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Syllabus of the UN-Data Analytics Professional Certificate
UN-DAPC- Fall Semester
Contents
Learning outcomes ......................................................................................................................... 1
Target audience ............................................................................................................................... 2
Thematic self-paced modules .................................................................................................... 2
Live webinars .................................................................................................................................... 4
On the job practice ......................................................................................................................... 4
Tools ..................................................................................................................................................... 6
Completion requirements ............................................................................................................ 6
Digital certification ......................................................................................................................... 6
Faculty ................................................................................................................................................. 6
Learning outcomes
The UN DAPC offers a unique blended curriculum on descriptive and predictive
analytics in the UN context. Over six months, participants are guided through a
specialized learning built by UN data experts.
The programme has been designed to prepare UN staff to unlock their data potential
through a comprehensive and interactive overview of core data science concepts
from descriptive to predictive analytics. Participants will be better equipped to
formulate problem statements for data-informed solutions, apply data visualization
and storytelling design principles to deliver powerful messages, and build basic
predictive models with appropriate methods and skills. They will have the opportunity
to hone their skills in effectively communicating data analysis findings and dealing
with the ethical dilemmas and risks associated with working with real-world data
cases. At the end of the Programme, participants will be able to:
• Explain the different types of analytics and their applications in the UN
context
• Implement a scoped data analysis to their needs for information
• Use data visualization and storytelling techniques to communicate key
messages
• Identify suitable predictive analytics applications to meet the business
needs at their workplaces
• Describe the key features of predictive models, understand risks and how
to ensure an ethical use.
It is composed of self-paced lessons and instructor led webinars. This course's key
features are practical case studies and on-the-job practice opportunities. The features
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ensure that participants not only gain solid knowledge and skills but also apply them.
The UN DAPC offers a number of learning modalities that give participants a unique
opportunity to learn by doing through a data-use case approach.
Target audience
The course targets UN personnel (professional and general service staff) at
headquarters and field locations, interested in using data more effectively at work. It
will be of great benefit to those who need to present analyses or to those charged with
research, analytical and reporting responsibilities. More in general, the course will
benefit all UN staff interested in expanding their knowledge and ability to access, use,
interpret and communicate data.
Thematic self-paced modules
A comprehensive curriculum of thematic self-paced modules delivered online
through UNSSC’s e-learning platform. More information on the 10 thematic modules
is displayed below:
Module 1 Data fundamentals
This module lays the foundation of data science. It describes the main elements and
characteristics of data and the importance of data for the United Nations. Meanwhile, it
introduces descriptive statistical measures that can help us understand the quality of our
data.
Module 2 Data science project
This module offers an overview of a data science project. We explore the management
components of a data science project, and identify different types of approaches to data
analysis. And then, we analyze different methods for data sampling and data collection to
get high-quality data for analysis.
Module 3 Data exploration and analysis
This module guides us to the core steps of a data science project: data preparation and
data analysis. We discuss the processes of data cleaning and the measures for data
protection. Also, we cover the concepts and skills of data analysis and statistical models
with EXCEL examples.
Module 4 Data for decision making
This module explains the use of data analysis results in the decision-making process. We
establish the processes of turning data into wisdom, and at the same time, understand
biases and noisy environments interrupt thoughtful data decisions. From here, we
explore the efforts conducted to move towards data-driven organizations.
Module 5 Data visualization- Part 1
This module reveals the basics of data visualization. Starting from the concepts and
theories, we learn to communicate with data and explore the scenarios for different
visualization types. By analyzing some particular examples, we identify practices that
misuse data and manipulate the information, as well as skills to make accessible data
visualizations.
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Module 6 Data visualization- Part 2
This module extends the data visualization to the advanced level. We refine our graphs
from a design standpoint to ensure the message is clear and well-emphasized. Then, we
learn how to interpret more advanced graph types, as well as when to use them.
Module 7 Data storytelling
This module incorporates data into storytelling to deliver an engaging and credible
message. We explore how to leverage data storytelling concepts and approaches to enable
decision-making. Following the traditional narrative arc, we consider the context, the
message, and the interactivity to build a powerful data story.
Module 8 Fundamentals of predictive analytics
This module introduces the key concepts and features of predictive analytics. From its
mathematical logic to various methods, we learn the fundamentals of predictive analytics
and the procedures for developing a predictive model. With real-life examples, we examine
different kinds of predictive models for suitable scenarios in social sciences.
Module 9 The science of predictive analytics
This module describes common approaches to predictive analytics. First, we learn the
components and functions of time series and learn how to develop one. Second, we
analyze the methods of machine learning and its application. Third, we explore how to use
ensemble learning to build a high-quality predictive model.
Module 10 Applying predictive analytics
This module demonstrates the process of machine learning model deployment and the
ethical use of predictive analytics. We learn the main steps and identify key arrangements
to deploy predictive models from production to operation. Also, we discuss key
considerations of ethics and risks in applying predictive models, and summarize good
practice.
All self-thematic modules include a resource section with relevant materials to read.
Each self-paced module takes about 4 hours to complete.
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Live webinars
Practical and interactive live webinar sessions with practitioners and data experts.
Information about the topics, duration and dates is displayed below.
Live session Time Date
Orientation webinar
60 min
17 September
Data collection and cleaning
120 min
19 September
Data exploration and analysis
120 min
26 September
Cognitive bias and logical fallacies
120 min
3 October
Data Visualization 1
120 min
10 October
Data Visualization 2
120 min
17 October
Data Storytelling
120 min
24 October
Orientation and tools to predictive modeling
120 min
31 October
Predictive analysis of malaria prevalence
120 min
7 November
Conflict prevention through classification analysis
120 min
14 November
Analysis of time series forecasting
120 min
21 November
On the job practice and Case study
Mentoring sessions will guide the application of the knowledge and newly acquired
skills. Throughout the practice, the learners will join 3 group meetings with their
mentors.
On the Job Project
The On the Job Project (OJP) is expected to summarize the skills and knowledge
gained through the training directly applicable to the learners’ work.
The learner should choose one of the following areas of specialization:
1) Data preparation, exploration and analysis
2) Data visualization and storytelling
By 20 September the learner should choose their specialization and inform
the UNSSC Team, so he/she will be assigned to a mentor.
The first meeting will take place by 4 October to discuss potential approaches for the
OJP and guide the learner on the proposal.
The OJP proposal (1-2 pages) will include:
1) A title and brief description of the proposed project, in one of the three areas of
specialization
2) A brief statement of expected results
3) Specification of methods and tools to use
By 11 October the learner should submit the OJP proposal to the mentor, after the
first session. Then mentors will provide written feedback by Oct 18.
The second meeting will be scheduled during the first week of November to give
guidance and support during the research.
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By 29 November, the learner should submit a brief OJP report documenting the project
experience and the results obtained.
The OJP report (3-4 pages) will include:
1) Summary of the work conducted
2) Examples of analysis or visualizations used
3) A brief statement of conclusions
By 13 December the mentor should provide brief written feedback and have a
meeting with the mentees. During the meeting, mentors will clarify feedback and
share best practices.
Case Study
For those learners that do not work with data, or prefer to work on a given Case Study
(CS), they will be asked to choose one of the following areas of specialization:
1) Data preparation, exploration and analysis
2) Data visualization and storytelling
3) Predictive modeling
By 20 September the learner should inform the UNSSC Team about the area
of interest, and he/she will be assigned to a case-study mentor.
Throughout the practice, the learners will join 3 group meetings with their mentors.
The first meeting will take place by 4 October to discuss potential approaches for the
case study.
The CS proposal (1-2 pages) will include:
1) A title and brief description of the proposed project, in one of the three areas of
specialization
2) A brief statement of expected results
3) Specification of methods and tools to use
By 11 October the learner should submit the proposal to the mentor, after the
first session. Then mentors will provide written feedback by Oct 18.
The second meeting will be scheduled during the first week of November to give
guidance and support during the research.
By 29 November , the learner should submit a brief CS report documenting
the experience and the results obtained.
The CS report (3-4 pages) will include:
1) Summary of the work conducted
2) Examples of analysis, visualizations or models used
3) A brief summary of conclusions
By 13 December the mentor should provide brief written feedback and have a
meeting with the group. During the meeting, mentors will clarify feedback and share
best practices.
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Tools
The self-paced modules are mainly tool agnostic, as they focus on principles and
concepts that can be used by any tool. During the webinars, the learners will discuss
and practice with different tools, including:
1) Data fundamentals: Excel, OpenRefine, Trifacta Wrangler, and WinPur
2) Data visualization: Flourish, Tableau, Power BI, ChatGPT
3) Predictive modeling: BigML and Google AI
During the OJP or CS, participants will be able to practice with the tools discussed
during the webinars or others, in coordination with the mentors. However, the main
focus is given to the tools that are free to use. Excel will be the main tool for data
cleaning and analysis. Power BI has a free version that will be available for all of our
learners to carry out data exploration. Also, the free version of Flourish is good enough
to carry out the work of data visualization practice in our course. Lastly, we will offer a
short-term learner license of Tableau for participants in the data visualization track,
which gives full access to the app.
Completion requirements
The UN DAPC will be issued upon successful completion of mandatory activities,
final quiz and OJP/CS positive assessment. To receive the Certificate, the learners
should have:
• Completed all lessons in the Modules
• Responded to the questions in the discussion forums
• Joined all live webinars
• Completed a final test
• Submitted the OJP/CS report
• Answered a final survey about the course
Digital certification
A digital certificate will be issued as indicator of accomplishment of the acquired
learning. It will be possible to display and verify the certificate online following open
badge standards.
Faculty
• Rebeca Pop
• Katerina Tsetsura
• Tarek Azzam
• Jaume Manero
• Demetrio Barragan
• Rose Barranco
• Itziar Arispe
