DSA June 2019 Summer School on Machine Learning and Data Science

Dates: 03 June - 05 June 2019
Venue: Addis Ababa University, Addis Ababa

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, complemented by moderate to advanced practical and tutorial sessions. Participants gained hands-on experience wrangling and processing datasets and applying cutting-edge machine learning techniques to draw meaningful insights from data.

Lectures were delivered by distinguished, world-renowned researchers and practitioners, including experts from Sheffield University, Amazon, ARM, 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 featured end-to-end tutorial sessions led by professionals who guided participants through real-world data analytics problems — from data acquisition to data presentation. To benefit from the course, participants were required to have prior knowledge of programming, particularly in Python and machine learning. The registration process included the submission of worked examples in Jupyter notebooks as part of a data science challenge.

School programme outline:

Pre-workshop

Configure a DSA Environment with Anaconda

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

Overview

Using Anaconda consists of the following:

1. Install miniconda on your computer
2. Create a new conda environment using this project
3. Each time you wish to work, activate your conda environment

Installation

Download the latest version of miniconda that matches your system.

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

Setup the dsa2018 environment.

git clone https://github.com/emwebaze/dsa2018materials.git
cd setup

Create dsa2018. Running this command will create a new conda environment that is provisioned with most of the libraries you need for this summerschool.

conda env create -f dsaenvironment.yml

Verify that the dsa2018 environment was created in your environments:

conda info --envs

Cleanup downloaded libraries (remove tarballs, zip files, etc):

conda clean -tp

Uninstalling

To uninstall the environment:

conda env remove -n dsa2018

Using Anaconda

Now that you have created an environment, in order to use it, you will need to activate the environment. This must be done each time you begin a new working session i.e. open a new terminal window.

Activate the dsa2018 environment:

OS X and Linux

$ source activate dsa2018

Windows

Depending on shell either:

$ source activate dsa2018

or

$ activate dsa2018

That’s it. Now you can fire up your Jupyter Notebook from this terminal and it will load all the necessary libraries.

To exit the environment when you have completed your work session, simply close the terminal window.

Troubleshooting and comments..

Use the comment section below to (a) ask questions that are not already answered (b) help your peers by providing answers to their questions, if you can.

Summer School Day 1

The first day of the data science school will start with an introduction of Machine Learning and Data Science, introduce python, jupyter notebook and pandas and Fundamentals of IoT, Data Visualisation and a tutorial on Mechanism Design. We have practical sessions for python, pandas and jupter and data Visualisation.

Summer School Day 2

Summer School Day 3