Closing gender data gaps is essential for achieving the Sustainable Development Goals (SDGs). Gender statistics are critical for understanding the distinct realities of women and men, girls and boys, and for informing policies aimed at addressing inequalities, but data and information gaps are often impediments to achieving this. While efforts to enhance gender data production have advanced, significant gaps still remain.
This course introduces machine learning as a tool for using either traditional (surveys, micro data,…) or non-traditional data sources (Big Data) to produce high quality predictions for Official Statistics or Sustainable Development Goals (SDGs) indicators.
The overall objective of the week-long training is to help participants better understand the use and benefits of Big Data in the production of gender statistics. To that end, the training program will present recent development in the compilation of gender statistics as well as new methods, case studies and processes that can facilitate the integration of Big Data in this process. The training will also emphasize the limitations, constraints and privacy issues inherent to the use of Big, non-traditional or administrative data.
The achievement of the Sustainable Development Goals (SDGs) requires the availability of highquality, timely and reliable data to produce the relevant SDG indicators and other statistics, disaggregated as relevant. To meet this need, official statistics must modernize and incorporate new data sources, including Big Data.
The Economic and Social Commission for Asia and the Pacific (ESCAP) is implementing a project supporting national statistical offices in Asia and the Pacific to leverage innovative data sources, tools and methods for the streamlined production and use of better, more timely data for official statistics: “The 2030 Data Decade – Strengthening the institutional capacity of national statistical offices in Asia and the Pacific to use innovative, new and big data sources for official statistics in support of the 2030 Agenda for Sustainable Development”, which is funded through the 2030 Agenda for
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 achievement of the Sustainable Development Goals (SDGs) requires the availability of high quality, timely and reliable data to produce the relevant SDG indicators and other statistics, disaggregated as relevant. In order to meet this need, official statistics must modernize and incorporate new data sources, including Big Data.
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.
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 training course is aimed at strengthening the technical capacity of statistical producers to incorporate new data sources, including Big Data, into their statistical production processes. This will enhance the capacity to provide of high-quality, timely and reliable data to produce the relevant SDG indicators and other statistics. This training aims at sharing experience and building the skills required for the production and dissemination of official statistics with new data sources.
