Introduction to Big Data analytics | June 2019 Workshop

Research is currently underway to build a comprehensive and sustainable database for Sickle Cell affected individuals living in Sub Saharan Africa. To maximise the utility of this database - specifically for the needs of the local communities involved - it is imperative that researchers from many African institutions are able to effectively plan and execute research projects aimed at reducing the burden of this disease, for both nearby residents, and the world sickle cell community as a whole. This course will give participants the tools needed to design and implement such studies. Students will learn how to: apply for necessary clearances; collect and analyse their own high quality data with high performance computational methods; integrate their data with other studies; interpret the results in the wider context of epidemiology and shifts in government policy.

Raphael Sangeda, Nchangwi Syntia Munung, Nicola Mulder, Gaston Mazandu, Mario Jonas, Arthemon Nguweneza, Vicky Nembaware, Jack Morrice
Event Theme/Subject Category: 
Coordinator's name: 
Vicky Nembaware
Coordinator's email address :
Name of Venue, Institute: 
Wolfson Lab, University of Cape Town
City of Venue: 
Cape Town
Country of Venue: 
South Africa
Organisers/Organizing Body: 
National Institutes of Health
Dates of Event: 
Monday, June 10, 2019 - 09:00 to Friday, June 14, 2019 - 16:00
Eligibility & Application Instructions: 
must have satisfactorily engaged with preceding online component.
Logos of supporting Institute: 
Logos of supporting Institute (2nd): 
Logos of supporting Institute (3rd): 
Extra information: 
Students are asked to bring their own research data; during the workshop they will formulate a research project with ethical clearance application, perform summary statistics, and draft a research question and abstract.
Targeted Learning Outcomes: 
On completing the course, students will be able to: list ways that Big Data sets arise in clinical epidemiology research; describe what is meant by the term “Big Data” in clinical epidemiology; apply basic statistical methods to epidemiological Big Data sets; evaluate the quality of an epidemiological Big Data set, in general and with respect to a specific research question; design a multi-site retrospective epidemiology study.
SickleInAfrica Project/PI/Working Group: