Name: Analyzing and Forecasting Public Health Data using Time Series Analysis in R
Start: 2023-05-27T17:00:00+02:00
End: 2023-05-27T18:30:00+02:00

We are excited to be hosting George Ngugi for our May meetup. George is AI and Data professional at inSupply Health Ltd in Kenya. He is thrilled to share his knowledge and expertise with you at the upcoming #RStats meetup.

George is an experienced R User. He loves helping people gain insights into the world of data and AI and to make complex concepts more accessible to all. Don't miss out on the opportunity to learn a few forecasting tricks from him in this upcoming meetup.

George may do some live demonstrations of code during the meetup. If you want to follow up, ensure you have R and R Studio on your computer.

The meeting will be online, and the Zoom Link will be shared in due course.

R can be downloaded using this link: https://cran.r-project.org/bin/windows/base/
R Studio can be downloaded at this link: https://posit.co/downloads/

Emmanuel Ọlámíjùwón

Nontsikelelo Shongwe

EswatiniUseR Group

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Celumusa Fakudze

Sicabí Cruz

Jawad Ashraf

Kamogelo Mogoba

Nelson Isidoro

Phinda Peter masilela

Buhle Shabangu

George is an AI and Data professional at inSupply Health Ltd in Kenya, where he forecasts health commodities using digital tools like machine learning with R. He is a data guy through and through and enjoys getting his hands dirty with large datasets.

He is particularly skilled in R programming, especially the tidyverse dialect. He is passionate about staying up-to-date with the latest technologies and techniques in the field so that he can ensure that in-Supply Health Ltd is at the forefront of the industry.

Additional information about our speaker can be found here:
[https://georgengugi.netlify.app/](https://georgengugi.netlify.app/)
[https://github.com/mwangi-george](https://github.com/mwangi-george)

George Ngugi

Analyzing and Forecasting Public Health Data using Time Series Analysis in R

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Analyzing and Forecasting Public Health Data using Time Series Analysis in R

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