G.1 General

Question 1

1. Define the notion of “Datification”? In which way is it a revolution with respect to smart environments?

Answer 1

Data is everything and everything is data. Datafication is to render into data all the aspects of the world, even those that have never been quantified. It is revolutionary because for the first time is open to individuals and companies, making most of the crutial algorithms and evidence-based decision available.

Question 2

2. Define the characteristics of data centric sciences? What is the role of data for them? What are the two components that make them a new generation of experimental sciences?

Answer 2

Data management, greedy algorithms and programming models to be deployed in different target computer architectures.

Data collections as backbone for conducting experiments, drive hypothesis and lead to “valid” conclusions, models, simulations, understanding.

The two components: Big Data handling and architecture

Question 3

3. Define the notion of Big Data. In your opinion how does this notion opens new challenges to data management?

Answer 3

“Big Data” refers to datasets that are so large, complex, and dynamic that traditional data processing systems have difficulty efficiently handling and analyzing them. That’s why we refer to the V’s. There are three aspects to consider when talking about big data and those are:
* Data collection – characteristics difficult to process on single machines or traditional DBs.
* New generations of tools, methods and technologies to collect, process and analyze massive data collections.
* Tools imposing the use of parallel processing and distributed storage.

Question 4

Give 5 properties that characterise Big Data? Explain in which way they are challenging for managing data?

Answer 4

Properties - Challenges:
1. Volume – quantity of collected and generated data
2. Veracity – quality and reliability of the data, that they really reflect the natural state.
3. Variety - diverse types of data and formats
4. Variability – changes in the structure and format of data
5. Value – that it can be turned into valuable insights.
6. Velocity – data production rate
These challenges require a combination of technological solutions, organizational strategies, and data management best practices.

Question 5

In the case of your domain of expertise, how does Big Data open novel possibilities or problems/challenges?

Answer 5

Big Data opens exciting possibilities for advancing natural language processing and machine learning capabilities, it also brings forth challenges related to data quality, bias, computational resources, privacy, interpretability, and ethical considerations.

Question 6

What are the type of questions that data analytics can answer that cannot be answered by classic factual querying?

Answer 6

The type of questions that data analytics can answer that cannot be answered by classic factual querying are the ones in prediction and classification nature.

Question 7

Programming language used to manage and manipulate data stored in Relational Database Management Systems (RDBMS). Allowing operations such as creating and modifying tables, inserting, updating, and deleting data, as well as retrieving information through queries

Answer 7

SQL (Structured Query Language)

Question 8

Refers to a set of database technologies that depart from the relational model. …. this Databases are designed to handle large volumes of unstructured or semi-structured data and provide flexibility in the data schema.

Answer 8

NoSQL (Not Only SQL)

Question 9

In terms of Big Data Volume, which is the basic unit of measure?

Answer 9

1B = 1 Byte

Question 10

In terms of Big Data Volume, what does a Gigabyte represent?

Answer 10

1x10´9 Bytes

Question 11

In terms of Big Data Volume, which is the highest capacity achieved lately?

Answer 11

1x10´24 Bytes = Yottabyte. Brontotype and Geaobyte en términos de 1000 de uno a otro

Question 12

Social Data, Network Science, Digital Humanities and Computational Science are…

Answer 12

Data centric science that emerged and developed new methodologies to explode data.

Question 13

Develop Methodologies weaving data management, greedy algorithms and programming models that must be turned to be deployed in different target computer architectures…

Answer 13

Data centric science

Question 14

Its hypothesis is that any complex system can be represented as a net so operations can be applied and by ende modeled, dupledixs. Graphic theory is applied.

Answer 14

Network science

Question 15

Now days we have many algortihms and mathematical model but we need new ones. The numerical models are less costly than the new solutions now days (A.I) but when there is no solution we should go to the second option. Here we are talking about

Answer 15

Computation

Question 16

When is about ………… we don’t work on a lab we work on computer support. That’s why we need to clearly define the architecture computing environment (cycles) to carry it out.

Answer 16

Experiment setting

Question 17

Which are the Big Data properties?

Answer 17

Volume, Velocity, Varity, Variability, Veracity, Value

Question 18

The Big Data property that refers to QUANTITY / SIZE

Answer 18

Volume

Question 19

The Big Data property that refers to PRODUCTION RATE

Answer 19

Velocity

Question 20

The Big Data property that refers to DIVERSITY OF DATA TYPE AND FORMAT (heterogenicity)

Answer 20

Variety

Question 21

The Big Data property that refers to the QUALITY OF THE DATA

Answer 21

Verecity

Question 22

The Big Data property that refers to CHANGES IN THE STRUCTURE AND FORMAT OF DATA

Answer 22

Variability

Question 23

The Big Data property that refers to the ABILITY TO TURN DATA INTO USEFUL INSIGHTS

Answer 23

Value

Question 24

What are three aspects that we can say about Big Data

Answer 24

1. Data collections with characteristics difficult to process on single machines or traditional databases.
2. A new generation of tools, methods, and technologies to collect, process and analyze massive data collections.
3. Tools imposing the use of parallel processing and distributed storage.

Question 25

Hadoop of industry what can we tell?

Answer 25

Processes CNC machine logs, Aggregate logs from multiple machines and industries, builds failure predictions and predetermined thresholds of confidence, SaaS-based to aggregate data

Question 26

What does CNC mean?

Answer 26

Computer Numerical Control

Question 27

What is SaaS-based?

Answer 27

Software as a Service. En lugar de descargar e instalar el software en dispositivos locales, los usuarios pueden acceder a las aplicaciones y a sus funciones a través de un navegador web.

Question 28

What are the queries that NoSQL stores cannot respond? (Classic solutions / factual Queries)

Answer 28

Prediction and classification

Question 29

What drives that NoSQL can not answer prediction and classification?

Answer 29

Experimental data processing approaches. Data science

Question 30

It is the representation of complex environments by rich data opening up the possibility of applying all the scientific knowledge regarding how to infer knowledge from data

Answer 30

Data science

Question 31

Is the main hypothesis of the methodology by which actionable insights can be inferred from data

Answer 31

We have enough data to come up with something / decide

Question 32

What is the objective of data science

Answer 32

to produce beliefs informed by data. Being able to explain decition making

Question 33

“Data is everything and everything is data” Pythian

Answer 33

Datafication

Question 34

What is Datification

Answer 34

It is rendering into data, aspects of the world that have never been quantified.

Question 35

What are the characteristics and importance of Datification?

Answer 35

The magnitude of analytical knowledge, most of the crucial algorithms are accessible. It uses rich data to make evidence-based decition OPEN TO “INDIVIDUALS” AND COMPANIES

Question 36

Any process in which measurments and tests are used within a tipically controlled environment to support or refute a hypothesis.

Answer 36

Scientific experiment

Question 37

Hypotheses

Answer 37

Unsupported propositions, usually based in insights or prior research, from which an investigation can be developed.

Question 38

Experiments are generally designed to identify a casual relationship

Answer 38

True

Question 39

Without a good experiment design, no causality can be inferred from your results

Answer 39

True

Question 40

Experiments in data science: Initial questions

Answer 40

Why to support H0 What is being measured an how What data and collection Tools / libraries

Question 41

Experiments in data science: design

Answer 41

Experimental workflow Formulate Ho Data gathering definition Possible problems identification Collecting data

Question 42

When we want to explain what happened we use statistics and the output are descriptive analytics, positioning us in having information and hindsight. If we now pass to the analysis of descriptive analytics we get why it happened, diagnostic analytics, growing in information and generating an insight. If we go further thrying to understand what will happen we culd use the predictive analysis, where machine learning plays a role and we will get a Foresight that with the optimization level can descrive how can we make it happen -> prescriptice analytics.

Answer 42

Experiments objectives

Question 43

I have raw dara, from which I will do Data exploration and preparation. Intending Quantitative profiling (descriptive analytics), Cleaning, normalization, attributes engineering and samples selection; all of this with in between uni, bi multivariable observation and inteactive graphs. Then we go to the insights/ foresight. Search, where we will have the sample fragmentation, generative dicriminative moel (training) and validation with in between error analysis and ablative analysis

Answer 43

Data science pipe line

Question 44

Observations of phenomena often described as series of features/attributes

Answer 44

Data

Question 45

Analytics objective (looks for insights or foresights) expressed as pipeline of operations guided by the conditions and characteristics of the data.

Answer 45

Query

Question 46

Model or prediction with associated assessment indexes, not definitive accepted with an associated error margin, accepted by comparison.

Answer 46

Result

Question 47

How can we define the data processing spectrum raw to curated spectrum?

Answer 47

Querying objective, Data exploration, hindsight, insight, fersight, relational & aggregation querying and information revel

Question 48

When talking about Querying approaches…

Answer 48

I have two categories of data: Databases information retrieval and data science from which we can define the query type, execution model, results properties and data content.

Question 49

Math and statistics + Computer science / IT

Answer 49

Machine Learning

Question 50

Math and statistics + Domains /Business knowledge

Answer 50

Traditional research

Question 51

Domains /Business knowledge + Computer science / IT

Answer 51

Software development

Question 52

Math and statistics + Domains /Business knowledge + Computer science / IT

Answer 52

Data Science

Question 53

Depends on data science/engineers “expertise.”

Answer 53

Artisanal design

Question 54

Using many different libraries, stack, tools difficult to integrate

Answer 54

In house programming

Question 55

Espacio dedicado a la práctica y experimentación en el campo de la ciencia de datos (Kaggle, Google colab, azure notebooks)

Answer 55

Data Science Lab

Question 56

Combinación de herramientas, tecnologías y lenguajes de programación utilizados en el campo de la ciencia de datos.

Answer 56

Data Science stack

Question 57

FaaS, DaaS, PaaS, STaaS, IaaS

Answer 57

BD services platforms

Question 58

When talking about data science pipeline, which are the challenges faced?

Answer 58

Execution on distributed arquitectures and tuning for improving data management across nodes.

Question 59

Data science toolboxes enactment environments:

Answer 59

Big Data Platforms & Stacks WIDE environments Machine Learning Services

Question 60

Essential tool designed to maximize programmer productivity

Answer 60

IED Integrated Development Environment

Question 61

Basic pieces of IDE

Answer 61

Editor , compiler, debugger