Online survey tools_google-forms_nv_nsh (2) (2)Vasantha Raju N
Google Forms is a free online survey tool that allows users to design and distribute web-based questionnaires. The paper discusses how Google Forms was used to design and distribute a survey on employability of library science graduates. A questionnaire on graduates' employment status and skills was created in Google Forms and distributed via an online forum and Facebook. Over 70 graduates responded online, with their responses automatically recorded in a spreadsheet for analysis. While online surveys have advantages, issues like sample selection, technology variations, and privacy must be considered, especially in developing countries with lower internet access.
Conferenza-evento presentazione di Roberto Monducci Rapporto sulla competitività dei settori produttivi. Quinta edizione
Aula Magna Istat, Roma 3 marzo 2017
Online survey tools_google-forms_nv_nsh (2) (2)Vasantha Raju N
Google Forms is a free online survey tool that allows users to design and distribute web-based questionnaires. The paper discusses how Google Forms was used to design and distribute a survey on employability of library science graduates. A questionnaire on graduates' employment status and skills was created in Google Forms and distributed via an online forum and Facebook. Over 70 graduates responded online, with their responses automatically recorded in a spreadsheet for analysis. While online surveys have advantages, issues like sample selection, technology variations, and privacy must be considered, especially in developing countries with lower internet access.
Conferenza-evento presentazione di Roberto Monducci Rapporto sulla competitività dei settori produttivi. Quinta edizione
Aula Magna Istat, Roma 3 marzo 2017
Intervento Giorgio Alleva, La pratica sportiva in Italia - Conferenza Stampa Coni-Istat - Salone d'Onore del CONI, Foro Italico, Roma, 23 febbraio 2017
The problems we are faced with in the 21st century require efficient analysis of ever more complex systems. This presentation outlines how such problems can be better understood and effectively solved if they are modeled as graphs or networks. We present two tools for to help solve such problems at scale: Titan, which is a real-time distributed graph database based on Apache Cassandra and Hbase and Faunus, which is a batch analytics framework for graphs based on Apache Hadoop. We discuss their current development status as of November 2012 and illustrate an example application for the GitHub coding network.
Organizations hugely rely on their customer sentiments to make business related decisions. Net Promoter Score is a reliable metric to understand customer satisfaction.This infographic highlights some points to remember to make the most out of the process.
Titan is a scalable graph database that can distribute and query graph data across multiple machines. This presentation provides a general introduction to graph computing and Titan in particular. It also focuses on some recent development for Titan 0.9 and TinkerPop 3.
Machine learning algorithms can adapt and learn from experience. The three main machine learning methods are supervised learning (using labeled training data), unsupervised learning (using unlabeled data), and semi-supervised learning (using some labeled and some unlabeled data). Supervised learning includes classification and regression tasks, while unsupervised learning includes cluster analysis.
The document discusses dashboard design and best practices. It provides examples of good and bad dashboard design, highlighting principles like removing clutter, unnecessary colors and duplication. Key sources discussed include Edward Tufte, Stephen Few, and their emphasis on visualizing data clearly and simply without distorting the meaning. The document advocates for dashboards that focus on communicating effectively by highlighting important data and trends.
RWDG Slides: Apply Data Governance to Agile EffortsDATAVERSITY
Data Governance Programs and Agile Data Projects are known to conflict when it comes to how the information and data is managed. Senior leadership has come to expect both the formal governance of data and data projects to be delivered quickly and effectively. These two requirements continue to cause problems.
Bob Seiner will discuss how to govern data during Agile projects during this month’s installment of the RWDG webinar series. It is inevitable that governance and Agile need to work together and complement each discipline’s intended results. Bob will share several considerations for bringing the two together.
During this webinar Bob will discuss:
- Looking for common ground to stand on
- The data goals of an Agile effort
- The Agile goals of a Data Governance program
- Bridging the gap and building understanding
- Steps to apply governance to Agile efforts
The document is about Edureka's Data Science Certification Training course. It covers the following key topics:
- An introduction to machine learning and how it works. Common machine learning techniques like supervised and unsupervised learning are discussed.
- Cluster analysis and k-means clustering are explained in detail as important unsupervised learning algorithms. K-means clustering partitions observations into k clusters where each observation belongs to the cluster with the nearest mean.
- A demo of k-means clustering is shown on a Netflix movie dataset to group movies based on characteristics and increase business. Testimonials from past learners praise the quality of Edureka's data science training.
Bitcoin price rallied in Q4 2015, increasing 82% to end the quarter at $430.05. Trading volume on exchanges also increased significantly, rising 424% compared to Q4 2014. However, the growth rate of quarterly VC investment in the bitcoin/blockchain sector slowed from 11% in Q3 2015 to 3% in Q4 2015, though it is unclear if this decline is sector-specific or due to the overall VC environment. Major developments in the quarter included the European Court of Justice ruling that bitcoin sales are not subject to VAT, and 42 major financial firms partnering with R3 to explore blockchain applications.
How to win data science competitions with Deep LearningSri Ambati
This document summarizes a presentation about how to win data science competitions using deep learning with H2O. It discusses H2O's architecture and capabilities for deep learning. It then demonstrates live modeling on Kaggle competitions, providing step-by-step explanations of building and evaluating deep learning models on three different datasets - an African soil properties prediction challenge, a display advertising challenge, and a Higgs boson machine learning challenge. It concludes with tips and tricks for deep learning with H2O and an invitation to the H2O World conference.
(1) This document provides a quick tour of machine learning concepts including the components, types, and step-by-step process of machine learning.
(2) It discusses machine learning applications in areas like credit approval, education, recommender systems, and reinforcement learning.
(3) The tour outlines the key components of a machine learning problem including the target function, training data, learning algorithm, hypothesis set, and learned hypothesis. It also distinguishes between supervised, unsupervised, and semi-supervised learning problems.
DAMA Webinar - Big and Little Data QualityDATAVERSITY
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
The document provides an overview of the state of blockchain in Q1 2016. Some key points:
- Total VC investment in bitcoin and blockchain startups rebounded in Q1 after declining in Q4 2015, though investment remains below previous highs. The average deal size and largest deals also increased.
- More VC funding is going to blockchain rather than bitcoin startups, as companies pivot away from currency applications. Over 40% of all funding now goes to blockchain and hybrid startups.
- The number of blockchain companies has grown significantly from 14 in Q1 2015 to 59 in Q1 2016. Investment is concentrating in the US, UK, Israel, Argentina, Sweden and Germany.
- Metrics like
Seminario Le fonti integrate per l’analisi del turismo in Campania, Napoli 15 dicembre 2016
Università degli studi di Napoli “Parthenope” Villa Doria d’Angri Sala Convegni via Francesco Petrarca, 80
OTTAVA GIORNATA ITALIANA DELLA STATISTICA
l nuovo Censimento permanente della popolazione.
Un racconto continuo del Paese
Catanzaro, 7 novembre 2018
Regione Calabria Cittadella Regionale Sala Verde
Viale Europa, Località Germaneto
L’HÔTELLERIE A VENEZIA
Una modesta proposta per imparare dal web
Giovanni Santoro
Novembre 2010
La modesta proposta che segue è diretta ad utilizzare la rete, il web, non solo per
prenotarsi le vacanze ma anche per indagare, oltre le usuali fonti, come e quanto il
sistema turistico apporti benefici alle destinazioni e alle loro comunità residenti.
Intervento Giorgio Alleva, La pratica sportiva in Italia - Conferenza Stampa Coni-Istat - Salone d'Onore del CONI, Foro Italico, Roma, 23 febbraio 2017
The problems we are faced with in the 21st century require efficient analysis of ever more complex systems. This presentation outlines how such problems can be better understood and effectively solved if they are modeled as graphs or networks. We present two tools for to help solve such problems at scale: Titan, which is a real-time distributed graph database based on Apache Cassandra and Hbase and Faunus, which is a batch analytics framework for graphs based on Apache Hadoop. We discuss their current development status as of November 2012 and illustrate an example application for the GitHub coding network.
Organizations hugely rely on their customer sentiments to make business related decisions. Net Promoter Score is a reliable metric to understand customer satisfaction.This infographic highlights some points to remember to make the most out of the process.
Titan is a scalable graph database that can distribute and query graph data across multiple machines. This presentation provides a general introduction to graph computing and Titan in particular. It also focuses on some recent development for Titan 0.9 and TinkerPop 3.
Machine learning algorithms can adapt and learn from experience. The three main machine learning methods are supervised learning (using labeled training data), unsupervised learning (using unlabeled data), and semi-supervised learning (using some labeled and some unlabeled data). Supervised learning includes classification and regression tasks, while unsupervised learning includes cluster analysis.
The document discusses dashboard design and best practices. It provides examples of good and bad dashboard design, highlighting principles like removing clutter, unnecessary colors and duplication. Key sources discussed include Edward Tufte, Stephen Few, and their emphasis on visualizing data clearly and simply without distorting the meaning. The document advocates for dashboards that focus on communicating effectively by highlighting important data and trends.
RWDG Slides: Apply Data Governance to Agile EffortsDATAVERSITY
Data Governance Programs and Agile Data Projects are known to conflict when it comes to how the information and data is managed. Senior leadership has come to expect both the formal governance of data and data projects to be delivered quickly and effectively. These two requirements continue to cause problems.
Bob Seiner will discuss how to govern data during Agile projects during this month’s installment of the RWDG webinar series. It is inevitable that governance and Agile need to work together and complement each discipline’s intended results. Bob will share several considerations for bringing the two together.
During this webinar Bob will discuss:
- Looking for common ground to stand on
- The data goals of an Agile effort
- The Agile goals of a Data Governance program
- Bridging the gap and building understanding
- Steps to apply governance to Agile efforts
The document is about Edureka's Data Science Certification Training course. It covers the following key topics:
- An introduction to machine learning and how it works. Common machine learning techniques like supervised and unsupervised learning are discussed.
- Cluster analysis and k-means clustering are explained in detail as important unsupervised learning algorithms. K-means clustering partitions observations into k clusters where each observation belongs to the cluster with the nearest mean.
- A demo of k-means clustering is shown on a Netflix movie dataset to group movies based on characteristics and increase business. Testimonials from past learners praise the quality of Edureka's data science training.
Bitcoin price rallied in Q4 2015, increasing 82% to end the quarter at $430.05. Trading volume on exchanges also increased significantly, rising 424% compared to Q4 2014. However, the growth rate of quarterly VC investment in the bitcoin/blockchain sector slowed from 11% in Q3 2015 to 3% in Q4 2015, though it is unclear if this decline is sector-specific or due to the overall VC environment. Major developments in the quarter included the European Court of Justice ruling that bitcoin sales are not subject to VAT, and 42 major financial firms partnering with R3 to explore blockchain applications.
How to win data science competitions with Deep LearningSri Ambati
This document summarizes a presentation about how to win data science competitions using deep learning with H2O. It discusses H2O's architecture and capabilities for deep learning. It then demonstrates live modeling on Kaggle competitions, providing step-by-step explanations of building and evaluating deep learning models on three different datasets - an African soil properties prediction challenge, a display advertising challenge, and a Higgs boson machine learning challenge. It concludes with tips and tricks for deep learning with H2O and an invitation to the H2O World conference.
(1) This document provides a quick tour of machine learning concepts including the components, types, and step-by-step process of machine learning.
(2) It discusses machine learning applications in areas like credit approval, education, recommender systems, and reinforcement learning.
(3) The tour outlines the key components of a machine learning problem including the target function, training data, learning algorithm, hypothesis set, and learned hypothesis. It also distinguishes between supervised, unsupervised, and semi-supervised learning problems.
DAMA Webinar - Big and Little Data QualityDATAVERSITY
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
The document provides an overview of the state of blockchain in Q1 2016. Some key points:
- Total VC investment in bitcoin and blockchain startups rebounded in Q1 after declining in Q4 2015, though investment remains below previous highs. The average deal size and largest deals also increased.
- More VC funding is going to blockchain rather than bitcoin startups, as companies pivot away from currency applications. Over 40% of all funding now goes to blockchain and hybrid startups.
- The number of blockchain companies has grown significantly from 14 in Q1 2015 to 59 in Q1 2016. Investment is concentrating in the US, UK, Israel, Argentina, Sweden and Germany.
- Metrics like
Seminario Le fonti integrate per l’analisi del turismo in Campania, Napoli 15 dicembre 2016
Università degli studi di Napoli “Parthenope” Villa Doria d’Angri Sala Convegni via Francesco Petrarca, 80
OTTAVA GIORNATA ITALIANA DELLA STATISTICA
l nuovo Censimento permanente della popolazione.
Un racconto continuo del Paese
Catanzaro, 7 novembre 2018
Regione Calabria Cittadella Regionale Sala Verde
Viale Europa, Località Germaneto
L’HÔTELLERIE A VENEZIA
Una modesta proposta per imparare dal web
Giovanni Santoro
Novembre 2010
La modesta proposta che segue è diretta ad utilizzare la rete, il web, non solo per
prenotarsi le vacanze ma anche per indagare, oltre le usuali fonti, come e quanto il
sistema turistico apporti benefici alle destinazioni e alle loro comunità residenti.
Un progetto che mira a dotare le regioni di un DMS per le funzioni integrate di informazione accoglienza e promo-commercializzazione e a livello nazionale un motore aggregatore.
La presentazione tratta delle modalità e della tempistica del processo di trasmissione all’Istat delle Liste Anagrafiche Comunali (LAC) per la realizzazione del 15° Censimento Generale della popolazione. Le LAC rappresenteranno la base informativa di riferimento per l’invio del questionario alle unità di rilevazione e per il monitoraggio e la gestione della rilevazione sul campo. Il processo di trasmissione delle LAC, in formato elettronico, da ciascun Comune all’Istat si fonda sul rispetto di standard tecnici e qualitativi indispensabili per il raggiungimento degli obiettivi istituzionali.
La sicurezza cibernetica della PA: una sfida necessariaCSI Piemonte
Intervento di Mario Terranova, AgID, al lunch seminar ICT per "Cybersecurity: evoluzione e nuove sfide per la PA" (Torino, CSI Piemonte, 28 novembre 2016)
13° Conferenza Nazionale di Statistica 4-5-6- luglio 2018
CAMPO DELLE PARTNERSHIP Il Portale della statistica pubblica
Centro Congressi Ergife via Aurelia 619
The document discusses several models for classifying and representing professions:
1) The Dictionary of Occupational Titles (DOT) which divides occupations into categories, groups worker functions, and guides occupational exploration.
2) The Répertoire Opérationnel des Métiers et des Emplois (ROME) which categorizes professions, represents job/occupation sheets, and identifies areas of professional mobility.
3) The Occupational Information Network (O*Net) which analyzes cognitive, psychomotor, and sensory abilities for occupations.
The document provides details on how each model organizes and represents different aspects of occupations and professions.
1. Microdati «amministrativi» sull'offerta turistica.
Una ricognizione ragionata.
Aldo Scarnera
DIRM/RMF
Flussi di dati amministrativi sull'offerta turistica. Prospettive per la produzione statistica ufficiale
Istat, Aula Magna. Roma 14 Marzo 2017.,
2. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
La proposal «Riprogettazione/ottimizzazione dei flussi per la raccolta dati sul Turismo» (Project id 318/2016)
Sommario
I nuovi modi dell’offerta ricettiva: peer-to-peer rental platform e nuovi servizi nella net economy
L’identificazione degli alloggiati ex art. 109 del Testo Unico delle Leggi di Pubblica Sicurezza (TULPS)
Quattro sistemi informativi regionali
Il fisco e i redditi prodotti dall’offerta di servizi e/o strutture ricettive. L’imposizione sul soggiorno
Il convitato di pietra
Che fare?
3. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
http://www.economist.com/news/leaders/21573104-internet-everything-hire-rise-sharing-economy
4. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Alloggi offerti on-line in Italia (al 24 settembre 2016) da alcuni siti di
intermediazione e strutture ricettive rilevate dalla statistica ufficiale al 2015
Alloggi
Siti
https://www.9flats.com/it 14874
https://www.airbnb.it/ >300(222786*)
http://www.cottages.com/ 740
https://www.holidaylettings.co.uk/italy/ 96065
https://www.homeaway.co.uk 123868
https://www.housetrip.com/ 95930
https://www.ownersdirect.co.uk/ 13271
https://www.clickstay.com/ 1617
http://www.villas.com/it/ 53483
http://www.wimdu.com/ >500
https://www.jamesvillas.co.uk 113
https://www.sabbaticalhomes.com/ 331
Istat. Strutture ricettive rilevate al 2015 (dati.istat.it) 167718
Fonte: dati rilevati attraverso funzioni di ricerca semplice su ciascun sito
*Dati stimati da Federalberghi ad Agosto 2016 ( http://bit.ly/2iE6Igg )
6. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Codebookedenotazionidellestrutture utilizzati nelPortale AlloggiatidellaPolizia diStato
Codice CATEGORIA CLASSE
0 CASA DI CURA NESSUNA
1 ALBERGO
NESSUNA
1 STELLA
2 STELLE
3 STELLE
4 STELLE
5 STELLE
5 STELLE LUSSO
2 HOTEL
NESSUNA
1 STELLA
2 STELLE
3 STELLE
4 STELLE
5 STELLE
5 STELLE LUSSO
3 BED AND BREAKFAST
NESSUNA
GARNI’
4 AFFITTACAMERE
NESSUNA
CASA VACANZE
CASA PER FERIE
ALLOGGI TURISTICI
CASA RELIGIOSA
RESIDENZA D'EPOCA
CASA FAMIGLIA
5 MOTEL NESSUNA
6 VILLAGGIO NESSUNA
7 RESIDENCE NESSUNA
8 PENSIONE/LOCANDA
NESSUNA
CASA PER ANZIANI
COMUNITA' EDUCATIVA
9 RIFUGIO ALPINO NESSUNA
10 OSTELLO
NESSUNA
PENSIONATO PER STUDENTI
CENTRO SOGGIORNO STUDI
CENTRO VACANZA PER RAGAZZI
ONLUS
11 CAMPEGGIO NESSUNA
12 AGRITURISMO
NESSUNA
COUNTRY HOUSE - TURISMO RURALE
13 APPARTAMENTO AD USO TURISTICO
NESSUNA
LOCAZIONE PARZIALE
LOCAZIONE PURA
99 ALTRO NESSUNA
100 GESTIONE APPARTAMENTI
NESSUNA
CASA VACANZE
CASA PER FERIE
ALLOGGI TURISTICI
CASA RELIGIOSA
AFFITTACAMERE
APPARTAMENTO AD USO TURISTICO
AGENZIA
ALBERGO DIFFUSO
Fonte: Centro Elettronico Nazionale della Polizia di Stato
8. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Acquisire microdati dai gestionali delle strutture ricettive. Filosofie,
obblighi , facilitazioni e premialità
9. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Microdati dai gestionali delle strutture ricettive ...
Da check in
Elaborati da check out e da sistema
10. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
L’accreditamento: Segnalazione Certificata di Inizio Attività via SUAP
11. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Colonna 8 (Contratti non superiori 30 gg.):
barrare la casella nel caso di contratto di
locazione non registrato di durata non superiore
a trenta giorni complessivi nell’anno (per tale
tipologia di contratto è previsto l’obbligo di
registrazione solo in caso d’uso). Se è barrata
questa casella, non vanno compilate né le
colonne da 3 a 6 relative agli estremi di
registrazione del contratto né la colonna 7
relativa al codice identificativo del contratto.
12. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Collective sensing e dintorni (Fonia mobile, Passaporto digitale, QR
Code, Guest Card, Web scraping)
13. Microdati «amministrativi» sull'offerta turistica. Una ricognizione ragionata.
Aldo Scarnera, Istat, Roma 14 Marzo 2017
Riassumendo:
Le informazioni raccolte dalla Polizia di Stato sugli alloggiati nelle strutture ricettive coprono
l’intero spettro dell’offerta ricettiva e delle tipologie di ospiti (al netto della evasione), sono solo
parzialmente complete/precise rispetto alle esigenze informative della rilevazione sul movimento
turistico (manca la residenza dell’alloggiato, i pernottamenti vanno considerati stime) e vanno
trattate per selezionare e per identificare le tipologie delle strutture ricettive eligibili. Tale
trattamento (e i relativi costi) potrebbe anche essere accettato dal ministero sotto forma di
allineamento della classificazione delle strutture ricettive eligibili da fare sull’intera base di dati
e da adottare per il futuro ma non potrebbe comunque distinguere i turisti fra gli ospitati. In
ogni caso solo apparentemente sarebbero disponibili in «tempi brevi» come microdati perché i
vincoli normativi in materia di privacy a cui sono soggetti sono particolarmente stringenti e la
loro modifica potrebbe comportare interventi legislativi di peso. Sono del tutto inutilizzabili per
la rilevazione sulla capacità ricettiva che, nel caso, dovrà continuare ad essere svolta nel modo
tradizionale o con tecniche alternative da individuare.
Per contro i dati rilasciati dai sistemi informativi regionali considerati sono completi rispetto
alle esigenze informative attuali di entrambe le rilevazioni sull’offerta, sono già disponibili in
forma di microdati con vincoli di tipo statistico-gestionali rispetto alla privacy e possono essere
flessibili rispetto allo spettro di informazioni rilevabili. I sistemi sono progettati come open
source e le software house che producono gestionali commerciali sono messe in grado di
implementare applicativi di rilevazione e di condivisione dei dati richiesti.
Tuttavia, tali sistemi vanno armonizzati (magari con l’impegno diretto dell’Istituto e il tavolo di
lavoro proposto a conclusione del seminario CISIS a Napoli) e diffusi a tutte le Regioni che per
farlo potrebbero dover legiferare in materia e dotarsi di assetti ICT adeguati.
In entrambi i casi, e al netto della prospettiva dei Big Data e dell’interruzione della serie
statistica, l’Istituto dovrà adeguare protocolli, applicativi e sistemi dedicati per attivare flussi
tempestivi e sostanzialmente continui di moli consistenti di microdati, ripensare rispetto a
questi nuove modalità e nuove opportunità della produzione statistica, senza escludere la
possibilità di individuare forme diverse di collaborazione fra i soggetti Sistan interessati.