SlideShare a Scribd company logo
1 of 11
Download to read offline
MK99 – Big Data 1
Big data
&
cross-platform analytics
MOOC lectures Pr. Clement Levallois
MK99 – Big Data 2
Note
• You will find terms squared like this in the slides.
• These terms are part of your quizz assignment for
the week, to be found on the online platform.
• Often technical terms, it is vital that you know
their meaning, as they are the basic vocabulary of
data science.
MK99 – Big Data 3
What you we learn here:
• The definition of data
• The many ways to speak about data.
MK99 – Big Data 4
What is data?
• Definition:
– Originally, data is plural for “datum”, a Latin word
– a “datum” is a single factual, a single entity, a single point of matter.
– Datums are most often called “data points”.
– Data represents a collection of data points.
• We speak also of datasets instead of data (so a dataset is a collection of data points).
– Today, “data” is used in a singular or plural form.
-> “My data is…”, but we sometimes still hear “My data are…”
MK99 – Big Data 5
Examples!
• A date
• A color
• A grade
• An address
• A price
• A number of friends
• A longitude
• An index of poverty
• An item in a catalogue
• A sound frequency
• A list of favorite
movies
• A movie
• A number of clicks on
a web page
• A duration
• A book
• An author of a book
• A vote at an election
• A still image
• A measurement of
CO2
• A response to a
consumer survey
• A purchase ticket
• A curriculum vitae
• Your blood pressure
MK99 – Big Data 6
Data or Metadata?
• Metadata: this is some data describing some other data.
• Example:
– The bibliographical reference describing a book.
– Key takeaway: data without metadata can be worthless
-> What would you do with a pile of 10,000 books without any indication on their title,
authors, or date of publication?
– The difference between data and metadata is not always relevant
-> In the alumni network dataset, what is data and what is metadata?
The metadata The data
MK99 – Big Data 7
Data: how to talk about it
• Example of some data point -> “Four more years. http://t.co/bAJE6Vom”
This textual data is in digital form
(because it is stored in bits on a computer, not by hand writing on a piece of paper)
(as opposed to analog).
The tweet is textual
(as opposed to numerical. In programming, text can also be called a String)
this is the type (or format) of the data
The tweet appears plain text
“plain text” is one sort of format for text.
Others formats are JSON, XML or CSV
this is the format of the data
The text of the tweet is encoded in UTF-8 this is the encoding of the data
The tweet is part of a list of tweets I collected this is the data structure
The tweet is stored in a Word file on my laptop this is the format of the data
Notice the
ambiguity in the
terminology!
MK99 – Big Data 8
Data stored in tables: vocabulary
Rows, or lines.
Each represents
a data point
Columns. Each represents an
attribute of the data.
Header: these are the
names of the attributes.
A value.
(can be
empty).
A spreadsheet, or a table.
This is still the most common
way to represent a dataset.
MK99 – Big Data 9
Data and size.
• The size of data gives an idea of what can be done with it and the
challenges it might pose.
• The size of a dataset can be expressed in number of datapoints.
– Often called lines because we store them as lines in a spreadsheet
• Or the size can be expressed in terms of the storage space the data
takes on a computer drive (see next slide).
– A dataset with 23,000 lines and 16 columns takes ~ 2.6Mb when
presented as an Excel file.
MK99 – Big Data 10
Bytes!
1 bit Can store a yes / no value
8 bits 1 byte (or octet) Can store a single letter
~ 1,000 bytes 1 kilobyte (kb) Can store a paragraph
~ 1 million bytes 1 megabyte (Mb) Can store a low res picture.
~ 1 billion bytes 1 gigabyte (Gb) Can store a movie
~ 1 trillion bytes 1 terabyte (Tb) Can store 1,000 movies. Size of
commercial hard drives in 2014.
~ 1,000 trillion bytes 1 petabyte (Pb) 20 Pb = Google Maps in 2013
Most
firms
today
MK99 – Big Data 11
Much more…
• Make the readings for Week 1.
• Watch the video on big data, also in Week 1.
• Start following #bigdata and #dataanalytics on
Twitter.

More Related Content

What's hot (20)

Data and its Types
Data and its TypesData and its Types
Data and its Types
 
Data analytics
Data analyticsData analytics
Data analytics
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
 
Exploratory data analysis
Exploratory data analysisExploratory data analysis
Exploratory data analysis
 
Data Cleaning
Data CleaningData Cleaning
Data Cleaning
 
Data analysis
Data analysisData analysis
Data analysis
 
Introduction to Database
Introduction to DatabaseIntroduction to Database
Introduction to Database
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Data Visualization - A Brief Overview
Data Visualization - A Brief OverviewData Visualization - A Brief Overview
Data Visualization - A Brief Overview
 
Database Management System ppt
Database Management System pptDatabase Management System ppt
Database Management System ppt
 
Data analysis
Data analysisData analysis
Data analysis
 
Data visualization introduction
Data visualization introductionData visualization introduction
Data visualization introduction
 
Data
DataData
Data
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Lecture1 introduction to big data
Lecture1 introduction to big dataLecture1 introduction to big data
Lecture1 introduction to big data
 
Data mining
Data miningData mining
Data mining
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSING
 
Exploratory data analysis with Python
Exploratory data analysis with PythonExploratory data analysis with Python
Exploratory data analysis with Python
 

Viewers also liked

PrePARe: What is 'data'?
PrePARe: What is 'data'?PrePARe: What is 'data'?
PrePARe: What is 'data'?dspace_cam
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Clement Levallois
 
Research Data Management Planning: problems and solutions
Research Data Management Planning: problems and solutionsResearch Data Management Planning: problems and solutions
Research Data Management Planning: problems and solutionsArhiv družboslovnih podatkov
 
Data Management for Librarians: An Introduction
Data Management for Librarians: An IntroductionData Management for Librarians: An Introduction
Data Management for Librarians: An IntroductionGarethKnight
 
Data, information & its attributes uwsb
Data, information & its attributes   uwsbData, information & its attributes   uwsb
Data, information & its attributes uwsbArnab Roy Chowdhury
 
DATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGEDATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGENeeraj Goswami
 

Viewers also liked (10)

PrePARe: What is 'data'?
PrePARe: What is 'data'?PrePARe: What is 'data'?
PrePARe: What is 'data'?
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
 
Research Data Management Planning: problems and solutions
Research Data Management Planning: problems and solutionsResearch Data Management Planning: problems and solutions
Research Data Management Planning: problems and solutions
 
Reserve bank of india
Reserve bank of india Reserve bank of india
Reserve bank of india
 
What is big data?
What is big data?What is big data?
What is big data?
 
Data Management for Librarians: An Introduction
Data Management for Librarians: An IntroductionData Management for Librarians: An Introduction
Data Management for Librarians: An Introduction
 
Data, information & its attributes uwsb
Data, information & its attributes   uwsbData, information & its attributes   uwsb
Data, information & its attributes uwsb
 
DATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGEDATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGE
 
Introduction to computers by abdul rahaman
Introduction to computers by abdul rahamanIntroduction to computers by abdul rahaman
Introduction to computers by abdul rahaman
 
Data Strategy
Data StrategyData Strategy
Data Strategy
 

Similar to What is "data"?

Bioinformatics&Databases.ppt
Bioinformatics&Databases.pptBioinformatics&Databases.ppt
Bioinformatics&Databases.pptBlackHunt1
 
Info systems databases
Info systems databasesInfo systems databases
Info systems databasesMR Z
 
Introduction to database
Introduction to databaseIntroduction to database
Introduction to databaseSuleman Memon
 
Multi-Model Data Query Languages and Processing Paradigms
Multi-Model Data Query Languages and Processing ParadigmsMulti-Model Data Query Languages and Processing Paradigms
Multi-Model Data Query Languages and Processing ParadigmsJiaheng Lu
 
Chapter 2 - Introduction to Data Science.pptx
Chapter 2 - Introduction to Data Science.pptxChapter 2 - Introduction to Data Science.pptx
Chapter 2 - Introduction to Data Science.pptxWollo UNiversity
 
Semi-automated Exploration and Extraction of Data in Scientific Tables
Semi-automated Exploration and Extraction of Data in Scientific TablesSemi-automated Exploration and Extraction of Data in Scientific Tables
Semi-automated Exploration and Extraction of Data in Scientific TablesElsevier
 
Kskv kutch university DBMS unit 1 basic concepts, data,information,database,...
Kskv kutch university DBMS unit 1  basic concepts, data,information,database,...Kskv kutch university DBMS unit 1  basic concepts, data,information,database,...
Kskv kutch university DBMS unit 1 basic concepts, data,information,database,...Dipen Parmar
 
Lecture-8-The-GIS-Database-Part-1.ppt
Lecture-8-The-GIS-Database-Part-1.pptLecture-8-The-GIS-Database-Part-1.ppt
Lecture-8-The-GIS-Database-Part-1.pptPrabin Pandit
 
Database Systems - Lecture Week 1
Database Systems - Lecture Week 1Database Systems - Lecture Week 1
Database Systems - Lecture Week 1Dios Kurniawan
 
L2 identifying photos
L2   identifying photosL2   identifying photos
L2 identifying photosMrJRogers
 
Binary Search Tree Investigation
Binary Search Tree InvestigationBinary Search Tree Investigation
Binary Search Tree InvestigationLindsay Alston
 
Database Management Systems 1
Database Management Systems 1Database Management Systems 1
Database Management Systems 1Nickkisha Farrell
 
Hector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsHector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsErika Marr
 

Similar to What is "data"? (20)

Bioinformatics&Databases.ppt
Bioinformatics&Databases.pptBioinformatics&Databases.ppt
Bioinformatics&Databases.ppt
 
Database
DatabaseDatabase
Database
 
Text Mining
Text MiningText Mining
Text Mining
 
Data Mining
Data MiningData Mining
Data Mining
 
Info systems databases
Info systems databasesInfo systems databases
Info systems databases
 
nosql.pptx
nosql.pptxnosql.pptx
nosql.pptx
 
Database_Introduction.pdf
Database_Introduction.pdfDatabase_Introduction.pdf
Database_Introduction.pdf
 
unit 1.pptx
unit 1.pptxunit 1.pptx
unit 1.pptx
 
Introduction to database
Introduction to databaseIntroduction to database
Introduction to database
 
Multi-Model Data Query Languages and Processing Paradigms
Multi-Model Data Query Languages and Processing ParadigmsMulti-Model Data Query Languages and Processing Paradigms
Multi-Model Data Query Languages and Processing Paradigms
 
Chapter 2 - Introduction to Data Science.pptx
Chapter 2 - Introduction to Data Science.pptxChapter 2 - Introduction to Data Science.pptx
Chapter 2 - Introduction to Data Science.pptx
 
Dma unit 1
Dma unit   1Dma unit   1
Dma unit 1
 
Semi-automated Exploration and Extraction of Data in Scientific Tables
Semi-automated Exploration and Extraction of Data in Scientific TablesSemi-automated Exploration and Extraction of Data in Scientific Tables
Semi-automated Exploration and Extraction of Data in Scientific Tables
 
Kskv kutch university DBMS unit 1 basic concepts, data,information,database,...
Kskv kutch university DBMS unit 1  basic concepts, data,information,database,...Kskv kutch university DBMS unit 1  basic concepts, data,information,database,...
Kskv kutch university DBMS unit 1 basic concepts, data,information,database,...
 
Lecture-8-The-GIS-Database-Part-1.ppt
Lecture-8-The-GIS-Database-Part-1.pptLecture-8-The-GIS-Database-Part-1.ppt
Lecture-8-The-GIS-Database-Part-1.ppt
 
Database Systems - Lecture Week 1
Database Systems - Lecture Week 1Database Systems - Lecture Week 1
Database Systems - Lecture Week 1
 
L2 identifying photos
L2   identifying photosL2   identifying photos
L2 identifying photos
 
Binary Search Tree Investigation
Binary Search Tree InvestigationBinary Search Tree Investigation
Binary Search Tree Investigation
 
Database Management Systems 1
Database Management Systems 1Database Management Systems 1
Database Management Systems 1
 
Hector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsHector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business Analytics
 

More from Clement Levallois

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsClement Levallois
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielleClement Levallois
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications businessClement Levallois
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginnersClement Levallois
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomClement Levallois
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le businessClement Levallois
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for businessClement Levallois
 
A Primer on Text Mining for Business
A Primer on Text Mining for BusinessA Primer on Text Mining for Business
A Primer on Text Mining for BusinessClement Levallois
 
The business stakes of data integration
The business stakes of data integrationThe business stakes of data integration
The business stakes of data integrationClement Levallois
 

More from Clement Levallois (11)

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielle
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginners
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroom
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le business
 
Twitter for beginners
Twitter for beginnersTwitter for beginners
Twitter for beginners
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for business
 
Data and personalization
Data and personalizationData and personalization
Data and personalization
 
A Primer on Text Mining for Business
A Primer on Text Mining for BusinessA Primer on Text Mining for Business
A Primer on Text Mining for Business
 
The business stakes of data integration
The business stakes of data integrationThe business stakes of data integration
The business stakes of data integration
 

Recently uploaded

Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Dr. Asif Anas
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17Celine George
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxraviapr7
 
Work Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashaWork Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashasashalaycock03
 
Optical Fibre and It's Applications.pptx
Optical Fibre and It's Applications.pptxOptical Fibre and It's Applications.pptx
Optical Fibre and It's Applications.pptxPurva Nikam
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfYu Kanazawa / Osaka University
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptxraviapr7
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxiammrhaywood
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?TechSoup
 
Patient Counselling. Definition of patient counseling; steps involved in pati...
Patient Counselling. Definition of patient counseling; steps involved in pati...Patient Counselling. Definition of patient counseling; steps involved in pati...
Patient Counselling. Definition of patient counseling; steps involved in pati...raviapr7
 
Riddhi Kevadiya. WILLIAM SHAKESPEARE....
Riddhi Kevadiya. WILLIAM SHAKESPEARE....Riddhi Kevadiya. WILLIAM SHAKESPEARE....
Riddhi Kevadiya. WILLIAM SHAKESPEARE....Riddhi Kevadiya
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxraviapr7
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptxSandy Millin
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.EnglishCEIPdeSigeiro
 
Department of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfDepartment of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfMohonDas
 
Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICESayali Powar
 
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTDR. SNEHA NAIR
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...M56BOOKSTORE PRODUCT/SERVICE
 

Recently uploaded (20)

Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptx
 
Finals of Kant get Marx 2.0 : a general politics quiz
Finals of Kant get Marx 2.0 : a general politics quizFinals of Kant get Marx 2.0 : a general politics quiz
Finals of Kant get Marx 2.0 : a general politics quiz
 
Work Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashaWork Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sasha
 
Optical Fibre and It's Applications.pptx
Optical Fibre and It's Applications.pptxOptical Fibre and It's Applications.pptx
Optical Fibre and It's Applications.pptx
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?
 
Patient Counselling. Definition of patient counseling; steps involved in pati...
Patient Counselling. Definition of patient counseling; steps involved in pati...Patient Counselling. Definition of patient counseling; steps involved in pati...
Patient Counselling. Definition of patient counseling; steps involved in pati...
 
March 2024 Directors Meeting, Division of Student Affairs and Academic Support
March 2024 Directors Meeting, Division of Student Affairs and Academic SupportMarch 2024 Directors Meeting, Division of Student Affairs and Academic Support
March 2024 Directors Meeting, Division of Student Affairs and Academic Support
 
Riddhi Kevadiya. WILLIAM SHAKESPEARE....
Riddhi Kevadiya. WILLIAM SHAKESPEARE....Riddhi Kevadiya. WILLIAM SHAKESPEARE....
Riddhi Kevadiya. WILLIAM SHAKESPEARE....
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptx
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.
 
Department of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfDepartment of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdf
 
Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICE
 
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
 

What is "data"?

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 Note • You will find terms squared like this in the slides. • These terms are part of your quizz assignment for the week, to be found on the online platform. • Often technical terms, it is vital that you know their meaning, as they are the basic vocabulary of data science.
  • 3. MK99 – Big Data 3 What you we learn here: • The definition of data • The many ways to speak about data.
  • 4. MK99 – Big Data 4 What is data? • Definition: – Originally, data is plural for “datum”, a Latin word – a “datum” is a single factual, a single entity, a single point of matter. – Datums are most often called “data points”. – Data represents a collection of data points. • We speak also of datasets instead of data (so a dataset is a collection of data points). – Today, “data” is used in a singular or plural form. -> “My data is…”, but we sometimes still hear “My data are…”
  • 5. MK99 – Big Data 5 Examples! • A date • A color • A grade • An address • A price • A number of friends • A longitude • An index of poverty • An item in a catalogue • A sound frequency • A list of favorite movies • A movie • A number of clicks on a web page • A duration • A book • An author of a book • A vote at an election • A still image • A measurement of CO2 • A response to a consumer survey • A purchase ticket • A curriculum vitae • Your blood pressure
  • 6. MK99 – Big Data 6 Data or Metadata? • Metadata: this is some data describing some other data. • Example: – The bibliographical reference describing a book. – Key takeaway: data without metadata can be worthless -> What would you do with a pile of 10,000 books without any indication on their title, authors, or date of publication? – The difference between data and metadata is not always relevant -> In the alumni network dataset, what is data and what is metadata? The metadata The data
  • 7. MK99 – Big Data 7 Data: how to talk about it • Example of some data point -> “Four more years. http://t.co/bAJE6Vom” This textual data is in digital form (because it is stored in bits on a computer, not by hand writing on a piece of paper) (as opposed to analog). The tweet is textual (as opposed to numerical. In programming, text can also be called a String) this is the type (or format) of the data The tweet appears plain text “plain text” is one sort of format for text. Others formats are JSON, XML or CSV this is the format of the data The text of the tweet is encoded in UTF-8 this is the encoding of the data The tweet is part of a list of tweets I collected this is the data structure The tweet is stored in a Word file on my laptop this is the format of the data Notice the ambiguity in the terminology!
  • 8. MK99 – Big Data 8 Data stored in tables: vocabulary Rows, or lines. Each represents a data point Columns. Each represents an attribute of the data. Header: these are the names of the attributes. A value. (can be empty). A spreadsheet, or a table. This is still the most common way to represent a dataset.
  • 9. MK99 – Big Data 9 Data and size. • The size of data gives an idea of what can be done with it and the challenges it might pose. • The size of a dataset can be expressed in number of datapoints. – Often called lines because we store them as lines in a spreadsheet • Or the size can be expressed in terms of the storage space the data takes on a computer drive (see next slide). – A dataset with 23,000 lines and 16 columns takes ~ 2.6Mb when presented as an Excel file.
  • 10. MK99 – Big Data 10 Bytes! 1 bit Can store a yes / no value 8 bits 1 byte (or octet) Can store a single letter ~ 1,000 bytes 1 kilobyte (kb) Can store a paragraph ~ 1 million bytes 1 megabyte (Mb) Can store a low res picture. ~ 1 billion bytes 1 gigabyte (Gb) Can store a movie ~ 1 trillion bytes 1 terabyte (Tb) Can store 1,000 movies. Size of commercial hard drives in 2014. ~ 1,000 trillion bytes 1 petabyte (Pb) 20 Pb = Google Maps in 2013 Most firms today
  • 11. MK99 – Big Data 11 Much more… • Make the readings for Week 1. • Watch the video on big data, also in Week 1. • Start following #bigdata and #dataanalytics on Twitter.