Interested in using data effectively? The e-learning path on Data Analytics is designed to enhance your ability to analyse, visualize, report and communicate data effectively. Participants will complete modules and activities at their own pace. They will participate in engaging online forums and interact with faculty and peers.

Introduction

The Secretary-General's Data Strategy highlights the need to start with "data action that adds immediate value for our organization and the people we serve". This programme is designed to enhance the ability of UN personnel to effectively apply and use data in their work. It is geared to those in research, analytics and reporting, as well as those who wish to expand their knowledge and ability to access, use, interpret and communicate data.

Objectives

Upon successful completion 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 of their needs for information.
  • Use data visualization and storytelling techniques to communicate key messages.
  • Identify applications of predictive analytics at their workplace.
  • Describe key features of predictive models, understanding risks and how to ensure an ethical use.
Course methodology

It is a self-paced learning path delivered entirely online. Participants can start at any time and complete modules and activities at their own pace.

The e-learning path on Data Analytics offers:

  • A micro-learning experience characterized by small knowledge units (modules and micro-lessons) where participants consolidate and reflect on learnings through the creation of micro-content (multimodal forum replies, etc.).
  • Scenario-based learning exposing participants to unique real-life challenges and tasks of UN managers
  • Consolidation of takeaways through reflective practices and social learning to facilitate information exchange and peer-to-peer learning
  • Unlimited access to all modules for six months from the date of enrolment.
  • A large collection of relevant tools, readings, guidelines and examples that complement the concepts and theories covered in the course.

Participants will be granted unlimited access to the learning path for six months from the date of enrolment. UNSSC's dedicated e-learning platform tracks completion of individual modules. A final certificate will be given to participants upon completion of all modules in the learning path.

Each module is estimated to require approximately three hours of study time to complete at your own pace.

This learning path forms part of the UNSSC Blueline learning platform. Subject to completion of this learning path, interested participants can sign up and access other E-Learning Paths in Blueline.

This learning path is also accessible through the UNKampus 30 platform for non-UN staff.

Please contact elp@unssc.org for further information.

Course contents

The e-learning path on Data Analytics includes the following modules:

  • Data fundamentals
  • Data science project
  • Data exploration and analysis
  • Data visualization- Part 1
  • Data visualization- Part 2
  • Data storytelling
  • Data for decision making
  • Fundamentals of predictive analytics
  • The science of predictive analytics
  • Applying predictive analytics

The programme is delivered through UNSSC's Blueline e-learning platform. By completing the learning path, participants will have access to an exclusive alumni network for continuous learning and exchange.

Target audience

All UN personnel (professional and general service staff) at headquarters and field locations.

UNFPA personnel can gain free access to this path as part of their corporate subscription to the Blue Line by registering HERE

UN Secretariat personnel can gain free access to this path as part of their corporate subscription to the Blue Line by registering HERE.

Cost of participation

$1.000

Modules

4 h

DATA Module 1: Data Fundamentals

Today, we observe organizations everywhere adjusting to the reality of high amounts of available data, anywhere and almost everything. This is the data revolution. The recently launched UN Secretary - General's Data Strategy identifies data and analytics as a crucial and strategic asset for greater impact on the ground. And emphasizes that data has not fully met its potential yet. This course is a step forward to strengthening UN staff capacities in data collection, management and use. To deliver a stronger support, to those we serve.
English
4 h

DATA Module 10: Applying Predictive Analytics

Predictive analytics can enable us to make better decisions and formulate data-informed strategies. However, when applying predictive analytics, we need to think about the social impact of its application. It is not just a purely mathematical model when used in the wilder operation. We need to take into consideration its risks and limitations.
English
4 h

DATA Module 2: Data Science Project

Data analytics is often visualized as something difficult, with experts immersed in the analysis of complex spreadsheets. But as any other set of tasks with clear objective, data analytics is a data science project. A good understanding of project management facilitates its implementation.
English
4 h

DATA Module 3: Data Exploration and Analysis

The main tasks of a data analytics project are data preparation and data analysis. Once we have collected our data, we need to ensure it is ready for analysis. Correcting data errors, validating data quality, and ensuring data privacy so to speak. Then, we follow the concepts and principles of data analysis to build statistical models. We will unleash the power of data from these processes. 
English
4 h

DATA Module 4: Data for Decision Making

In the world of data, we need to base our decisions on facts. In the past, accessing the right data and exploring it was, in many cases, an almost impossible task. But today, accessing data is key to making decisions at any level, either in the private or public sector.
English
4 h

DATA Module 5: Data Visualization Part 1

The value of data is not unleashed until we understand it and we can communicate it. We visualize data to make a point, to deliver a message. Sometimes, there is a temptation to use all kind of tricks to make the message more attractive. Or, other times we erroneously use the wrong chart type or design elements. Doing this can end up in misusing the data and manipulating information. 
English
4 h

DATA Module 6: Data Visualization Part 2

When was the last time you opened Excel or PowerPoint and clicked on a graph available in templates to quickly and effortlessly generate a graph? While templates can save us a lot of time and effort, they often don’t incorporate design best practices. This means that you’re likely to end up with graphs that are cluttered, confusing, redundant, and not engaging at all. Data visualization best practices teach us about how to make tiny design changes that can ultimately help us communicate the visual message more clearly and in a more engaging manner.
English
4 h

DATA Module 7: Data Storytelling

Data and storytelling might sound like two distinct concepts. Data is usually perceived to be exact, credible, and somewhat unimaginative. On the other hand, storytelling makes us think of culture, creativity, and narratives. In data storytelling, the two concepts come together to deliver a powerful message. One that is engaging for the audience, but also highly credible.
English
4 h

DATA Module 8: Fundamentals of predictive analytics

Let’s switch the gear and face the future! Predictive analytics involves the use of current and historical data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes. With the increase of data availability, predictive analytics have become ubiquitous in many areas for the common good. The UN applies predictive modeling in areas such as humanitarian response, demographic evolution, and prediction of armed conflicts.
English
4 h

DATA Module 9: The Science of Predictive Analytics

Machine Learning (ML) techniques are tools used in predictive analytics to find underlying patterns within complex data automatically. ML uses programmed algorithms that analyze input data to predict output values. The power of machine learning algorithms exceeds human capabilities, and their applications are endless.
English