This document provides an overview of a training on using the Kobo data collection tool. The training covers designing a data collection plan and form, collecting and combining data, analyzing data, and becoming a super user of Kobo. The agenda outlines sessions on designing questions and a data model, data collection, combining and processing data, data analysis and dissemination, and programming in Kobo. It emphasizes the importance of planning before data collection and assessing data needs, including what to measure, why to measure it, and how to measure it.
This document provides an overview of a training on using Kobo, an online data collection tool, held in Kabul, Afghanistan on April 24, 2018. It discusses designing data collection forms, including assessing data needs, creating questions, and building a data model. It also covers deploying forms for online data collection and accessing collected data. The training covered all stages of the data management cycle from design to analysis and dissemination. [/SUMMARY]
The document outlines an agenda and objectives for a training on using the Kobo data collection tool in Afghanistan. The training covers various aspects of designing and implementing a data collection process, including defining needs, creating a data model, sampling techniques, question design, and using common operational datasets. The goal is to help participants independently develop data collection tools and establish a community of practice around data management.
Active learning is a machine learning technique that can perform well with less training data by allowing algorithms to select the most informative samples to be labelled. It trains an initial model on labelled data, evaluates the model on unlabelled samples, and selects samples to label that will most improve the model. Common strategies to select samples include least confidence, margin sampling, and entropy sampling. Active learning is useful when labelling data is expensive and can reduce labelling requirements for tasks like natural language processing.
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
This 21 slide presentation Needs Analysis is Module 2 of a nine (9) module online course for adult education policy makers and practitioners to complement an innovative toolkit to guide adult education policy and practice.
Participation in adult education varies significantly across states and regions of Europe! Why? Evidence and literature suggests a wide disparity in policy making, programming and implementation skills in the adult education sector across Europe. It is imperative that policy makers and programme managers address this disparity to foster life-long learning for a smart-sustainable Europe (see EU2020 https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-economic-governance-monitoring-prevention-correction/european-semester/framework/europe-2020-strategy_en) and to achieve a European target of 15% of the adult population engaged in learning.
In response to this challenge, the ERASMUS+ DIMA project (See https://dima-project.eu/index.php/en/, 2015 to 2017) developed a practical 9 module online course to complement an innovative toolkit to guide adult education policy and practice. The DIMA toolkit (See https://dima-project.eu/index.php/en/toolkit) introduces tools for developing, implementing, and monitoring adult education policies, strategies, and practices.
Author: Michael Kenny and DIMA Project partners (https://dima-project.eu/index.php/en/partners)
This document discusses research methods and data collection techniques. It provides examples of a descriptive study design being used to explore employee empowerment policies and strategies. It also describes various quantitative and qualitative data collection methods, noting their advantages like collecting information from many people quickly, and disadvantages like not being able to determine causation. Cross-sectional studies are described as collecting data at a single point in time, making them less costly but unable to examine changes over time.
The document discusses the steps in an AI project cycle which includes problem scoping, data acquisition, data exploration, modelling, and evaluation. It provides examples of each step, such as identifying a problem in problem scoping, collecting reliable data from various sources in data acquisition, arranging data in tables and charts for better understanding in data exploration, creating models from visualized data in modelling, and testing model performance in evaluation.
This document provides an overview of a training on using Kobo, an online data collection tool, held in Kabul, Afghanistan on April 24, 2018. It discusses designing data collection forms, including assessing data needs, creating questions, and building a data model. It also covers deploying forms for online data collection and accessing collected data. The training covered all stages of the data management cycle from design to analysis and dissemination. [/SUMMARY]
The document outlines an agenda and objectives for a training on using the Kobo data collection tool in Afghanistan. The training covers various aspects of designing and implementing a data collection process, including defining needs, creating a data model, sampling techniques, question design, and using common operational datasets. The goal is to help participants independently develop data collection tools and establish a community of practice around data management.
Active learning is a machine learning technique that can perform well with less training data by allowing algorithms to select the most informative samples to be labelled. It trains an initial model on labelled data, evaluates the model on unlabelled samples, and selects samples to label that will most improve the model. Common strategies to select samples include least confidence, margin sampling, and entropy sampling. Active learning is useful when labelling data is expensive and can reduce labelling requirements for tasks like natural language processing.
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
This 21 slide presentation Needs Analysis is Module 2 of a nine (9) module online course for adult education policy makers and practitioners to complement an innovative toolkit to guide adult education policy and practice.
Participation in adult education varies significantly across states and regions of Europe! Why? Evidence and literature suggests a wide disparity in policy making, programming and implementation skills in the adult education sector across Europe. It is imperative that policy makers and programme managers address this disparity to foster life-long learning for a smart-sustainable Europe (see EU2020 https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-economic-governance-monitoring-prevention-correction/european-semester/framework/europe-2020-strategy_en) and to achieve a European target of 15% of the adult population engaged in learning.
In response to this challenge, the ERASMUS+ DIMA project (See https://dima-project.eu/index.php/en/, 2015 to 2017) developed a practical 9 module online course to complement an innovative toolkit to guide adult education policy and practice. The DIMA toolkit (See https://dima-project.eu/index.php/en/toolkit) introduces tools for developing, implementing, and monitoring adult education policies, strategies, and practices.
Author: Michael Kenny and DIMA Project partners (https://dima-project.eu/index.php/en/partners)
This document discusses research methods and data collection techniques. It provides examples of a descriptive study design being used to explore employee empowerment policies and strategies. It also describes various quantitative and qualitative data collection methods, noting their advantages like collecting information from many people quickly, and disadvantages like not being able to determine causation. Cross-sectional studies are described as collecting data at a single point in time, making them less costly but unable to examine changes over time.
The document discusses the steps in an AI project cycle which includes problem scoping, data acquisition, data exploration, modelling, and evaluation. It provides examples of each step, such as identifying a problem in problem scoping, collecting reliable data from various sources in data acquisition, arranging data in tables and charts for better understanding in data exploration, creating models from visualized data in modelling, and testing model performance in evaluation.
Citi Global T4I Accelerator Data and Analytics PresentationMarquis Cabrera
Presented on data and analytics for the Citi T4I Global Social Good Accelerator, which is an open innovation initiative seeking to source tech solutions that promote integrity around the world.
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
Advances in Exploratory Data Analysis, Visualisation and Quality for Data Cen...Hima Patel
It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this session, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.
Qualitative research data is interpretive and descriptive in nature. The best way to organize and manage qualitative data is through coding or grouping the data to look for patterns in the findings. Good qualitative data management involves having a clear file naming system, a data tracking system, and securely storing data during and after the research process. Qualitative data collection methods aim to understand people's experiences through techniques like interviews, observations, and focus groups to gain an in-depth perspective.
Turning Data into Infographics: An Interactive Workshop for Problem SolversUNCResearchHub
This document provides an overview of creating infographics from data. It discusses finding relevant data from government, commercial, think tank and hybrid sources. It also covers best practices for exploring data to find patterns and stories, visualizing data in infographics, and critiquing infographics. The workshop teaches how to plan infographics based on data about food insecurity in the US and sketch an example infographic on this topic. Resources for creating and finding inspiration for infographics are also listed.
Global Insight is a platform for exploring economic and statistical databases, not a database itself. It provides access to data sources depending on the user's institutional subscriptions. The document outlines the steps to use Global Insight, which include logging in, selecting a data source and search terms or browsing files, choosing data series, setting date ranges, selecting an output format, and downloading results. Users can obtain time series data in various frequencies like monthly, quarterly or yearly from sources like Statistics South Africa.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
This document provides instructions for a Year 10 science research project on genetics and evolution. Students are to complete a digital portfolio on the topic of gene technology. The portfolio will include sections on defining the research task, locating relevant information from multiple sources, evaluating sources, organizing findings, and presenting information while referencing sources properly. A self-evaluation is also required to reflect on the research process. The portfolio is to be submitted through the school's Edmodo account by the due date of March 18, 2012.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
ODSC East 2017: Data Science Models For GoodKarry Lu
Abstract: The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved
H2O World - Intro to Data Science with Erin LedellSri Ambati
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Artificial intelligence: Simulation of IntelligenceAbhishek Upadhyay
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
This document discusses building a recommendation system for e-commerce. It begins by noting the importance of recommendations, with over 30% of online purchases coming from recommendations. It then discusses gathering data, both explicitly via ratings and reviews, and implicitly via user actions. Main approaches covered include content-based filtering, collaborative filtering using user-user and item-item similarities, and matrix factorization. The document also addresses challenges like sparsity, cold starts, scalability and privacy considerations in implementing recommendation systems.
This document provides an overview of key aspects of data preparation and processing for data mining. It discusses the importance of domain expertise in understanding data. The goals of data preparation are identified as cleaning missing, noisy, and inconsistent data; integrating data from multiple sources; transforming data into appropriate formats; and reducing data through feature selection, sampling, and discretization. Common techniques for each step are outlined at a high level, such as binning, clustering, and regression for handling noisy data. The document emphasizes that data preparation is crucial and can require 70-80% of the effort for effective real-world data mining.
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Secrets of a Successful Sale: Optimizing Your Checkout ProcessAggregage
https://www.onlineretailtoday.com/frs/26905197/secrets-of-a-successful-sale--optimizing-your-checkout-process
Once upon a time, in the vast realm of online commerce, there lived a humble checkout button overlooked by many. Yet, within its humble click lay the power to transform a mere visitor into a loyal customer. 🧐 💡
Getting checkout right can mark the difference between a successful sale and an abandoned cart, yet many businesses fail to make payments a part of their commerce strategy even when it has a direct impact on revenue. But payments are just one part of a chain. What’s the next touch point? How do you use the data sitting behind a payment to find the next loyal customer?
In this session you’ll learn:
• The integral relationship between payment experience and customer satisfaction
• Proven methods for optimizing the checkout journey
• Leveraging payments data for personalized marketing and enhanced customer loyalty
• Gain invaluable insights into consumer behavior across online and offline channels through data
Citi Global T4I Accelerator Data and Analytics PresentationMarquis Cabrera
Presented on data and analytics for the Citi T4I Global Social Good Accelerator, which is an open innovation initiative seeking to source tech solutions that promote integrity around the world.
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
Advances in Exploratory Data Analysis, Visualisation and Quality for Data Cen...Hima Patel
It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this session, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.
Qualitative research data is interpretive and descriptive in nature. The best way to organize and manage qualitative data is through coding or grouping the data to look for patterns in the findings. Good qualitative data management involves having a clear file naming system, a data tracking system, and securely storing data during and after the research process. Qualitative data collection methods aim to understand people's experiences through techniques like interviews, observations, and focus groups to gain an in-depth perspective.
Turning Data into Infographics: An Interactive Workshop for Problem SolversUNCResearchHub
This document provides an overview of creating infographics from data. It discusses finding relevant data from government, commercial, think tank and hybrid sources. It also covers best practices for exploring data to find patterns and stories, visualizing data in infographics, and critiquing infographics. The workshop teaches how to plan infographics based on data about food insecurity in the US and sketch an example infographic on this topic. Resources for creating and finding inspiration for infographics are also listed.
Global Insight is a platform for exploring economic and statistical databases, not a database itself. It provides access to data sources depending on the user's institutional subscriptions. The document outlines the steps to use Global Insight, which include logging in, selecting a data source and search terms or browsing files, choosing data series, setting date ranges, selecting an output format, and downloading results. Users can obtain time series data in various frequencies like monthly, quarterly or yearly from sources like Statistics South Africa.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
This document provides instructions for a Year 10 science research project on genetics and evolution. Students are to complete a digital portfolio on the topic of gene technology. The portfolio will include sections on defining the research task, locating relevant information from multiple sources, evaluating sources, organizing findings, and presenting information while referencing sources properly. A self-evaluation is also required to reflect on the research process. The portfolio is to be submitted through the school's Edmodo account by the due date of March 18, 2012.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
ODSC East 2017: Data Science Models For GoodKarry Lu
Abstract: The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved
H2O World - Intro to Data Science with Erin LedellSri Ambati
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Artificial intelligence: Simulation of IntelligenceAbhishek Upadhyay
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
This document discusses building a recommendation system for e-commerce. It begins by noting the importance of recommendations, with over 30% of online purchases coming from recommendations. It then discusses gathering data, both explicitly via ratings and reviews, and implicitly via user actions. Main approaches covered include content-based filtering, collaborative filtering using user-user and item-item similarities, and matrix factorization. The document also addresses challenges like sparsity, cold starts, scalability and privacy considerations in implementing recommendation systems.
This document provides an overview of key aspects of data preparation and processing for data mining. It discusses the importance of domain expertise in understanding data. The goals of data preparation are identified as cleaning missing, noisy, and inconsistent data; integrating data from multiple sources; transforming data into appropriate formats; and reducing data through feature selection, sampling, and discretization. Common techniques for each step are outlined at a high level, such as binning, clustering, and regression for handling noisy data. The document emphasizes that data preparation is crucial and can require 70-80% of the effort for effective real-world data mining.
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Secrets of a Successful Sale: Optimizing Your Checkout ProcessAggregage
https://www.onlineretailtoday.com/frs/26905197/secrets-of-a-successful-sale--optimizing-your-checkout-process
Once upon a time, in the vast realm of online commerce, there lived a humble checkout button overlooked by many. Yet, within its humble click lay the power to transform a mere visitor into a loyal customer. 🧐 💡
Getting checkout right can mark the difference between a successful sale and an abandoned cart, yet many businesses fail to make payments a part of their commerce strategy even when it has a direct impact on revenue. But payments are just one part of a chain. What’s the next touch point? How do you use the data sitting behind a payment to find the next loyal customer?
In this session you’ll learn:
• The integral relationship between payment experience and customer satisfaction
• Proven methods for optimizing the checkout journey
• Leveraging payments data for personalized marketing and enhanced customer loyalty
• Gain invaluable insights into consumer behavior across online and offline channels through data
It’s no secret that the marketing landscape is growing increasingly complex, with numerous channels, privacy regulations, signal loss, and more. One of the biggest problems facing marketers today is that they’re experiencing data deluge and data drought simultaneously.
Bliss Point by Tinuti addresses these challenges by providing a single, user-friendly platform for measuring what marketers previously struggled to measure. With Bliss Point, you can move beyond simply validating past actions and instead use measurement to guide real-time decision-making on what should happen next.
Join our product experts for a live demonstration of Bliss Point. Discover how it can empower your brand with the tools and insights needed to optimize each channel, across your entire media mix, and your overall brand performance.
3. Session 1 Objectives
Learn skills to independently develop simple form, to
modify existing one, to extract and analyze the data
collected
To create a community of practice with an
understanding of data collection & able to develop
and relay a data collection campaign
Objective, to get you as Super User
of Kobo data collection tool
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
4. IM
generalist
IM Data
Collection
Specialist
Session 1 Evolution Tree of Users
N ot
using …
The Boss
Basic
User
No excel,
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
No computer…
Basic excel,
Using on line forms
Advanced
User
Automatic sum & filters on Excel
Did participate on a survey…
Pivot table on Excel
Did design a survey
Super
User
6. Information Management is a cycle of six stages
Design Combining
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
7. Why Online/mobile data collection tool?
??
?
??
REPO R T
??
??
Q UA L I T Y
C O N T ROL
??
?
DATA
EN T RY
??
I N T ER
v I E W
FIELD
AG E N T
?
??
?
RAW
DATA
A N A LY SIS
??
?
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
8. Why Kobo ?
Acquee
COMMANDmobile®
CommCare
CommTrack
CSPro
CyberTracker
DevInfo
do Forms
droidSURVEY
Enketo Smart Paper
EpiCollect
FrontlineSMS
Fulcrum
GeoChat
GeoPoll
Humanitarian Data
Toolkit
Imogene
iSURVEY
KoBo
Last Mile Mobile
Solution
PSI Mobile –
Fusion
RapidSMS
RDMS
Smap
SoukTel
Telerivet
ViewWorld
Voxiva
Wepi
Magpi
Majella Insight
Mobenzi Researcher
Nokia Data
Gathering system
Oasis Mobile
Open Data Kit
openXdata
Pendragon
Poimapper
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
10. Information Management is a cycle of six stages
Design Combining
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
11. Session 2. Making a plan is first step
to any data collection!
Plan before you start data collection;
Define roles and assign responsibilities for each step;
Budget!
Recognize stakeholders;
Define expected outcomes;
Define data model;
Create, manage and document data management system
Document strategy;
Revisit the plan throughout project life cycle.
Afghanistan Shelter Cluster
ShelterCluster.org
Coordinating Humanitarian Shelter
12. Session 2. Assess data needs
Strategic
WHAT you want to measure
WHY you need to measure
HOW you will measure
Operational
What is level of data collection?
Design your data model
Explain your data
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13. Session 2. WHAT you want
to measure
Population data
• # IDPs
• # affected communities
Damage data
• # destroyed houses
• # damaged houses (cat 1,2,3)
Other thematic data
• # communities without water
• # cases of flu per week
Format
Text
Number
Date
GPS Coordinates
Picture
Type
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14. Session 2. WHY you need to
measure it?
We do not have
these data! We
need it NOW!
Bring me
beneficiary
lists NOW!
I want to have an overall
picture! What is going
on outside this office???
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• Quantify needs;
• Register beneficiaries;
• Monitor and evaluate
program
• Etc…
15. Session 2. HOW you will measure
it?
Check for
available data
• Government data;
• Assessments;
• Available researches;
• Info from partners…
Going to the field to
collect data directly from
people
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Primarily data collection
16. Session 2. LEVELs of data collection
Individual
Household
Community (village, city, district,
region)
Institution (school, Collective
Center)
etc…
Note: More detailed data collected
requires more time, people and
resources!
Levels of data collection are
interlinked:
Individual Household
Community
SAMPLING
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17. Session 2. Sampling and confidence
level - 1
Having enough representative number of assessed
units, it is possible to make judgments about the entire
group.
Sampling – scientifically defined number of enough
interviews to be able to assess entire group.
Confidence level – degree to which indicators on the
bigger group are statistically relevant.
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18. Session 2. Sampling and
confidence level - 2
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To calculate sample size, confidence level and
confidence interval, see:
http://www.surveysystem.com/sscalc.htm
Let’s try calculate sample size for population
group 30,000 individuals and with confidence
interval 5%, confidence level is 95%...
– 379…
19. Design you questions;
Decide on types of answers;
Explain your data.
Session 2. Design a data model
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20. Session 2. Designing your
questions
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Open questions: Closed questions:
What is your
most urgent
need ?
Father is ill and
cannot work
W
W
h
h
a
a
ttiissy
y
o
o
u
u
r
r
m
m
o
o
s
s
t
t
u
u
r
r
g
g
e
e
n
n
t
t
n
n
e
e
e
e
d
d
?
?
(
((
P
P
P
iiic
c
c
k
k
kt
o
o
h
n
n
re
e)) )
F
F
o
o
o
o
d
d
X
X S
S
S
h
h
h
e
e
e
l
l
l
t
t
t
e
e
e
r
r
r
W
W
W
a
a
a
t
t
t
e
e
e
r
r
r
X L
L
i
iv
v
e
e
l
l
i
i
h
h
oo
o
o
d
d
s
s
H
H
e
e
a
a
l
l
t
t
h
h
X E
E
E
d
d
d
u
u
u
c
c
c
a
a
a
t
t
t
i
i
i
o
o
o
n
n
n
X Other (specify)
Requires human reading in
order to analyse
! What about answers you had
not thought of?
!
21. Session 2. 1,2,3-choice questions
Single choice questions;
Multiple choice questions…
…
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WHAT IS THE DIFFERENCE and WHEN we use
them?
22. Session 2. SMART Indicators
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Specific – target a specific area; should be clear what is to be
measured/improved
Measurable – quantifiable or clear qualitative measurement;
something that can be expressed in numbers or in terms of a
meaningful scale of values
Achievable – has to be possible to measure from an operational
standpoint
Relevant – relevant and useful in measuring the
need/activity/objective it’s linked to
Timebound – must be measurable in a specific period of time
(more relevant to monitoring)
23. A data model describes how you store your data
Regions
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Region Number of houses Number of people GCA/NGCA
TEXT NUMBER NUMBER GCA/NGCA
Agency Name Email address Phone number Active
TEXT TEXT TEXT YES/NO
Agencies
Session 2. Design a data model
Too much data in one field makes it difficult to analyze data
!
24. Session 2. How to build the
model in Excel
First row headers
One data type per column
One piece of data per cell
A sheet with the definition
of your data on
Guidance notes
Example
Example
Example
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25. Afghanistan Shelter Cluster
ShelterCluster.org
Session 2. Explain your data
25
Give explanation of each data
Partially damaged house
A house is partially
damaged if it is still
repairable.
Example
Let’s have a look at a
simple example…
Coordinating Humanitarian Shelter
26. Session 2. Data model example
RAW DATA
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QUESTIONNAIRE
27. Session 2. P Coding - 1
Each region, province, district and village have its own
pre-defined unique number: the P-code
This is useful, because many units have the same names
Sometimes if you have your location question “open”, it is
impossible to find the right village…
Example
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28. Session 2. Common Operational Datasets
The Common Operational Datasets (CODs) are
critical datasets that are used to support the work
of humanitarian actors across multiple sectors.
They are considered a de facto standard for the
humanitarian community and should represent
the best-available datasets for each theme.
They may include:
Administrative boundaries;
Populated places (settlements);
Transportation network (roads, airports,
checkpoints);
Hydrology (rivers etc)
Population statistics (IDPs, resident
population etc);
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29. Session 2. Where to get CODs?
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Humanitarian Data Exchange:
https://data.humdata.org/dataset/afg-admin-
boundaries
OCHA Afghanistan
30. Session 2. Structure and priorities
There are always several key questions and many
additional.
Use cascading option to prioritize and set a
structure:
Do you have
your house
damaged?
No Yes
What is the level
of damage?
Light
Medium
Heavy
Destroyed
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31. Session 2. EXERCISE 2
In groups prepare data model
with 10 questions on a specific
thematic (15 min):
• Village assessment;
• Monitoring and evaluation;
• Beneficiary registration;
• Damage assessment.
Take into account the following:
• Level of data collection;
• Types of questions;
• Are they SMART?
• Structure.
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33. Information Management is a cycle of six stages
Design Combining
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34. Session 3. Kobo main page
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Page address is:
https://kobo.unhcr.org
• Account for humanitarian agencies is
free of charge;
• As many surveys as possible;
• 5 min to register
35. Session 3. KOBO MAIN MENU
Projects– all forms that
are deployed and work online
New – To create new and
upload forms that user is
working on.
Deployed
Draft
Archive
Library – library of
questions, if created.
New-To access questions,
upload and collection
My Library
Public collections
Settings
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36. MENU NEW
Session 3. New
Click on Projects, then on “new”, and then two
choice “ project” and “upload”
NEW is accessible by clicking
here
To create new draft form.
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37. NEW PROJECT
Write here the
name of the
form
Write here the short
description of the form
Select here your sector Select here country
Finally click here to
start creating the
form
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38. Form can be
deployed, but
disabled for
submissions
Link to the form can
enable users without
login and password
access and view project.
Link to the form can
enable users without
login and password
access and view all the
data.
Projects within Kobo can
be shared with other
Kobo users. Three level
of rights: to edit, to view
and to submit data.
Additional
documents may be
uploaded, in case
form uses media
embedded in
questions and/or
notes media
(pictures, sounds)
DO NOT DELETE
WITHOUT
THINKING TWICE!!!
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PROJECTS Deployed SETTINGS
39. MENU PROJECTS NEW Deployed FORM
Download data in XLS
and XML. Share and
Clone the project
Preview submitteddata
in the browser. Allows
to see how it look like
the online form
Replace with XLS
Allows to edit
submittedonline form
It allows to copy and
share the link of the
form
It shows how it look like
the online form. It is
similar with preview.
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40. It shows the summary
of submissions of form
in table
Export data submitted
to date in XLS, CSV, ZIP,
KML or Excel Analyzer
Gallery of pictures
It shows the summary
report of submission of
form
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MENU PROJECTS Deployed
SUMMARY
41. Language selection (for
Multilanguage forms only)
Print entire form for offline use
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ONLINE FORM
42. Session 3. Steps to create and edit
question
Steps to follow
Step 1: write the question
Step 2: Click Add question
Step 3: Define type of
variable
Step 4: Go to settings and
set options of question
Step 5: Set hints if
necessary
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43. Session 3. Creating and editing
forms
Manual
Coding in Excel,
transforming in XML;
Needs advanced skills and
knowledge;
Allows to use more
functionality (ex.: cascading is not yet
available in visual editor);
Allows design form better.
Visual editor
Easy for beginners;
Reduced functionality;
Quick to start;
Allows immediate preview.
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44. Session 3. Adding questions to the
form
One choice question
Additional explanationand text.
May be introduction message.
Multiple choice
questions
Open questions
Only numbers are
accepted as an answer
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45. Session 3. Labels and values - 1
• Label: what is seen in user mode: question text and options as answers etc.
• Value: the way data recorded in the online database. The same format
would be seen when export data for analysis.
!!! Proper values at the design stage are critical to simplify analysis process
later. !!!
Question text
Answers’
labels: what
user will see
when using
form
Answers’
values: what
YOU will see
when
exporting
submissions
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46. Session 3. Labels and values - 2
Labels and values are critical for analysis:
– All data is exported in the format of values, not
labels. Column header: is made out of
value, not label (available only in
manual coding)
All answers are values, not labels. Easiness of their
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understandingis upon form designer!
47. Session 3. Additional features of
Kobo
Hints;
Restrictions and restrictions messages;
Validation of data;
Adding picture;
Adding geodata;
Introduction to designing form manually;
Multilanguage support
Using mobile devices for data collection…
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48. Session 3. Before going for data
collection
Have a plan! Who goes where and when, how
often, logistics planned etc.
Have idea what you will do with data after it is
collected!
Brief and train your team! Communicate
priorities!
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49. Session 4. Combining and
processing data
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50. Information Management is a cycle of six stages
Design Combining
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51. Session 4. Combining data
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Always all the data is pulled together into one
document for further processing and analysis.
Depending on the way data was collected
combining data may happen in different ways
and steps…
52. Session 4. Paper based form
Digitalizing data – entering
data from paper form into
Excel or any online form;
Merging all data from
different localities into master
one;
Cleaning data, validating data
and beginning of analysis.
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53. Session 4. Excel based form
Digitalizing data – entering
data from paper form into
Excel or any online form;
Merging all data from
different localities into master
one;
Cleaning data, validating data
and beginning of analysis.
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54. Session 4. Kobo form
Digitalizing data – entering
data from paper form into
Excel or any online form;
Merging all data from
different localities into master
one;
Cleaning data, validating data
and beginning of analysis.
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55. Session 4. Well-designed Kobo
form
Digitalizing data – entering
data from paper form into
Excel or any online form;
Merging all data from
different localities into master
one;
Cleaning data, validating data
and beginning of analysis.
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56. Session 4. Extracting data from
Kobo
Simply click “Download data” at the project
management form and select format: XLS,
CSV, ZIP, KML…
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57. Session 4. Data cleaning
Data cleaning is the process of detecting and
correcting corrupt or inaccurate records from
a database.
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58. Session 4. Data cleaning
Be creative!
– Lookup functions
• Easy to find non-existing codes (typos)
– Formulas
• Check for mathematical and logic consistency
– Compare with other sources (Triangulation)
• Validation of values/expected ranges (do we
have approximately the same)
– Compare with previous years
• Validation of values/expected ranges (do we
have approximately the same)
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59. Session 4. Useful Excel Tools
• Data -> Validation (allows only certain values)
• Data -> Sort & Filter
• Home-> Conditional Formatting
• Pivot Tables
• Formulas
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60. Session 4. Some useful Excel
functions
• Logic
– AND
– OR
– IF (THEN)
– NOT
• Mathematical/Statistical
– AVERAGE
– COUNT
– COUNTA
– COUNTBLANK
– COUNTIF
– DSUM
– SUMIF
– RANK
• Information
– TRIM
– CLEAN
– VLOOKUP
– CONCATENATE
– LEFT
– RIGHT
– MID
– LEN
– FIND
– PROPER
– LOWER
– UPPER
– ISBLANK
– ISTEXT
– YEARFRAC
– TODAY
Use the help in Excel which
gives guidance on the use of
each formula
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62. Information Management is a cycle of six stages
Design Combining
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63. Session 5: ANALYSIS
Analysis aims to proceed clean raw
data into indicators to prove or
revoke a hypothesis.
Analysis involves a level of expertise
for the interpretation of the data.
Decide the final confidence level
(usually the same as sampling)
Keep in mind what you are looking
for or want to demonstrate.
Even better plan a list of what
you expect and don’t worry we
will always find interesting other
pieces of information along
road.
Decide to use , or not, proxy indicator.
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64. Session 5: Proxy Indicator
What is a proxy indicator? (intermediate calculation)
Be careful to not use too early and too many proxy indicators, it
will jeopardize the confidence level.
Raw Data A
Raw Data B
Indicator
Expected
Example: Direct analysis
# of Individual per Household
Example: Indirect analysis
Adequate Shelter
PROXY
Raw
A1
Raw
A2
Raw
A3
Indicator
Expected
Raw B
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65. Session 5 Tool for the ANALYSIS
Processing an indicator could be an average
(cost for rent in a city), a range (minimum/
maximum), a sum (number of IDP’s per oblast).
To get them, we mainly use excel with filters
and subtotal formula (useful for monitoring
master lists on regular manner) or create a
pivot table.
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66. Session 5 Working on master list 1
REMINDER Be sure that data is organized on sequential
and consecutive manner, one data per cell, avoiding cell
merging
Set some automatic filters
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67. Session 5 Working on master list 2
Insert on title the formula =subtotal(109,$A$1:$A$100) for
summing visible cell
Use formula =subtotal(9,$A$1:$A$100) for summing
complete column range
Please note that $ argument is used to fix a variable in excel
facilitating the copy paste of a determined fixed range.
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68. Session 5 Working on master list 3
For better presentation use group function …
… & cell formatting
#,## meaning a figures formatted
with separator like 1,000
“ ” is to included a text label
(visible but not counted as cell with
text)
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69. Afghanistan Shelter Cluster
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Session 5 Working with Pivot Table 1
REMINDER Be sure that data is organized on sequential and
consecutive manner, one data per cell, avoiding cell merging,
each column of the future pivot table shall get an unique title.
Pivot table is a two steps creation
2) Click on pivot table
1)SELECT ONLY
the part with
column header
70. Afghanistan Shelter Cluster
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Session 5 Working with Pivot Table 2
Pivot table is a way to organized data
Clicking inside
pivot table
range will
activate it
Name of
selected
column to be
‘drag & drop’
Place for main
filters ex:
working only
on certain
oblast
Row &/or Column to
organized categories
Value of what will be
represented example
counting in #of HH or
summing # of beneficiaries
71. Session 5 Working with Pivot Table 3
Note:
Small arrows allow specific
selection
Sum precise the function of
the value
By clicking on the Value, we can change the function,
sum, average or the format (figures, %, % per column)
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73. Session 5 Working with Pivot Table 5
You can link a graph to visualize your main findings
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74. Session 5 Tool for Dissemination
How to visualize and represent your indicators might
significantly emphasize one aspect or the others
21%
22%
47%
10%
0%
10%
20%
30%
40%
50%
70%
60%
80%
90%
100%
1
Dynamic in CC
empty
Did return
Did
integrate
Did remain
Did
integrate
Did return
empty
22%
21%
Intentions
Did integrate Did return
47% Occupancy
Ratio
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76. session 6- PROGRAMMING
My 5 first question on Kobo
In 30 minutes set up the 5 first question from
the exercise of the morning.
The exercise is on individual computer but you
can still keep your group in order to help each
other.
Take the first and most simple one
Your objective is to get a first run
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77. session 7 - PROGRAMMING
Another 10 please….
On the 5 first questions, please select 10 others
but covering field as date, photo, restrictions.
This step will last for 45 minutes…
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78. Presentation from participants
1 example will be shown per group
- State which difficulty you met
- Give your feed back and specific questions
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79. session – Wrap up
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Identify what did you learn today (referring to
the self evaluation form and use another color
for ticking boxes)
Fill up the feed back form
Have a good rest