DSA June 2017 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 huge amounts of data or unique datasets.

The summer school focused on introductory and advanced lectures in data science and machine learning, as well as moderate to advanced practical and tutorial sessions where participants got their hands wet wrangling and munging datasets and applied cutting-edge machine learning techniques to derive inference from the data. Lectures were delivered by distinguished world-renowned researchers and practitioners, including researchers from Sheffield University, Amazon, Swansea University Medical School, Facebook, Pulse Lab Kampala, the AI and Data Science (AIR) Lab–Makerere University, ARM, and Dedan Kimathi University of Technology (DeKUT).

The school also involved end-to-end tutorial sessions from professionals who walked the participants through a real data analytics problem from data acquisition to data presentation. To benefit from this course, participants were encouraged to have some background in programming, particularly programming with Python.

School Programme Outline

Pre-workshop

Stuff to install
To ensure participants the ground running, it was essential to install the prerequisite software, test it out, and make sure it worked on their computers. The venue for the summer school had some computers on which the software was preinstalled, but participants were advised to come with their own laptops with the software installed.

Anaconda
Luckily, all the software required had been prepackaged in a bundle called Anaconda. Participants could download the various versions of the software for their laptop OS and architecture from the Anaconda website. They were instructed to download the Python 3.6 version. Instructions on how to install it were provided next to the download links on the Anaconda website.

Stuff to do
To ensure that the software worked properly on their machines and to get them up and running, participants downloaded the provided Jupyter notebook (right-click and “save as”) and completed the exercises therein. To access it, they needed to run a Jupyter notebook (instructions were provided).

Troubleshooting and comments
Participants used the comment section below to (a) ask questions that had not already been answered, and (b) help their peers by providing answers to their questions whenever possible.

Summer School Day 1

The first day of the data science school introduced the Jupyter notebook and provided an overview of the use of Python for analyzing data. The session introduced the machine learning technique of classification and included lab practicals that explored these techniques.

Summer School Day 2

The second day will feature two tracks dealing with applications of data science in health and an introduction to the Internet of Things.

Summer School Day 3

The third day will feature a single track of lectures and practical sessions. However, there will be an opportunity for interested participants to explore building sensor systems for data collection during the practical sessions