DSA November 2018 Summer School on Machine Learning and Data Science

In the tradition of previous Africa Data Science workshops, a summer school on machine learning and data science was held prior to the main workshop. This summer school targeted graduate students, researchers, and professionals working with large or unique datasets.

The summer school focused on both introductory and advanced lectures in data science and machine learning, as well as moderate to advanced practical and tutorial sessions. Participants gained hands-on experience wrangling and processing datasets and applying cutting-edge machine learning techniques to derive insights from data.

Lectures were delivered by distinguished, world-renowned researchers and practitioners, including experts from Sheffield University, Amazon, the University of Lagos, Facebook, Google, Pulse Lab Kampala, the AI and Data Science (AIR) Lab at Makerere University, Dedan Kimathi University of Technology (DeKUT), and the African University of Science and Technology (AUST), among others.

The school also involved end-to-end tutorial sessions led by professionals who guided participants through real data analytics problems—from data acquisition to data presentation. To benefit from the course, participants were required to have some background in programming, particularly in Python and machine learning. The registration process included the submission of worked examples in Jupyter notebooks. Registration was officially closed before the commencement of the program.

School Programme Outline

Stuff to Install
To ensure participants hit the ground running, it was essential that they installed the prerequisite software, tested it, and confirmed it was functioning properly on their computers. While the summer school venue had computers with the necessary software preinstalled, participants were advised to bring their own laptops with the software set up.

Fortunately, all the required software had been prepackaged in a bundle called Anaconda. Participants could download the appropriate version for their operating system and architecture from the Anaconda website.

Stuff to Do
To verify that the software worked correctly and to get participants ready for the sessions, a Jupyter notebook was provided for download. Participants were encouraged to complete the exercises in it. Those unfamiliar with Jupyter notebooks were advised to seek online tutorials and guidance to get started.

Troubleshooting and Comments
Participants used the comment section below to (a) ask questions not already addressed, and (b) assist their peers by providing helpful answers where possible.

Summer School Day 1

The first day of the data science school began with an introduction to Data Science, followed by sessions on Python, Jupyter Notebook, and Pandas. Participants were also introduced to the Fundamentals of IoT, Data Visualization, and Mechanism Design. The day featured practical sessions covering Python, Pandas, Jupyter Notebook, and Data Visualization, enabling participants to apply the concepts learned in real time.

Summer School Day 2

Summer School Day 3