Lecture 1

Question 1

What is the role of a data scientist? (responsible data analytics from a data scientist perspective)

Answer 1

data scientist:

technical tools:
* has statistical tools for data analytics
* has the fundamentals of machinine learning for data analytics
* makes the design choices

Responsible analysis
* accounts for data bias and bias mitigation
* accounts for other stakeholders and “non-customers”
* decides on which design choices are made

Question 2

What are the four flavours/parts in data analytics?

Answer 2

Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics

Question 3

What is descriptive analytics?
(Main question and tools)

Answer 3

Main question: What is happening?

Tools: Visualization, Statistics

Question 4

What is Diagnostic analytics?
(Main question and tools)

Answer 4

Main question: why did it happen?
tools: Advanced statistics. clustering

Question 5

What is Predictive analytics?
(Main question and tools)

Answer 5

Main question: What is likely to happen?

Tools: Supervised, unsupervised machine learning

Question 6

What is Prescriptive analytics?
(Main question and tools)

Answer 6

Main question: What should I do about it?
Tools: Monitoring, Stakeholder analysis

Question 7

What are the data science aspects?

Answer 7

• Proper Data Usage
• Data Nature
• Data Type
• Data Visualiation
• Modelling
• Validation

Question 8

What are the goals of data science?

Answer 8

• To have an overview and terminology
• to know where to look for answers
• to ask the “right” questions
• to answer the “right” answers
• data value
• opportunities
• challenges

I think to understand the data value and add value to the data

Question 9

What does data science consist of? (Data as integral part)

Answer 9

• collecting
• curating
• cleaning
  of the data

collecting gathering the data

curating select, organize, and look after the data

cleaning (the data that has been collected and curated) now fixing or removing incorrect, corrupted, incorrectly formatted, duplicate or incomplete data within the data set.

this can be visualized, analysized, modeling

These steps, collecting curating and cleaning can be presented by: visualizing, analysing, and modeling (slide 37 week 1)

Question 10

Modelling questions (to ask yourself)

Answer 10

• Why do I want to model?
• what is useful to model?
• what can i model?
• how will the model be used?
• who is going to use the model?

Question 11

Data questions (to ask yourself)

Answer 11

• What data do I need?
• What data do i have?
• How hard is it to get the data?

Question 12

What is the essence of data science?

Answer 12

To refine the questions,
( slide 48, bit vague but i think asking questions as a DS, getting responses (from customers who do not know a lot about data science) and based on those responses refining the question and asking new, more specific questions. )

Question 13

What is data?

Answer 13

1. “Factual information (such as
   measurements or statistics)
   used as a basis for reasoning,
   discussion, or calculation
2. Information in digital form that
   can be transmitted or
   processed
3. Information output by a
   sensing device or organ that
   includes both useful and
   irrelevant or redundant
   information and must be
   processed to be meaningful” –
   Merriam-Webster Dictionary

Question 14

Types of Data

Answer 14

There is a lot of different types:
* Transport
* Geographical
* cultural
* scientific
* financial
* statistical
* meteorological (about weather)
* natural (nature)

Question 15

Types of data structures

Answer 15

• Structured data
• semi-structured data
• unstructured data

Question 16

What is the difference between structured, semi-structured and unstructured data ?

Answer 16

Structured data is stored in a predefined format and is highly specific; whereas unstructured data is a collection of many varied data types that are stored in their native formats; while semi-structured data does not follow the tabular data structure models associated with relational databases or other data table forms

Question 17

Differences between structured and unstructured data

Answer 17

Structured data:
1. displayed in rows, columns and relational databases
2. it is made out of number dates and strings
3. estimated to be 20% of enterprise data
4. requires less storage
5. easier to manage and protect with legacy soultions

Unstructured data:
1. cannot be displayed in rows, columns and relational databases
2. images, audio, video, word processing files, e-mails, spreadsheets
3. estimated 80% of enterprise data
4. requires more storage
5. more difficult to manage and protect with legacy solutions

Question 18

Unstructured (digital) data examples

Answer 18

An image
* An image is basically a matrix of numbers
* each element of the matrix (a pixel) is identified by three values: R, G, B (red green blue)

Signal/sound/speech
* a signal is represented as a vector (array)
* time corresponds to the index of the array
* the different values represent the content of the array

Text
* A text is represented as a vector (array)
* the position of a letter in the text correspond to the index of the array
* the different values represent the content of the array.

Question 19

Structured data, specifically tabular data, what are the databases?

Answer 19

Excel files, CSV files for example

Question 20

Structured data types
(what are the two that it can be broken down in?)

Answer 20

Quantitive (also called numerical) data can be directly represented as a number (integer or floating point). can be broken down further into:
* continuous (height, weight, age)
* discrete (number of cards, number of patients, number of books)

Categorical data is generally textual and (often) needs further processing to be analyzed can be broken down further into:
* ordinal (grades, size of clothing, study level)
* nominal (hair colour, gender, marital status)

Question 21

What is metadata? and why is it there?

Answer 21

data about the data.
IT is usually employed for administative/archival purposes

Question 22

Different types of metadata

Answer 22

• descriptive
• technical
• administrative
• structural
• rights
• presentation

Question 23

What is descriptive metadata?

Answer 23

Defines of describes an information resource to aid identification, recovery, and retrieval at any and all levels of aggregation
for example:
* publication-level metadata
* citation metadata
* subject indexing
* linking metadata

Question 24

What is technical metadata?

Answer 24

describes obejectives technical information about an information resource
for example:
* file size
* pixel height
* duration

Question 25

What is Administrative metadata?

Answer 25

Supports the general management and use of an information resource
for example:
* identification metadata
* content lifecycle metadata
* versioning metadata

Question 26

What is structural metadata?

Answer 26

defines what the component objects are and how they relate to each other
for example:
* product definition metadata
* product organization, assembly metadata

Question 27

What is ‘rights’ metadata?

Answer 27

Supports the management of an information resource’s intellectual property.
* geographic scope metadata
* timeframe metadata
* rights holder metadata

Question 28

What is presentation metadata?

Answer 28

Defines how information will be fomratted for a particular object.
for example:
* tagging for browser display

Question 29

What is the ‘Role of data Nature’?

Answer 29

The role of data nature defines the:
* type of questions
* type of model
* data collection approach

Question 30

What implications do data bring?

Answer 30

• how to store it?
• how to process it?
• what ML approach?
• what computational power?
• what are the inputs and outputs?