns: Recurring or significant characteristics within data that can be identified through analysis.

Insights: New understanding or knowledge gained from analyzing data.

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Lesson 2.4 Using programs with data

Data processing programs can help you acquire information from data

Data filtering systems help with finding information and recognizing patterns

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Lesson 2.4 Using programs with data

Manipulating data by combining, clustering or classifying it can bring out new information and patterns previously unseen in the raw data, making it a helpful tool for data analysis

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Some Vocabulary

Digital Data: Digital data refers to information that is represented using discrete, binary values (0s and 1s).
It is commonly used in computers and other digital devices because it can be easily stored, processed, and transmitted


Analog data: Values that change smoothly, rather than in discrete intervals, over time. For example, the pitch and volume of a live concert.

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Some Vocabulary

Data Transformation: Data transformation refers to the process of converting data from one format or structure to another.


It involves modifying, reorganizing, or manipulating data to make it more suitable for analysis or other purposes.

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Some Vocabulary

Lossless Compression Algorithms: Lossless compression algorithms are methods used to compress data files without losing any information.


The compressed file can be fully restored to its original form without any loss of data.

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Some Vocabulary

Lossy Compression Algorithms: Lossy compression algorithms are methods used to reduce the size of a file by permanently removing some data.

These algorithms achieve high compression rates but result in a loss of quality or detail in the compressed file.

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Some Vocabulary

Overflow Error: An overflow error occurs when a computer program or system tries to store a value that is too large to be represented within the available memory or data type.

This can lead to unexpected and incorrect results.

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Some Vocabulary

Rounding Error: Rounding error occurs when a number is approximated or rounded to a certain decimal place, resulting in a small discrepancy between the rounded value and the actual value.

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