3.1 DS Data

Geographical Data

Data associated with a location relative to Earth

Data Reliability

Sample size and accuracy of data

Aggregation

Combining different data sets to compare contrast

Data encryption (from data process of data life cycle)

Data transformed into another code to prevent easy access

Reducing Data Error

Validation: only suitable data can be entered in the database Verification: check to ensure the data entered is accurate and correct

Information

Data processed into a logical format, tells us who, what, when, where, but not HOW

Focus Group

A cluster of a few people as respondents for qualitative data collection

Phenomenology

Qualitative research analyzing a specific event

Experimental Research

Quantitative research differentiating right and wrong statements, mainly used in natural sciences

Survey Research

Quantitative research using data to support ideas

Meteorological Data

Data related to atmospheric sciences

Example: Temperature, wind direction, wind speed

Data Processing & Usage

Transformation and utilization of raw data, processed from its raw form, includes data wrangling, data compression, and data encryption

Data Management

Organizing, storing, and retrieving data, ongoing process from the beginning the the end of the data life cycle

Data Interpretation

Making sense of data analysis (investigate from multiple different dta analysis sources)

Data Security

Protecting data from unauthorized access, containing data encryption, masking, and erasure

Data Encryption

Turning data into non-readable code, only authorized parties can access it

Symmetric key encryption: same key used for both encryption and decryption
Asymmetric key encryption: different keys used for encryption and decryption

Data compression (from data process of data life cycle)

Data transformed into a format that can be efficiently stored

Data erasure

Replacing data with binaries
Physical:
Degaussers - powerful magnetic fields to remove data
Paper shredders
Fire place
Water - short circuit the system

Digital:
Data erasure digitally by replacing it with 0s and 1s

Deepfake

Deepfakes rely on machine learning, training, and combining with computer graphics

Data dilemma

reliability of the data, trustworthiness of the data, outdated data, human error and lack of precision

DIKW Pyramid

Hierarchy representing relationships between data, information, knowledge, and wisdom

Example:
Data: 25 degrees celsius
Information: The temperature is 25, which is for mild activities
Knowledge: The temparature of 25 is for mild activities such as picnic or a walk in the park
Wisdom: Understanding taking a walk in the park during mild weather is good for people's happiness and wellbeing

Data

Collection of facts in raw, neutral, and unorganized form

Knowledge

Logical description of collected facts (data), we know how to apply it to achieve our goals

Wisdom

Application of knowledge in action, able to make sound judgement based on experiences and knowledge

Quantitative Data

Structured data measured using numbers and values, defined in nature

Prompts the question of how many and how much, includes continuous and discrete data

Qualitative Data

Unstructured data based on properties, attributes, labels, etc.

Used as start for asking WHY questions, used for interpretations, theorizations, hypothesis, and initial understandings

Example: social security number

Discrete Data

Data that cannot be broken down into similar parts consisting of integers

Finite and has a limit

Continuous Data

Data that can be infinitely broken down into smaller parts that continuously fluctuate

Case Study

Qualitative research done by an organization

Ethnographic Research

Qualitative research based on different geographic locations

Correlational Research

Quantitative research studying the relationship between variables

Causal Comparative Research

Quantitative research comparing unrelated variables, one is dependent and one is independent

Financial Data

Data related to the financial health of a business

Example: Assets, stock price, owner's equity

Medical/Clinical Data

Health-related information related to patient care

Example: Blood sugar level, heart rate

Transport Data

Data related to transportation studies

Example: walk, cycling, air travel

Data Generation & Creation

Action that digitally generates data, every action done digitally create data, can be manually entered

Data Collection & Extraction

Process of selecting and extracting useful data, only based on Stakeholders decision to use the data

Data Storage

Safe storage of data for accessibility (database) that not everyone can see

Example: Cloud, servers, local file

Data Analysis

Turning raw data into meaningful insights (patterns, trends, forecast)

Data Visualization

Representing analyzed data visually

Data Preservation

Ensuring data is preserved and of good quality, store it on a cloud or a drive

Data Destruction

Removing outdated data permanently

Meta Data

Information about other data

Containing
description metadata: data that identifies or discover other datas
Structural metadata: data that classfies and organizes other data
Administrative metadata: Data that helps provide useful information in managing other kinds of data
Reference metadata: Describes the quality of quantitative data or statistical data to help determine the validity of the data
Statistical metadata: Information that describes the characteristics and contexts of statistical data. Help to interpret statistical data
Legal metadata: The supporting information of legal data, the underlying data related to the legal case. Metadata makes it easier to access and retrieve information relating to the legal case

Data Integrity

Ensuring authenticity, transparency, and eliminating bias. Retaining authenticity and being transparent

Includes commission, omission, and manipulation

Relational Database

Database with multiple tables showing relationships

Data Masking

Turning data into fake data to deceive unauthorized access

Example: turn data of a company into a fairy tale

Data Erasure

Permanent removal of data

Deepfake

Combining computer graphics and AI to create fake data

Data Ethics

Ethical and legal considerations in data collection

Data Bias

Potential bias in data sets

Data Privacy

Control and access to data

Data Dilemmas

Challenges and considerations in data usage

Validation

To prove that collected data is verified and accurate

Data wrangling (from data process of data life cycle)

Raw data cleaned and transformed into useful data

Cultural data

Data that is related to the social studies/human studies

For example: art, trend, etc

Data life cycle

The life cycle for data

From beginning to end:
Generation, collection, processing, storage, management, analysis, visualization, interpretation

Primary data

Data created originally

Secondary data

Data collected by someone else

Deleting data

Putting data in the trash bin but it is still there

Blockchain

Stores information or transactions digitally into a block

Hash

An encryption way of blockchain, turns data into hash

Misinformation

Unintentional mistakes
Ex: satire taken literally

Disinformation

Fabricated or manipulated
Ex. conspiracy or rumours

Malinformation

Deliberate publication of private information

Big data

large volume of data including
volume - huge volume of data
velocity - high volume of data at high speeds
veracity - accuracy of data
variety - different types of data