UNITED NATIONSECONOMIC AND SOCIAL COMMISSION FOR ASIA AND THE PACIFICSTATISTICAL INSTITUTE FOR ASIA AND THE PACIFIC (SIAP)
Regional Training Course on Big Data for Sustainable Development
19-23 June 2023
Daejeon, Republic of Korea
CONCEPT NOTEI.

Background and rationale

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. In order to meet this need, official statistics must modernize andincorporate new data sources, including Big Data. At its 53rd session in 2022, the StatisticalCommission underscored the importance of mainstreaming the use of big data and data science
into the work programmes of national statistical offices and the necessity to include training in
big data and data science into the training curricula of national statistical offices. This course isa first response to this injunction by sharing experience, and providing the pedagogical activities
required for understanding the process leading to the production and dissemination of official
statistics with new data sources.
The Regional Training Course on Big Data for Sustainable Development will be heldfrom 19 to 23 June 2023 in Daejeon, Republic of Korea, and will be conducted by
the Statistical Institute for Asia and the Pacific (SIAP), in collaboration with Statistics
Korea (KOSTAT).

II.

Objectives

The course aims to provide a platform for demonstrating methods, good practices and tools andfor exchanging experiences in dealing with challenges and issues in the introduction of Big Data
into the production and dissemination of official statistics. The course will focus on the use of
publicly available data, private sector data sources, administrative data and other type of nontraditional data.

1III. Learning outcomes
As a result of this course, participants are expected to be able to:IV.

•Identify the concepts and technologies needed for analysing Big Data.

•Recognize the different type of Big Data and their use for Official Statistics and SDGIndicators.

•Discuss the processes required to introduce Big Data into the statistical process.

•Retrieve and perform simple analysis of publicly available Big Data.

•Recognize the limitations and challenges when using Big Data

Course design and content

The course is comprised of 6 modules, consisting of presentations on the main topic and related
issues/problems; illustrative good practices from resource persons presenting practical
approaches and solutions to the issues/problems; sharing of relevant country experiences andconcern. Plenary and group discussions will be organized throughout the course. The course is
not based on any specific software nor programming language. However some experience andknowledge in programming would help participants understand some examples and case studies.
To facilitate exchange of experiences, each participant will need to submit a country
presentation of no more than 5 slides on or before 2 June 2023 to escap-siap@un.org andchristophe.bontemps@un.org. The presentation should be 10 minutes long. It should briefly
introduce the current use of Big Data, present main challenges encountered, and outline specificplans to incorporate new data sources into statistical production processes. Please include
relevant detail regarding, (a) the current or planned use of Big Data (including administrative data
sources, private sector and crowd-sourced data); (b) the scope, sources, frequency, and quality
checks protocols used for these data; (c) SDG indicators that will be compiled using Big Data
sources or a combination of sources to include Big Data.

2V.

Course Outline1
ModuleCoverage

Module 1: Overview of Big Data••

Understanding Big Data
Uses of big data

Module 2 : Methods of Big Data••

Reproducible Analytical Pipelines, Machine Learning
Data Visualization

Module 3 : Countries examples•Current and future uses of Big Data at participants’
NSOs

Module 4 : Web scrapping methods•••

Methods, process and examples
Web scrapping with APIs
Web scrapping for CPIModule 5 : Use cases of Big Data

•
••
•
•

AIS data – Applications
Scanner data methods-Applications
Administrative data- Applications
Mobile phone data – Applications
Geospatial methods - Applications

Module 6: Challenges in Big Data•

Big Data for Official Statistics: Integration &limitations

Module 7: Field Trip•

Field trip

VI.

Target participants

The course is designed for mid-level statisticians and technical staff from national statistical
offices whose main responsibilities include the production and dissemination of officialstatistics with new data sources to include private sector data, administrate data and datacollected from non-traditional sources such as social media data, geospatial data and online
pricing data. Approximately 20 participants are expected to attend. The participants are
expected to have a basic proficiency in the analysis and modelling of statistical data and in
the use of a modern statistical software or language (R, Python).

1

The final detailed program of the training will be sent prior to the course.

3