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1Data Science Student Booklet - Coggle Diagram
1Data Science Student Booklet
1.1Glossary 5-9
1.1.1 Active Data Collection
1.1.1.1 - Involves anything you would directly ask users for. E.g. asking users to provide name or email address. Feedback forms and customer satisfaction surveys are also examples.
1.1.2 Blockchain Technology
1.1.2.1 - Growing list of records known as blocks that are linked using cryptography. Used to record transactions between 2 parties efficiently and in a verifiable and permanent way.
1.1.3 Cloud Storage
1.1.3.1 - Accessing files, software, and services over the internet.
1.1.4 Computational Thinking
1.1.4.1 - Process which a problem is analysed and solved so that a human machine or computer can effectively implement the solution. Uses strategies to organise data logically, break down problems, interpret patterns and designs, and implement algorithms to solve problems.
1.1.5 Data Warehousing
1.1.6 Emoticons
1.1.7 Graphs
1.1.8 Indigenous Cultural and Intellectual Property (ICIP)
1.1.9 Informatics
1.1.10 Interval Levels of Measurement
1.1.11 Machine Learning
1.1.12 Memes
1.1.13 Passive Data Collection
1.1.14 Pivot Table
1.1.15 Qualitative Data
1.1.17 Ratio Levels of Measurement
Relational Database
1.1.18 Structured Query Language (SQL)
1.1.19 Statistical Modelling
1.1.20 Structured Datasets
1.1.21 Unstructured Datasets
1.1.22 Variety
1.1.23 Velocity
1.1.24 Volume
1.1.25 What-if Modelling
1.1.26 NESA Glossary Keywords
1.1.26.1 Analyse
1.1.26.2 Apply
1.1.26.3 Assess
1.1.26.4 Define
1.1.26.5 Examine
1.1.26.6 Explain
1.1.26.7 Evaluate
1.1.26.8 Identify
1.1.26.9 Investigate
1.1.26.10 Interpret
1.1.26.11 Outline
1.1.26.12 Summarise
1.1.26.13 Collate
1.1.26.14 Develop
1.1.26.15 Determine
1.1.26.16 Explore
1.1.26.17 Filter
1.1.16 Quantitative Data
1.2Collecting, storing and analysing data (pg10)
1.2.1Quantitative data
ratio
income
Interval
temperature
pH measure
Quantitative data is often represented using numerical data types such as integers, decimals or floating point numbers.
1.2.2Qualitative data
Aka Categorical Data Types
Ordinal
classified into ordered categories which are based on a hierarchal scale eg. high to low
eg. agree to disagree
Nominal
can be classified into mutually exclusive categories within a variable
eg. preferred mode of transportation - car, train, bus
strings
characters
Qualitative data is data that cannot be expressed as numbers or measured, consisting of words, pictures or symbols.
Represented through charts, line graphs, bar graphs, pie charts, scatter plots
Examples
Case stduies
Documents
Colour
Taste
Touch
Used to answer the how and whys of a question
1.3Data collection types
1.3.1Comuterised
Active
Actively interacting with customers or clients
Surveys
Interviews
Focus groups
Passive
Collecting data without actively engaging with customers or clients
Observation
Collecting existing data (such as from public records)
1.3.2Manual
Passive
Collecting data automatically through technology
Web analytics
Tracking devices
Active
Using technology to actively collect data from participants
Online surveys
Mobile apps (?)
1.4Primary and secondary data
1.4.1Primary
Advantages
Relevance
Data collected specifically for the research question is more likely to be directly relevant and tailored to the specific needs of the study
Accuracy
Data collected directly from the source is more likely to be subject to errors or biases introduced through interpretation or secondary sources
Control
Customisation
Disadvantages
Cost
Burden
Time-consuming
Expertise
1.4.2Secondary
Advantages
Disadvantages