Completed e-Learning Courses

According to the World Health Organization , in 2021 more than 700 million people in Asia and the Pacific lived with some form of disability, which accounted for 16 per cent of the population. Persons with disabilities encounter social and economic barriers and bias in all aspects of life. Disability statistics enable tracking socioeconomic indicators related to persons with disabilities, therefore understanding better their needs to participate in society on an equal basis and identifying policy gaps to ensure disability-inclusive development.

Within the framework of the 2030 Agenda for Sustainable Development, disability inclusion in the development process has been increasingly crucial, necessitating the need to improve disability disaggregated data to monitor disability-related Sustainable Development Goal (SDG) indicators. At a regional level, the Incheon Strategy to ‘Make the Right Real’ for Persons with Disabilities in Asia and the Pacific, the regional development goal to promote disability inclusion, reinforces the importance of disability-disaggregated data to measure progress against its targets and indicators. Disability data as a critical instrument to advance disability-inclusive development was reaffirmed by the adoption of the Jakarta Declaration on the Asian and Pacific Decade of Persons with Disabilities, 2023–2032. Underscoring the continued implementation of the Incheon Strategy, the Jakarta Declaration urges regional stakeholders to take measures to close disability data gaps and strengthen statistical capacities.

Climate change is one of the most pressing issues of our time. Its impacts on well-being are far reaching, including impacts on health and the economy. Humans have contributed to climate change largely through economic activities which are intrinsically linked to climate change. Our supply and use of energy for example has led to increased emissions of greenhouse gases which have resulted in global warming. A better understanding of the relationship between the economy and climate change through the compilation of relevant indicators is key to mitigating and adapting to climate change.

This course will focus on climate change indicators that can be compiled from environmental economic accounts. After a brief overview of climate change and, relevant polices and multilateral agreements, participants will learn how to compile various indicators that inform climate change. The focus of the course is on better understanding the relationship between climate change and economic activity. And the statistical framework that provides the concepts, definitions, and methodology for measuring this relationship is the System of Environmental Economic Accounting. Participants will learn about physical supply and use tables for energy and air emissions, and indicators that can be compiled from these accounts. Other topics to be discussed include transaction accounts which can be used to derived expenditure type indicators such those on taxes on energy and pollution.  Further details on course content follow below.

The numbers of refugees and internally displaced persons (IDPs) have increased rapidly in recent years, with UNHCR estimating over 108.4 million forcibly displaced persons in 2022. Similarly, the issue of statelessness continues to persist in society, with 4.4 million people currently estimated to be stateless. This course introduces the “International Recommendations on Refugee Statistics” (IRRS), the “International Recommendations on IDP Statistics” (IRIS) and the “International Recommendations on Statelessness Statistics” (IROSS). These sets of recommendations were endorsed by the United Nations Statistical Commission in 2018 (IRRS), 2020 (IRIS) and 2023 (IROSS), respectively. The course is spread over six substantive modules and provides details on the statistical framework for compiling refugee, IDP and stateless persons statistics. It introduces concepts, definitions, classifications and methodologies relevant to collecting, compiling, analyzing and disseminating data and indicators on refugees, IDPs and stateless persons, 2 including Sustainable Development Goal (SDG) indicators. The course also includes information on data sources, institutional mechanisms, and coordination. 

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 put, reproducible 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 Python, and 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.  

Why are Reproducible Analytical Pipelines  important for Official Statistics?

All national statistical systems are engaged in the regular, high frequency production of many official statistics. For example, most countries compile monthly consumer price index (CPI). The input data for the compilation of CPI is generally the same from month to month. By developing and implementing an RAP for the compilation of CPI, countries can improve the timeliness and quality of the CPI since automation reduces the time required to clean and analyze the data; it also reduces the chance of errors that could occur when relying on non-automated processes. 

Furthermore, the Sustainable Development Goals (SDGs) require that countries use more diverse data sources in the compilation of indicators. The COVID-19 crisis has shown that automated tools can facilitate data analysis and reporting when these sources are updated. These tools, including software such as R and sharing platforms such as GitHub, allow statisticians  to streamline data cleaning, compilation, and analysis. 

Implementing these new approaches to data processing and analysis and utilizing these modern tools can aid in the timelier production of statistics and lower the time staff need to produce statistics. This approach is also more transparent, easier to review and to share among staff and thus less prone to errors in the data and/or statistical analysis. Furthermore, implementing good practices based on RAP can contribute to building institutional knowledge by ensuring that work programmes can be more seamlessly transferred among different staff and over time

This course introduces the SEEA Central Framework, the international statistical standards for measuring the interactions between the environment and the economy. It is comprised of 5 modules. The first module introduces the SEEA. Modules 2 discusses accounting for physical flows while module 3 discusses accounting for environmental assets. The fourth module contains details on environmental activity accounts, combined presentations and indicators. The last module contains an overview of subsystems, applications and extensions.

This 8-week course developed by SIAP in partnership with the Asian Development Bank (ADB) introduces machine learning as a tool for using either traditional (surveys, micro data, …) or non-traditional (big data) data sources to produce high quality predictions for Official Statistics or Sustainable Development Goals (SDGs) indicators. It provides an opportunity for participants to explore and manipulate the techniques of Machine Learning and their links with traditional statistical methods. The 6 modules (+1 module with recalls/prerequisites) aim at providing an overview of the current methods and applications of Machine Learning, through simplified theoretical concepts, pedagogical case studies and interactive resources. The course is not based, nor does it require, a particular software. However, reproducible examples on either simulated or real data are provided using the R/RStudio environment. Some Python procedures and packages are also provided.

SDMX stands for Statistical Data and Metadata eXchange. It is an ISO standard designed to describe statistical data and metadata, normalize their exchange, and improve their efficient sharing across statistical and similar organizations. SDMX provides an integrated approach to facilitating statistical data and metadata exchange, enabling interoperable implementations within and between systems concerned with the exchange, reporting and dissemination of statistical data and their related meta-information. This course has been developed by Asian Development Bank (ADB), United Nations Statistics Division (UNSD), Economic and Social Commission for Asia and the Pacific (ESCAP), and Statistical Institute for Asia and the Pacific (SIAP) with comments from the Bank of International Settlements (BIS) and the International Labour Organization (ILO).The course will be available in ADB eLearn, and is free-of-charge, self-paced, and open to anyone who is interested in learning more about SDMX.

The 2030 Agenda encourages member states to conduct regular and inclusive reviews of progress at the national and sub-national levels which are country-led and country-driven. This course focuses on building capacity in countries for reporting on the SDG indicators using national indicator frameworks. During the course participants will learn about key concepts around metadata, particularly related to the SDGs, learn how to fill the SDG metadata template and better understand the importance of metadata to help explain the data and potential differences between data. The target participants are officials in national statistical offices, line ministries and other institutions who are responsible for providing data and metadata for one or more national SDG indicators. The course can also be useful to a wider audience who is interested to learn more about metadata in the SDG context

This e-Learning course introduces the System of Environmental Economic Accounting -Ecosystem Accounting (SEEA EA), the agreed statistical framework for collecting such information on ecosystems and their relationship to human activity. The SEEA EA provides an integrated statistical framework for organizing biophysical information about ecosystems, measuring ecosystem services, tracking changes in ecosystem extent and condition, and linking this information to measures of economic and human activities. It supports the compilation of indicators for several global policy frameworks including the 2030 Agenda for Sustainable Development and the associated SDGs indicators as well as the Kunming-Montreal Global Biodiversity Framework. The course is being organized by the United Nations Statistics Division (UNSD) and the UN Statistical Institute for Asia and the Pacific (UNSIAP), under the overall guidance of the UN Committee of Experts on Environmental Economic Accounting (UNCEEA).

Disasters continue to have an ever-increasing impact on life, well-being and sustainable development. Given these impacts, there is a high demand at the local, national and international levels for high quality, integrated statistics to inform all phases of the disaster risk management cycle. Furthermore, disaster related statistics are not only relevant to those working to prepare and respond to disasters but also to the public at large, research institutions and policy analysts at the local and national levels. In response to the increased demands for disaster related statistics, the disaster management and official statistics communities in Asia-Pacific have developed the Disaster-related Statistics Framework (DRSF).

This course will focus on climate change indicators that can be compiled from environmental economic accounts. After a brief overview of climate change and, relevant polices and multilateral agreements, participants will learn how to compile various indicators that inform climate change. The focus of the course is on better understanding the relationship between climate change and economic activity. And the statistical framework that provides the concepts, definitions, and methodology for measuring this relationship is the System of Environmental Economic Accounting. In particular, participants will learn about physical supply and use tables for energy and air emissions, and indicators that can be compiled from these accounts. Other topics to be discussed include transaction accounts which can be used to derived expenditure type indicators such those on taxes on energy and pollution. Further details on course content follow below.

Disasters continue to have an ever-increasing impact on life, well-being and sustainable development. Given these impacts, there is a high demand at the local, national and international levels for high quality, integrated statistics to inform all phases of the disaster risk management cycle. Furthermore, disaster related statistics are not only relevant to those working to prepare and respond to disasters but also to the public at large, research institutions and policy analysts at the local and national levels. In response to the increased demands for disaster related statistics, the disaster management and official statistics communities in Asia-Pacific have developed the Disaster-related Statistics Framework (DRSF).

Environment is one of the three pillars of the 2030 Agenda and the Sustainable Development Goals (SDGs). Compiling the environment related indicators is key to informing policies and measuring progress towards achieving the SDGs. This course focuses on the 25 SDG indicators that are under the custodianship of UNEP. In the 10 modules of the course, participants will learn about the importance of monitoring the environmental dimension of development, the linkages of indicators with existing statistical frameworks, and how to use environment statistics in decision making. They will also learn about indicators informing specific topics including sustainable consumption and production, waste and oceans. Target participants are officials in national statistical offices, line ministries (especially ministries of environment) and other institutions who are working on the SDGs and environment related issues. The course can also be useful to a wider audience who is interested in learning more about environmental SDG indicators.

This course in Thai language aims to build national capacities for the compilation of disaster related statistics for enhancing disaster management and promote the risk-informed sustainable development in Thailand. The course also aims to facilitate national monitoring and reporting on the Sendai Framework for Disaster Risk Reduction and the 2030 Agenda for Sustainable Development. It introduces concepts relevant to compiling disaster related statistics. The course consists of online modules and online discussion forums. The online modules provide an overview of several topics including measuring disaster risk, measuring economic losses and disaster-risk reduction expenditures. Online discussion forums will provide a venue allow participants to interact with each other and ask questions. For further details on the course, please refer to the attached guidelines for participants.

This course is an advanced course in data visualization, conceived as an extension to SIAP’s facilitated course “Data Visualization for Official Statistics and SDG Indicators” conducted in 2021 and 2022 or, alternatively, to the self-paced course “Principles of Data Visualization for Official Statistics and SDG Indicators” available on SIAP’s e-learning platform. This new course focuses on methods to produce high-quality graphics for monitoring and publishing official statistics and the Sustainable Development Goals (SDGs) indicators. It also highlights advanced topics such as network visualization, high dimension data visualization, visualizing uncertainty and address some important issues when communicating with graphics. In particular, storytelling and the construction of visual narratives for any type of audience, or inclusive data visualization, is emphasized and discussed.

Water is essential to achieve sustainable development. At a fundamental level, the availability of clean water in and of itself is necessary for our health and overall well-being. Water is also an important input to many economic activities, from the production of food and energy to the manufacturing of computers and cars. High quality and timely statistics on water are need in order to manage our water assets and track water supply and use. This course will focus on water statistics and accounts, as well as some important water aggregates and indicators (including SDGs). The water accounts are based on the System for Environmental-Economic Accounting-Water (SEEA-Water) and the International Recommendations for Water Statistics (IRWS). These two documents support analysis of the role of water within the economy, understanding of the hydrological cycle and valuation of water assets. They are consistent with the SEEA Central Framework and follow a similar accounting structure to the System of National Accounts (SNA).

Inclusive and sustainable industrial development (ISID) is a primary source of income generation, allows for rapid and sustained increases in living standards of people, and provides technological solutions for an environmentally sound development. ISID is an integral part of the SDGs, particularly SDG 9, which focuses on industry, innovation and infrastructure. Promoting inclusive and sustainable industries and continuing to invest in physical infrastructure, innovation and research are vital to long-term economic development. Furthermore, industrialization not only contributes to economic growth and infrastructure upgrading, but can also directly and indirectly support the achievement of the SDGs’ socio-economic and environmental objectives through the creation of jobs, improvements in working conditions, innovation, and the development of new and greener production technologies. In addition to SDG 9, all other SDGs incorporate some industry-related aspects and targets.

Identifying and responding to the needs of users for data and statistics is at the heart of the mission of National Statistical Offices (NSOs) and national statistical systems. NSOs already undertake many activities interacting with different users such as preparing press releases to aid journalists and communicate with the public at large and organizing launch events/workshops to disseminate important results from surveys and censuses. In general, the NSO interacts with users towards the tail end of the statistical production process when the data is disseminated.There is an opportunity for national statistical offices to further improve their collaboration with users by engaging more systematically throughout the statistical production process. User engagement is the process of conducting a dialogue with users of official statistics to understand their needs and improve the products, services and operation of a statistical organization accordingly.

The numbers of refugees and internally displaced persons (IDPs) have increased rapidly in recent years, with UNHCR estimating over 100 million forcibly displaced persons in 2022. With displacement’s growing prominence, there is increasing interest at national, regional and international levels in statistics on refugees and IDPs including complete, accurate, timely and internationally comparable estimates of the numbers of people displaced, their well-being and vulnerabilities. Good-quality statistics are a requirement for developing, implementing and monitoring national, regional and international policies and agreements concerning refugees and IDPs. This course introduces the “International Recommendations on Refugee Statistics” (IRRS) and the “International Recommendations on IDP Statistics” (IRIS). These sets of recommendations were endorsed by the United Nations Statistical Commission in 2018 (IRRS) and 2020 (IRIS).

The numbers of refugees and internally displaced persons (IDPs) have increased rapidly in recent years, with UNHCR estimating over 100 million forcibly displaced persons in 2022. With displacement’s growing prominence, there is increasing interest at national, regional and international levels in statistics on refugees and IDPs including complete, accurate, timely and internationally comparable estimates of the numbers of people displaced, their well-being and vulnerabilities. Good-quality statistics are a requirement for developing, implementing and monitoring national, regional and international policies and agreements concerning refugees and IDPs. This course introduces the “International Recommendations on Refugee Statistics” (IRRS) and the “International Recommendations on IDP Statistics” (IRIS). These sets of recommendations were endorsed by the United Nations Statistical Commission in 2018 (IRRS) and 2020 (IRIS).

This course introduces concepts relevant to compiling indicators that inform climate change. The topics covered include among others: climate change policies and indicators; energy and air emission accounts; and data sources. Weekly webinars will also be held as part of the course to provide an overview of the topics and allow participants to ask questions. Following the completion of the course and depending on resource availability, in-kind support will be provided to countries interested in the compilation of climate change indicators that will be introduced and discussed during the course.

The course is designed for personnel working in the field of statistics, whose main responsibilities include data analysis of SDG indicators and related statistics with a specific target on data scientists from NSOs with an experience in both statistical modelling (regression analysis, prediction, classification, ...) and with programming or algorithmic skills. Although no programming will be required to follow and succeed in the course, the pedagogical materials include R code, in the form of reproducible markdown notebooks, as well as some Python resources and code.

This course introduces concepts relevant to compiling energy statistics and accounts, including compilation of relevant SDG indicators. The topics covered include physical supply and use tables, asset accounts, valuation and data sources. Webinars will also be held as part of the course to provide an overview of the topics and allow participants to ask questions. Following the completion of the course and depending on resource availability, in-kind support will be provided to countries interested in the compilation of the energy accounts using tools that will be introduced and discussed during the course.

Statistical Business Registers (SBRs) are part of the statistical infrastructure and form the backbone for producing economic statistics that meet the increasing demand for better integrated, coherent, and comparable statistics across countries and statistical domains. This course is designed to provide a strong foundation for the concepts, methods and framework required to build high-quality and viable SBRs. This will facilitate the international harmonization of economic statistics in terms of coverage, statistical units, and frame methodology.

The objective of the workshop is to build participants’ knowledge and skills in the use of register-based censuses to produce official statistics as an alternative to the traditional census, while also providing a platform for exchanging experiences and lessons learnt in transitioning to this approach.

The main reason for this workshop is to bring representatives of National Statistical Offices (NSOs), National Statistical System (NSS) and Planning Departments made up of Line Ministries responsible for planning and policy making at the national level (Data Users) to discuss the challenges around Statistical Literacy and Capacity Development in the Pacific.

The workshop is to explore the potential benefits and pitfalls, as well as presenting a number of case studies from peers in the region to dive more deeply into specific contextual examples. Platform-specific experts will provide insights into how to communicate persuasively across Twitter, Facebook and LinkedIn, and National Statistical Offices (NSOs) will have opportunities to share their own experiences and discuss best practices.

The objective of the workshop is to provide staff of national statistical systems with tools, methods and techniques for improved facilitation and communication in a workshop setting, including remote trainings. Topics to be covered during the workshop include: “Questioning, Listening & Responding Skills”, “Discussion leading process” and “Preparing a session plan”. At the end of the workshop participants will also practice the facilitation skills they have learned during the workshop.

This course introduces the SEEA Central Framework, the international statistical standards for measuring the interactions between the environment and the economy. By providing an internationally agreed standard with agreed concepts, definitions and classifications, the SEEA is an invaluable tool for compiling integrated information on the economy and the environment. The SEEA uses concepts, definitions and classifications consistent with the SNA in order to facilitate the integration of environmental and economic statistics. By doing so, the SEEA allows users to develop indicators (including SDG indicators) and conduct analysis on the economy-environment nexus.

Timely, reliable, and comparable health statistics are fundamental to monitoring the health status of the population and for developing, implementing, and evaluating health policies and practices that address health and health care. With the adoption of the 2030 Agenda for Sustainable Development, the need for high-quality data for the over 50 health and health-related Sustainable Development Goal (SDG) indicators has increased. The COVID-19 has impacted the health and well-being of populations all over the world and further underscored the need for real-time, good quality, disaggregated data to track health. Nonetheless, for many countries, limited statistical capacity continues to pose a major challenge for monitoring the health and health-related SDGs. The objective of this e-Learning course is to address this issue by providing a general understanding of the basic concept, methods and framework required to compile and monitor the health and health-related indicators.

The course introduces the SEEA EA, the international statistical standard for organizing data about ecosystems, measuring ecosystem services, tracking changes in ecosystem assets, and linking this information to economic and other human activity. The SEEA EA framework takes a spatial approach to organizing information on ecosystems. It supports the compilation of indicators for several global policy frameworks including the 2030 Agenda for Sustainable Development and related SDG indicators and the Post 2020 Global Biodiversity Agenda. The SEEA EA complements the accounts compiled in the SEEA Central Framework (SEEA CF) and together, SEEA EA and SEEA CF, provide a comprehensive framework for organizing data on the relationship between the environment and economy.

This course introduces concepts relevant to compiling economy wide material flow accounts (EW-MFA); it also covers the methodologies for the SDG indicators 8.4.1/12.2/1 and 8.4.2/12.2.2. EW-MFA represents a framework for describing the interaction of a domestic economy with the natural environment and the economy of the rest of the world in terms of flows of materials, waste and emissions. They are part of the broader System of Environmental Economic Accounting (SEEA) which is the international statistical standard for measuring the relationship between the economy and the environment.

The online course on crime statistics from a gender perspective will highlight basic concepts, methods and frameworks required to compile crime statistics in relation to gender and provide knowledge on the challenges and opportunities of working with different types of data sources (administrative data, sample surveys).

The course provides an opportunity for participants to explore and comprehend the techniques of data visualization for data exploration as well as for data presentation. Participants will discover, evaluate and apply the rules of data visualization on devoted cases studies and also tackle the problem of visualizing complex data. The course proposes strategies for visualizing in multi-dimensions as well as presenting the practical methods for representing statistical indicators on maps or within dashboards.

This course will focus on climate change indicators that can be compiled from environmental economic accounts. After a brief overview of climate change and, relevant polices and multilateral agreements, participants will learn how to compile various indicators that inform climate change. The focus of the course is on better understanding the relationship between climate change and economic activity. And the statistical framework that provides the concepts, definitions, and methodology for measuring this relationship is the System of Environmental Economic Accounting. In particular, participants will learn about physical supply and use tables for energy and air emissions, and indicators that can be compiled from these accounts. Other topics to be discussed include transaction accounts which can be used to derived expenditure type indicators such those on taxes on energy and pollution.