Overview sul mondo Wikipedia per un pubblico di aziende. Alcuni tip sul perché (sì e no) le aziende dovrebbero almeno monitorare la loro voce su Wikipedia.
A startup is a project and as every project must be led. In this lesson we discuss project management, a bit about agile project management and some useful tools
Come le donne usano i motori di ricerca, con un lampante esempio (personale) di come è meglio *non* usarli e un po' di storia.. perché quando racconto qualcosa di cui non sono un guru mi piace condividere anche la mia esperienza
Why should I communicate? Which are my needs?
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (Social media vs industrial media, social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, storytelling, goals and how I can sum it up in my editorial plan.
How funding works and which are the investors in Europe.
What is equity capital (seed investment, business angel, venture capitalist, crowd funding) and loan capital.
Other funding sources (announcements, acces to credit).
Overview sul mondo Wikipedia per un pubblico di aziende. Alcuni tip sul perché (sì e no) le aziende dovrebbero almeno monitorare la loro voce su Wikipedia.
A startup is a project and as every project must be led. In this lesson we discuss project management, a bit about agile project management and some useful tools
Come le donne usano i motori di ricerca, con un lampante esempio (personale) di come è meglio *non* usarli e un po' di storia.. perché quando racconto qualcosa di cui non sono un guru mi piace condividere anche la mia esperienza
Why should I communicate? Which are my needs?
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (Social media vs industrial media, social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, storytelling, goals and how I can sum it up in my editorial plan.
How funding works and which are the investors in Europe.
What is equity capital (seed investment, business angel, venture capitalist, crowd funding) and loan capital.
Other funding sources (announcements, acces to credit).
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix.
Storytelling fundamentals (from Propp to Andrea Fontana) and examples. Marketing perspectives on storytelling. Storytelling with data techniques. Hints and examples
Visual communication of qualitative and quantitative data (v. 2021 ITA)Frieda Brioschi
Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics. Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser.
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Introduction to data classification. Back to origins: history of libraries and their classification methods. Some examples of classification in different areas.
How to collect and organize data (v. ITA 2021)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
Storytelling fundamentals (from Propp to Andrea Fontana) and examples. Marketing perspectives on storytelling. Storytelling with data techniques. Hints and examples
Visual communication of qualitative data (v. 2020 ITA)Frieda Brioschi
Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser
Visual communication of quantitative data (v. 2020 ITA)Frieda Brioschi
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
The document discusses visual perception and how information is processed. It covers topics like cognition, cognitive science, learning styles, sensation and perception, visual memory, gestalt principles, and limits of short-term memory. Examples are provided to illustrate concepts like preattentive attributes, chunking, and how visualization can take advantage of human perception to effectively communicate data and patterns.
This document provides an overview of data mining and data aggregation basics. It discusses key concepts such as the phases of the data mining process according to the CRISP-DM framework which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It also discusses different types of data aggregation including time and spatial aggregation and summarization techniques such as calculating the mean, count, maximum, minimum, mode, range, and sum. Additionally, it presents different ways of visualizing data including tables, bar charts, histograms, pie charts, and line graphs.
This document outlines the process of data-informed decision making. It discusses acquiring relevant data from internal and external sources, analyzing the data to find patterns and relationships, applying personal expertise when interpreting the data, announcing and implementing decisions to stakeholders, and assessing the outcomes of decisions to continually improve the process. The goal is to formulate questions, leverage different types of data analysis, make evidence-based decisions, and monitor their impacts over time.
This document provides an overview of data organization and classification in libraries and other domains. It begins by discussing the history of libraries and early classification systems used in ancient Mesopotamia and China. It then covers modern library classification standards like the Dewey Decimal System and subject-based organization. The document also examines classification of natural phenomena like volcanoes, stars, satellites, and languages. It concludes by discussing classification of administrative divisions and examples of categorizing a country's average life expectancy data.
How to collect and organize data (v. ITA 2020)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix.
Storytelling fundamentals (from Propp to Andrea Fontana) and examples. Marketing perspectives on storytelling. Storytelling with data techniques. Hints and examples
Visual communication of qualitative and quantitative data (v. 2021 ITA)Frieda Brioschi
Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics. Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser.
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Introduction to data classification. Back to origins: history of libraries and their classification methods. Some examples of classification in different areas.
How to collect and organize data (v. ITA 2021)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
Storytelling fundamentals (from Propp to Andrea Fontana) and examples. Marketing perspectives on storytelling. Storytelling with data techniques. Hints and examples
Visual communication of qualitative data (v. 2020 ITA)Frieda Brioschi
Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser
Visual communication of quantitative data (v. 2020 ITA)Frieda Brioschi
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
The document discusses visual perception and how information is processed. It covers topics like cognition, cognitive science, learning styles, sensation and perception, visual memory, gestalt principles, and limits of short-term memory. Examples are provided to illustrate concepts like preattentive attributes, chunking, and how visualization can take advantage of human perception to effectively communicate data and patterns.
This document provides an overview of data mining and data aggregation basics. It discusses key concepts such as the phases of the data mining process according to the CRISP-DM framework which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It also discusses different types of data aggregation including time and spatial aggregation and summarization techniques such as calculating the mean, count, maximum, minimum, mode, range, and sum. Additionally, it presents different ways of visualizing data including tables, bar charts, histograms, pie charts, and line graphs.
This document outlines the process of data-informed decision making. It discusses acquiring relevant data from internal and external sources, analyzing the data to find patterns and relationships, applying personal expertise when interpreting the data, announcing and implementing decisions to stakeholders, and assessing the outcomes of decisions to continually improve the process. The goal is to formulate questions, leverage different types of data analysis, make evidence-based decisions, and monitor their impacts over time.
This document provides an overview of data organization and classification in libraries and other domains. It begins by discussing the history of libraries and early classification systems used in ancient Mesopotamia and China. It then covers modern library classification standards like the Dewey Decimal System and subject-based organization. The document also examines classification of natural phenomena like volcanoes, stars, satellites, and languages. It concludes by discussing classification of administrative divisions and examples of categorizing a country's average life expectancy data.
How to collect and organize data (v. ITA 2020)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
7. 2004, Joichi Ito One thing that has struck me is that many, if not most, of the people I’ve met from the community who are involved in managing Wikipedia seem to be women. [..]..is it something about Wikipedia that attracts powerful women? Rielab da photo by globetrotter1937 on Flickr, CC-BY-SA 2.0
8. Jimmy Wales I have to admit that at Wikipedia meetups the ratio of men to women is about 8:1 overall. But that ratio does not hold in ever aspect of the community, and it is absolutely right to say that there are strong women who are in major leadership roles. Photo by katepit on Flickr, CC-BY-NC-SA 2.0
9. Wikichix Mailing list + wiki di “supporto” dedicata alle donne di wiki*. Un ambiente dove discutere di disparità dei sessi e di come incoraggiare gli utenti donna. Photo by johnmueller on Flickr, CC-BY-NC-ND 2.0
12. Quante siamo? Fonte: da una slide di Enrico Gasperini, i cui dati provengo da Audiweb –AW Trends, Sintesi dei risultati della Ricerca di Base sulla diffusione dell'online in Italia” e AW Database Sett 2009 63,1% 66,6% 59,7% Photo by lchifi on Flickr, CC-BY 2.0
13. Girls dont wiki At least they don't seem to. Photo by rafael_zocoler on DeviantArt, CC-BY-NC-ND 3.0
14. ..e voi, ci siete? Photo by gigi_murru on Flickr, CC-BY-SA 2.0
15. Donne nella storia, il portale “ A quando il portale:uomini nella storia? Così siamo alla pari, almeno in Wikipedia ci sarà parità tra uomo e donna.” - Coralba11- Photo by motti on Flickr, CC-BY-NC-SA 2.0
16. Provoca? La donna, nel paradiso terrestre, ha morso il frutto dell'albero della conoscenza dieci minuti prima dell'uomo: da allora ha sempre conservato quei dieci minuti di vantaggio. - Alphonse Karr - Photo by vanessaO on Flickr, CC-BY 2.0
17. Porta equilibrio? Politica, teatro, cinema, sport, scienza, letteratura, religione, filosofia, arte, musica, personaggi mitologici, letterari, di fantasia, attiviste.. Photo by Sara Scamarcia, CC-BY-SA 2.0
18. Photo by shiratskii on Flickr, CC-BY-NC-ND 2.0 Donne in vetrina Bianca Maria Visconti Lalla Fadhma Sandy Denny Audrey Hepburn Maria Stuarda Alessandra Feodorovna Maria Antonietta Margherita di Valois