Description: Massive digitization drive across public and private sectors has led organizations and their people to possess vast amounts of data, both qualitative and quantitative, often complex, unstructured, and varied. This huge data proliferation has also been accompanied with advances and rapid developments in data analytics by a corresponding surge in the creation of powerful tools. The exponential growth in both data and analytical capabilities, including artificial intelligence, is creating opportunities and challenges for managing knowledge. By integrating data analytics and knowledge management individuals and organizations can gain and share powerful insights, test strategies, improve transparency, increase value and improve the impact. If you have witnessed a knowledge gap in your organization and want to learn about developing strategies on leveraging data to bridge this gap this is the course for you. Alternatively, if you seek to build a culture in your organization that capitalizes on data-driven knowledge to develop impactful policies, programmes and projects and streamline work processes, this certification programme will guide you in doing so. Or, if you are interested in learning how data analytics can improve your own performance and, if you are curious about the latest trends and the future foresights towards data analytics and knowledge management, this course is the right place to be! Welcome to the certification course on "Data Analytics and Knowledge Management for Development".
Target Audience: Programme and project managers, knowledge management officers, monitoring and evaluation officers, technical specialists, project professionals, professionals from private enterprises, academicians and researchers involved in the identification, design, implementation and evaluation of development policies, programmes and projects.
Source: ESCAP SIAP (Data extracted on: 31 May 2024 )
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Organizer(s): ESCAP SIAP
Description: This e-learning course aims to build capacity in national statistical systems for the development and implementation of Reproducible Analytical Pipelines (RAPs) for Official Statistics.What is a Reproducible Analytical Pipeline?Simply putreproducible analytical pipelines (RAPs) are automated statistical processes (data processing and analysis) that codify to the greatest extent possible the production of official statistics. Common tools that are used to develop RAP include software such as R or Pythonand version control management tools such as Git.Reproducibility is at the heart of the approach. It implies that the outputs can be generated again with any new or revised input datasets using the RAP developed. This also implies drafting documents explaining the RAP that make it possible to build institutional knowledge and use the RAP in the future by new staff.
Description: Massive digitization drive across public and private sectors has led organizations and their people to possess vast amounts of data, both qualitative and quantitative, often complex, unstructured, and varied. This huge data proliferation has also been accompanied with advances and rapid developments in data analytics by a corresponding surge in the creation of powerful tools. The exponential growth in both data and analytical capabilities, including artificial intelligence, is creating opportunities and challenges for managing knowledge. By integrating data analytics and knowledge management individuals and organizations can gain and share powerful insights, test strategies, improve transparency, increase value and improve the impact. If you have witnessed a knowledge gap in your organization and want to learn about developing strategies on leveraging data to bridge this gap this is the course for you. Alternatively, if you seek to build a culture in your organization that capitalizes on data-driven knowledge to develop impactful policies, programmes and projects and streamline work processes, this certification programme will guide you in doing so. Or, if you are interested in learning how data analytics can improve your own performance and, if you are curious about the latest trends and the future foresights towards data analytics and knowledge management, this course is the right place to be! Welcome to the certification course on "Data Analytics and Knowledge Management for Development".
Target Audience: Programme and project managers, knowledge management officers, monitoring and evaluation officers, technical specialists, project professionals, professionals from private enterprises, academicians and researchers involved in the identification, design, implementation and evaluation of development policies, programmes and projects.
Description: The increasing availability of multiple data sources to investigate complex socio-economic, agricultural, health and environmental phenomena represents a great opportunity for survey statisticians, given the need to reduce the cost of data collection and the increasing demand for detailed information. Combining already available data sources is a key element in complementing and increasing the relevant information available in a single source. However, sometimes lack of standardisation, different forms and structures (if any) of data pose challenges to data linkage procedures and may result in an error-prone linkage. Probabilistic data linking comprises a set of statistical methods for combining multiple sources, recognising the same units even in the absence of common identifiers. In addition, probabilistic linkage produces some measure of the potential errors generated by the statistical integration procedure itself. The propagation of uncertainty from data linkage to downstream analyses based on linked data is a topic of growing interest to survey statisticians, with some interesting proposals for adjusting standard methodology (and more).
Source: Eurostat (Data extracted on: 07 Mar 2024 )
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Organizer(s): Eurostat Icon-Institut
Description: To provide the participants with a basic understanding of the main concepts of seasonal and calendar adjustment, trend cycle, irregular components and related time-series issues To introduce the participants to the use of software JDEMETRA+ via the graphical interface and R.
Target Audience: Official statisticians (including managers) interested in seasonal adjustment methods and have no specific knowledge on this subject.
Description: Starting with a question, a knowledge gap, and looking for an answer through data. This certification programme aims to provide development practitioners with the knowledge, methods, tools and software solutions to gather, store, process, analyse, visualise, and utilise qualitative and quantitative data to meet the information needs of an organisation and its stakeholders. With the technological advances of recent years, the amount of data and its potential have multiplied, but together with the risks of data manipulation and misinformation. This situation calls for stronger evidence-based management in the public sector and in development cooperation. Through this certification programme, participants develop their data analysis skills to generate and manage knowledge that ultimately inform decision-making. The course relates to the phases of the policy and programme cycles and prepares for the identification of need of knowledge and data in order to - inform strategic intervention planning - the development of evidence-based development models and theories of change - allow adaptive and agile management fed by an up-to-date and on demand data and evidence availability - strengthen evaluation robustness on the basis of solid data collection, analysis and knowledge provision - underpin organisational development based on ready availability of data and analysis - enhance external communication fed by credible evidence
Target Audience: This certification program on data analysis and knowledge management for development is intended for a wide range of actors active in the field of development cooperation, in the public sector or the third sector who want to improve evidence-based management processes: technical specialists, programme and project managers, monitoring and evaluation officers, evaluators, programme managers. Members of the research community involved in the identification, design, implementation and evaluation of development policies, programmes and projects are also invited to participate.
Organizer(s): UNSD Global Partnership for Sustainable Development Data INE Uruguay UNCEBD UNMGCY AWS UN Big Data Regional Hub in Brazil UN Big Data Regional Hub in China UN Big Data Regional Hub in Rwanda UN Big Data Regional Hub in the United Arab Emirates ECLAC Statistics Indonesia Statistics Canada ONS United Kingdom Oblivious OpenMined World Food Forum
Description: Leverage technology and your expertise to develop innovative solutions for a more sustainable and equitable future. Join us on this journey of innovation, exploration, and problem-solving as we harness the power of data to create a more sustainable and resilient world. Datathon participants will develop innovative data-driven applications, tools or statistical models combining geospatial data with other data sources to help advance the implementation of the Sustainable Development Goals. Register today!
Description: Starting with a question, a knowledge gap, and looking for an answer through data. This certification programme aims to provide development practitioners with the knowledge, methods, tools and software solutions to gather, store, process, analyse, visualise, and utilise qualitative and quantitative data to meet the information needs of an organisation and its stakeholders. With the technological advances of recent years, the amount of data and its potential have multiplied, but together with the risks of data manipulation and misinformation. This situation calls for stronger evidence-based management in the public sector and in development cooperation. Through this certification programme, participants develop their data analysis skills to generate and manage knowledge that ultimately inform decision-making. The course relates to the phases of the policy and programme cycles and prepares for the identification of need of knowledge and data in order to - inform strategic intervention planning - the development of evidence-based development models and theories of change - allow adaptive and agile management fed by an up-to-date and on demand data and evidence availability - strengthen evaluation robustness on the basis of solid data collection, analysis and knowledge provision - underpin organisational development based on ready availability of data and analysis - enhance external communication fed by credible evidence
Target Audience: This certification program on data analysis and knowledge management for development is intended for a wide range of actors active in the field of development cooperation, in the public sector or the third sector who want to improve evidence-based management processes: technical specialists, programme and project managers, monitoring and evaluation officers, evaluators, programme managers. Members of the research community involved in the identification, design, implementation and evaluation of development policies, programmes and projects are also invited to participate.
Description: Concept note Data integration approaches are becoming increasingly popular among official statisticians worldwide. This is due to the growing and evolving data requirements for monitoring national development plans and the Sustainable Development Goals (SDGs) in the face of budget constraints and declining response rates on traditional data collection methods, i.e., censuses and surveys. Furthermore, technological advancements have made new data sources available and enabled official statisticians to produce statistics using more complex approaches. However, implementing data integration approaches requires a wide range of technical and institutional capabilities. Depending on its situation, each country may encounter different challenges needing tailored solutions. In 2022-3, the National Statistics Office of Nepal participated in a project on strengthening the capacity to implement data integration approaches for official statistics. The project involved a practical exercise of integrating multiple data sources and included capacity development activities. This Stats Café provided a brief overview of the project, its challenges and solutions, as well as shared the findings and lessons learned that may benefit other countries looking to develop similar data integration projects. During the session, the project report was also officially launched. Launch of the report: "Strengthening the national statistical capacity to implement data integration approaches: pilot project in Nepal 2022-23" , Stats Café Home: Upcoming events Concluded events
Description: Nonresponse is one of the factors that affect the quality of survey data. The course will focus on modelling optimum response in longitudinal survey.
Target Audience: Postgraduate students, data producers and survey methodology experts
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: Nowcasting refers to the practice of using recently published data to update key economic indicators that are published with a significant lag, such as real GDP. The aim of this course is to familiarize participants with cutting-edge nowcasting tools that facilitate the use mixed-frequency data in regression models. The course begins by establishing the importance of nowcasting for more timely and appropriate policy formulation during crisis periods such as the GFC or COVID-19. It then reviews the standard nowcasting regression-based procedures available, including the BRIDGE, MIDAS, and U-MIDAS estimators, both with and without dynamic factors. The course also reviews the more general state-space/Kalman filter approach to formulating and estimating a nowcasting model with mixed-frequency data. Procedures for combining nowcasts from the distinct models are considered, along with statistical procedures for evaluating the accuracy of a sequence of nowcasts. Each topic is complemented by a hands-on workshops and assignment using country-specific data using the EViews econometric package. The workshops and assignments are an integral component of the course designed to illuminate the actual steps required to generate a nowcast.
Description: It is standard practice in official statistics to seasonally adjust monthly and quarterly time series using the methods recommended in the ESS guidelines on seasonal adjustment. The COVID-19 pandemic accelerated demand for official statistics time series data to be published at a higher frequency than the more usual monthly or quarterly periodicity of indicators. There are well developed software for the seasonal adjustment of monthly and quarterly data, such as JDemetra+ and X-13ARIMA-SEATS, but neither of these are currently designed to handle daily or weekly series. The aim of this short course is cover both the standard approach to monthly and quarterly seasonal adjustment but to also introduce participants to some of the experimental methods for seasonally adjusting higher frequency time series . We present examples of published experimental weekly and daily time series and discuss some of the challenges in seasonally adjusting these series. Participants will also be introduced to the concept of seasonal adjustment in official statistics and cover the challenges of seasonally adjusting not just monthly and quarterly data, but also a focus on recent techniques and methods for weekly and daily data, and examples of how this has been achieved in practice. Learning outcomes to be covered: What is seasonal adjustment and why do we do it? Seasonal adjustment standard practice in official statistics for monthly and quarterly time series Demonstration of applying methods and software techniques for higher frequency time series (weekly and daily data examples) Examples of experimental high frequency seasonally adjusted outputs at the Office for National Statistics
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: You have created a survey and collected data, but you do not know how to analyze that data and come to the appropriate conclusions? The course “Introduction to survey analysis in SPSS” was created to show you how to use statistical software SPSS to properly analyze the data obtained through the survey and draw certain conclusions. In the course, through theoretical and practical examples, you will learn how to enter and prepare data for processing, descriptive statistics and other statistical techniques. Learning outcomes to be covered Participants will learn how to input data in SPSS, edit variables (recoding and calculating new variable), save and export output, summary statistics and plots, test for normality, comparing means (t-test, Mann-Whitney U) and chi-square test.
Target Audience: Master and PhD students, and researchers who are familiar with statistical analysis and have a theoretical background in statistics but did not use SPSS software for data analysis.
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: Even though there is substantial literature on studies that pool survey data, it is still not clear which are the most efficient methodologies and sampling designs for combining data from different surveys. For example, it is important to know whether the estimates from the different surveys involved should be given equal weights in the calculation of the combined statistics or not. If they are not given equal importance, then it should be clear how they should be weighted and why. In this paper, current and proposed methods considered to combine survey data are evaluated through simulation, in the context of simple random sampling, stratified random sampling and two stage cluster random sampling from finite populations generated from a normal distribution super-population model. Simulation results suggest superpopulation variance does not influence the choice of weighting method. However, the population size appears to influence this choice. Combining samples improved the precision of estimates regardless of weighting method used for data collected under all considered sampling techniques, with stratified sampling being more precise than simple random sampling and two stage random cluster sampling.
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: This seminar aims to discuss recent developments in robust estimation in linear time series models, with short and long memory correlation structures, in the presence of additive outliers. Robust estimators of the auto covariance matrix will be discussed from both time and domain approaches from both theoretical and applied points of view. A variety of application models will be considered for the use of the proposed methodologies, such as multivariate techniques (factor analysis and PCA), time series and mixed models. Real applications will also be discussed.
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: For several decades, national statistical agencies around the world have been using probability surveys as their preferred tool to meet information needs about a population of interest. In the last few years, there has been a wind of change and other data sources are being increasingly explored. Five key factors are behind this trend: the decline in response rates in probability surveys, the high cost of data collection, the increased burden on respondents, the desire for access to “real-time” statistics, and the proliferation of non-probability data sources. In this presentation, I review some data integration approaches that take advantage of both probability and non-probability data sources such as the dual frame weighting, calibration, statistical matching, inverse probability weighting and small area estimation. I discuss the characteristics of each approach, including their benefits and limitations, and present a few empirical results.
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Source: Eurostat (Data extracted on: 11 Aug 2021 )
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Organizer(s): Eurostat
Description: To provide the participants with a basic understanding of the main concepts of seasonal and calendar adjustment, trend cycle, irregular components and related time-series issues To introduce the participants to the use of software JDEMETRA+ via the graphical interface and R.
Target Audience: Staff of national statistical institutes (including newcomers) involved in the production process who want to acquire a good understanding of Seasonal Adjustment (SA) methods and practices. Please note: All ESTP Courses are exclusively available to staff members of a European Statistical System (ESS) institution.
Description: During this Global Network Webinar we had with us Fernando Cantú (UNIDO) and Daniel Hopp (UNCTAD) who talked about Using Nowcasting in Official Statistics. In this joint presentation, UNCTAD and UNIDO presented some of their recent work in the field of nowcasting. UNCTAD discussed two forthcoming papers, one on a comprehensive survey of the feasibility of nowcasting SDG indicators and one on the performance of LSTM neural networks in nowcasting during the COVID crisis. Additionally, plans for revamping how UNCTAD presents its nowcasts, moving from static PDF bulletins to an interactive website, were discussed. UNIDO presented the methodologies that are currently applied for nowcasting global industrial production, as well as plans for developing a system of interlinked country-level nowcasts. Special emphasis was made to model selection in nowcasting models.
Title in Arabic: تقنيات النمذجة والتقديرات باستعمال برنامج EVIEWS
Organizer(s): AITRS
Description: ينظم المعهد العربي للتدريب والبحوث الاحصائية دورة تدريبية حول تقنيات النمذجة والتقديرات باستعمال برنامج EVIEWS خلال الفترة من 14 إلى 18 نوفمبر 2021 لفائدة الجهاز المركزي للإحصاء بالجمهورية اليمنية وذلك بهدف دعم قدرات العاملين بالجهاز في الاساليب والتقنيات الضرورية للقيام بكل الانشطة الاحصائية المتعلقة بالتنبؤات وبالتقديرات الاقتصادية لأعداد الحسابات القومية بالأساس. وسيتم التعرض الى المواضيع التالية: - تنظيم ومعالجة المعطيات الإحصائية * بيانات لتقدير السلاسل الزمنية * بيانات لتقدير مجمع البيانات * تجميع البيانات للتقديرات - تقديرات المعادلات الخطية * الانحدارات المتعددة (Résidual Test, White test, Reset Test of Ramsey, Stability test of Chow, Forecast) - تقدير معادلات البيانات المجمعة POOL DATA (بنود مؤشر الإنتاج الصناعي) * إنشاء مشروع البيانات المجمعة POOL DATA * تنزيل البيانات المجمعة * تمثيل البيانات * اختبار جذر الوحدة لمجمع البيانات * التأثير الفردي العشوائي والتأثير الفردي الثابت * تقدير البيانلت المجمعة * اختبار Hausman: التأثير الفردي العشوائي * منظومة المعادلات: تأثير فردي ثابت * مؤشر الإنتاج الصناعي IPI: التنبؤ - السلاسل الزمنية. النمذجة أحادية المتغير (CPI) والتنبؤ * مؤشر الأسعار عند الاستهلاك * اختبار جذر الوحدة * تعريف النموذج: الارتباط الذاتي بين الأخطاء العشوائية والارتباط الذاتي الجزئي * تقدير النموذجARMA (1,1) * Box-Jenkins توقعات * الطريقة غير المعلمية: التنبؤ * نتائج طرق التنبؤ المختلفة - السلاسل الزمنية: دراسة نماذج المتغيرات المتعددة * اختبار العلاقة السببية بين المتغيرات (CPI,M3,NEER) * نموذج VAR (Vector Autoregression Estimate) * Block Exogeneity Tests - استهداف نماذج ECM للتنبؤ على المدى الطويل والقصير * نمذجة 3 معادلات باستعمال نموذج اصلاح الأخطاء العشوائية * اختبار Johanson * تقديرات نموذج اصلاح الأخطاء العشوائية Model EC * تقدير نظام المعادلات المتزامنة * اختيار الفرضيات
Source: Eurostat (Data extracted on: 03 May 2021 )
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Organizer(s): Eurostat Icon-Institut
Description: Introducing to the theory and practice of temporal disaggregation, balancing and statistical reconciliation of systems of time series.
Target Audience: Statistical officers in charge to regular production of National Accounts quarterly series. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Source: Eurostat (Data extracted on: 03 May 2021 )
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Organizer(s): Eurostat
Description: The objective of this course is to introduce participants to the practice of output checking for confidentiality risks. The course focuses on output that is generated by researchers from official microdata. In most cases researchers will have had access to microdata through the Research Data Centre of the data producer to produce the output. The participants will be invited to bring their own case studies for discussion in the training course.
Target Audience: Staff members dealing with statistical confidentiality, especially staff in Research Data Centres or Safe Centres. The course is intended for staff checking output that was created by external researchers or output from varying statistical analyses created by colleagues in statistical offices. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Description: During this Global Network Webinar we had with us Giorgia Lupi from the design studio Pentagram, New York who gave her illustrated talk “Speak Data!”. Today data is everywhere. But what does data really mean, and how can we extract real value from it in our daily lives? In this illustrated talk, information designer and Pentagram partner Giorgia Lupi discussed our new data reality and “data humanism”, her unique philosophy for understanding and working with data. Surveying her diverse work over the last decade, Giorgia introduced her distinctive approach to data visualization by looking at the human side of data and offered a look into the far-reaching applications of her work in data and design, from corporate to institutional to personal.
Source: Eurostat (Data extracted on: 03 May 2021 )
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Organizer(s): Eurostat Icon-Institut
Description: Objectives: * To provide participants with a specific knowledge of the features recently included within JDEMETRA+ and run the tool within the R environment; * To train the participants to use JDEMETRA+ for purposes different from seasonal adjustment, such as estimation of missing values temporal disaggregation, benchmarking, forecasting and analysis of revisions; * To prepare and to motivate the participants to become integral part of the extended network in charge of testing (software releases), maintaining (fixing bugs) and extending (new plug-ins) the tool.
Target Audience: Advanced users of seasonal adjustment methods involved in regular/massive data production and/or developers involved in the integration of SA methods in their IT environment wishing to enhance their knowledge of the JDEMETRA+ tool and/or using or potentially developing relative plug-ins. Ideal participants are either young statisticians with some interest in IT or young IT specialist with some interest in statistics. People currently using TRAMO/SEATS and/or X12 family product and/or old version of DEMETRA/JDEMETRA+ family products aiming at implementing the latest JDEMETRA+ version. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Description: During this Global Network Webinar, Fionntan O’ Donnell from Open Data Institute and Andrew Dudfield from Full Fact, presented how changes in the publishing of national statistics can support the work of fact checkers around the world. Data publishing by national statistical offices affects the fact checking process in many ways. From what fact checkers choose to verify, to how they find data to the human support they get. Better statistical data means easier and faster fact checking, which leads to more fact checks being published and misinformation being addressed faster.
Source: Eurostat (Data extracted on: 03 May 2021 )
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Organizer(s): Eurostat Icon-Institut
Description: Objectives: * To provide the participants with a state of the art knowledge of the methodology and practice of nowcasting; * To introduce the participants to the use of R to produce nowcasts for their own series.
Target Audience: Staff of national statistical institutes involved in the production process who want to acquire a good understanding of nowcasting methods and practices. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Description: This short course offers instructor-led and hands-on training in basketball analytics for students, young statisticians, and sports professionals. It provides the understanding of the concepts of basketball data science, by covering basic statistics tools and advanced methods of data analysis, as discussed in the book Basketball Data Science – with Applications in R by P. Zuccolotto and M. Manisera (2020) and using the R package BasketballAnalyzeR. Real examples from NBA data are shown and small exercises are assigned to students.
Target Audience: Young statisticians, young researchers in sport statistics, students with basic knowledge of statistics and R language, sports professionals with basic knowledge of statistics and R language. Prerequisites: Basic knowledge of statistics and R language.
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: Join us to launch the Asia-Pacific Guidelines to Data Integration for Official Statistics and to celebrate the 1st anniversary of the establishment of the Data Integration Community of Practice We will showcase Asia Pacific achievements in Data Integration and hear from the Statistical Center of Iran on implementing data integration using big and administrative data sources. Relevant publication: United Nations, Economic Commission for Europe, “A guide to data integration for official statistics”, version 2. Agenda Welcoming remarks, Ms. Gemma Van Halderen, UNESCAP Overview of achievements, Ms. Afsaneh Yazdani, UNESCAP DI-CoP: our virtual meeting place, Ms. Jessica Gardner, former UNESCAP consultant Launch of Asia Pacific Guidelines to Data Integration for Official Statistics Touch upon the Asia Pacific Guidelines to Data Integration for Official Statistics, Ms. Jenine Borowik, UNESCAP Innovations from Iran: Resolving quality issues in the integration of administrative and big data in official statistics, Mr. Saeed Fayyaz, Statistician, Statistical Center of Iran , Stats Café Home: Upcoming events Concluded events
Description: A session for the Data Integration Community of Practice to present the potential use of administrative data to enhance the compilation of labor and employment statistics.
Description: About the session With the COVID-19 pandemic significantly changing our world, official statisticians are now urged to apply new approaches to reduce face to face data collections One approach has been switching to mixed mode surveys, where a combination of modes such as face-to-face, telephone, web based questionnaire, etc is used to collect data Although this approach seems promising, there are technical issues that should be considered and technical capacity has not always caught up with demands. A recent Stats Café on "Is COVID-19 introducing mode effects into your official statistics?" co-hosted by ESCAP and the UN Statistical Commission’s Inter Secretariat Working Group on Household Surveys focused on mixed mode household surveys The Café shared practices around the more ‘front end’ parts of surveys such as questionnaires and field operations but many participants requested advice on the ‘back end’ parts. This Stats Café was organised as a rapid response to participant questions for advice on imputation, weighting and estimation in mixed mode data collections. , Stats Café Home: Upcoming events Concluded events
Description: The Samoa Tourism Authority (STA) are in the process of finalizing their next Samoa Tourism Sector Plan for the period 2020/21-2024/25, providing a timely opportunity for UNESCAP to support the process with an application of the EPiC tool in order to review the current status of the document, to determine what recommendations the tool can provide in the areas of coverage and monitoring for consideration by the STA. As such STA, in collaboration with SBS and EPPD, have indicated a willingness to work with UNESCAP to implement the EPIC online App for this newly developed Samoa Tourism Sector Plan, 2020/21-2024/25. The focus of the workshop exercise was threefold; i) reviewing the coverage of “Issues for Action” presented in the document, ii) reviewing the final “Statistical Indicators” proposed for monitoring progress against these issues, and iii) reviewing the alignment of steps to achieve these results. Agenda Concept Note , EPiC tool: Overview Resources Workshops
Source: Eurostat (Data extracted on: 21 Dec 2020 )
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Organizer(s): Eurostat Icon-Institut
Description: The main objective of the courses is to enhance the theoretical and practical knowledge related to the treatment of unit non-response and item non-response. In particular, participants will gain knowledge on weighting techniques in order to deal with unit non-response and imputation techniques in order to deal with item non-response. For unit-nonresponse, participants will also learn about up to date monitoring of data collection and application of adaptive survey designs.
Target Audience: All NSIs staff dealing with data collection facing non-response, either unit non-response where entire units intended to be collected are missing or item non-response where some items of otherwise responding units are missing. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Source: Eurostat (Data extracted on: 21 Dec 2020 )
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Organizer(s): Eurostat
Description: To provide the participants with a basic understanding of the main concepts of seasonal and calendar adjustment, trend cycle, irregular components and related time-series issues To introduce the participants to the use of software JDEMETRA+
Target Audience: Staff of national statistical institutes (including newcomers) involved in the production process who want to acquire a good understanding of Seasonal Adjustment (SA) methods and practices. ESTP Trainings are open to non-ESS members if capacity allows after ESS needs are fulfilled.
Description: Economic and Social Commission for Asia and the Pacific (ESCAP) jointly with partners organized a series of regional Workshops on Implementing Data Integration in Asia and the Pacific. Two rounds of identical workshops with the same content were organised at the following dates and hours, duplicated to accommodate participants from different time zones. ROUND 2 (preferably for countries from eastern part of the region) 1 December (08:00-11:00 BKK time) Data integration from a management perspectiveTarget group: managers, with experts to participate as observers 2-4 December (08:00-11:00 BKK time) Technical issues and solutions for data integrationTarget group: experts 4 December (13:00-14:00 BKK time) Concluding sessionTarget group: both managers and experts Link to ROUND 1 (24-27 November 2020) These workshops aimed to strengthen awareness and capacity on data integration, with a focus on improving poverty-related statistics. They target official statisticians, both at the management and expert level, from National Statistical Offices (NSOs) as well as other parts of the National Statistical Systems (NSS).
Description: Economic and Social Commission for Asia and the Pacific (ESCAP) jointly with partners organized a series of regional Workshops on Implementing Data Integration in Asia and the Pacific. Two rounds of identical workshops with the same content were organised at the following dates and hours, duplicated to accommodate participants from different time zones. ROUND 1 (preferably for countries from western part of the region) 24 November (12:00-15:00 BKK time) Data integration from a management perspectiveTarget group: managers, with experts to participate as observers 25-27 November (12:00-15:00 BKK time) Technical issues and solutions for data integrationTarget group: experts 27 November (17:00-18:00 BKK time) Concluding sessionTarget group: both managers and experts Link to ROUND 2 (1-4 December 2020) These workshops aimed to strengthen awareness and capacity on data integration, with a focus on improving poverty-related statistics. They target official statisticians, both at the management and expert level, from National Statistical Offices (NSOs) as well as other parts of the National Statistical Systems (NSS).
Description: There needs to be more discussion on the use of data-driven innovation in policy-making, e.g. exploring the role of public-private partnerships and how private-sector data can be leveraged, voluntarily, to provide evidence for informed policy-making. This session aims to uncover how data sharing can provide relevant tools for prevention and management of such global crises. Through break-group discussions the workshop will provide an opportunity for stakeholders to meet one another, share experiences and identify opportunities for multistakeholder collaboration. The workshop…
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Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
Description: To respond to evolving demands for timely statistics, national statistical agencies are now stepping out of their comfort zone, aiming to discover new modalities, and exploring all potential data sources. Data integration pulls together data from different traditional sources such as censuses, surveys, and administrative data, as well as new sources of data It can provide more frequent, timely and disaggregated statistics, at lower cost compared to traditional approaches While this is promising, there are challenges on the way, including access to data, quality, interoperability, privacy and confidentiality, and limited technical capacity. This Stats Café brought together expert panelists with relevant experience, providing perspectives on different dimensions of data integration. Agenda Welcome: Ms. Gemma Van Halderen, director of UNESCAP Statistics Division UNECE’s work on Data Integration: A Journey to “Where?”, Mr. Steve Vale Data integration a new statistical frontier for official statistics, Dr. Siu Ming Tam Integrated Sector Accounts of Turkey, Ms. Aycan S. Özek Q/A and discussions Moderated by Ms. Afsaneh Yazdani Chair Ms. Gemma Van Halderen, Director, Statistics Division UNESCAP. Panelists Mr. Steve Vale, Regional Adviser in Statistics UNECE Dr. Siu-Ming Tam, Honorary Professorial Fellow University of Wollongong Ms. Aycan Sultan Özek, Head expert Financial Reporting Department, CBRT Q/A moderator Ms. Afsaneh Yazdani, Statistician, Statistics Division UNESCAP Relevant publications: Integrated Statistics: A journey worthwhile A Guide to Data Integration for Official Statistics Guidance on Data Integration for Measuring Migration Using Administrative and Secondary Sources for Official Statistics --------------------- >> See others Asia-Pacific Stats Café series
Title in Arabic: معالجة القيم المفقودة في المسوح والتعدادات
Organizer(s): AITRS
Description: لا يكاد يخلو أي مسح إحصائي أو تعداد من قيم مفقودة لعدة أسباب تتراوح بين الرفض وعدم التواجد وأخطاء في استيفاء البيانات أو في إدخالها إلى الحاسوب. وقد تحدث هذه الظاهرة بشكل عشوائي تام، لكن أحياناً يكون فقدان البيانات مرتبطا ببيانات أخرى تم استيفاؤها وأحياناً يكون فقدان البيانات مرتبطاً بقيم المتغير نفسه. إذا كانت نسبة البيانات المفقودة مرتفعة فإن عدم التعويض عنها يؤدي إلى انخفاض في دقة التقديرات الإحصائية من جهة وقد يؤدي إلى وجود تحيز في التقديرات من جهة أخرى خاصة إذا كان فقدان البيانات غير عشوائي.
