Since 2015, Data Science Africa (DSA) has held an annual summer school and workshop to train participants on machine learning and data science methods and provide an avenue for researchers to present work demonstrating the application of these techniques to problems relevant in the African context.

For 2019, DSA will have two events: DSA 2019 Addis in Ethiopia 03rd to 07th June for East Africa and DSA 2019 Accra for West Africa -date to be confirmed. This follows two events last year with DSA 2018 Abuja in Nigeria 12th to 16th November and DSA 2018 Nyeri in Kenya, and May 31st to June 8th. DSA 2019 Addis Ababa will take DSA’s traditional structure of a three days Summer School and a two Day workshop under the theme "End-to-end Data Science".

DSA 2019 Addis tentative speakers and instructors include:

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 will be held prior to the main workshop. This summer school will target graduate students, researchers and professionals working with huge amounts of data or unique datasets.

The summer school will focus on introductory and advanced lectures in data science and machine learning as well as moderate to advanced practical and tutorial sessions where participants will get their hands wet wrangling and munging datasets and applying cutting edge machine learning techniques to derive inference from the data. Lectures will be given by distinguished world renown researchers and practitioners including researchers from Sheffield University, Amazon, ARM, Facebook, Google, Pulse Lab Kampala, the AI and Data Science (AIR) lab-Makerere University, and Dedan Kimathi University of Technology (DeKUT), African University of Science and Technology (AUST) among others.

The school will also involve end-to-end tutorial sessions from professionals walking the participants through a real data analytics problem from data acquisition to data presentation. To benefit from this course participants must have some background in programming particularly programming with Python and machine learning. The registration process will include submission of worked examples in Jupyter notebooks for a data science challenge. Registration is now OPEN.

School programme outline:

Draft Lecture Schedule

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.

  Linux Mac Windows
64-bit 64-bit (bash installer) 64-bit (bash installer) 64-bit (exe installer)
32-bit 32-bit (bash installer)   32-bit (exe installer)

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.

Time

Activity

Material

08:00-09:00

Arrival and Registration
Host and others

09:00-09:30

Welcome and Opening Speech and Remarks
Addis Ababa University Official and General Chair

09:30-10:30

Mechanism Design
Eric Sodomka (Facebook), Daniel Mutembesa (AIR Lab Makerere)

10:30-11:00

Break

11:00-12:00

Mechanism Design Tutorial
Eric Sodomka (Facebook Research), Daniel Mutembesa (AIR Lab Makerere)

12:00-13:00

Python, Pandas and Jupyter Tutorial
Claire Babirye (UTAMU), Neema Mduma (NM-AIST)

13:00-14:00

Lunch

14:00-15:00

Introduction to Machine Learning
Neil Lawrence (Amazon UK and University of Sheffield)

15:00-16:00

Data Visualisation
Elaine O. Nsoesie (Boston University), Mourine Amoturine (UN Global Pulse)

16:00-16:30

Break

16:30-17:30

Fundamentals of IoT
Ciira Maina (DeKUT), Damon Civin (ARM), Gezehagn Eggi (AAU)

Time

Activity

Material

08:30-09:30

Classification
Mike Smith (University of Sheffield), Martin Mubangizi(Pulse Lab Kampala)

09:30-10:30

Computer Vision and Image Analysis
Solomon Nsumba, Flavia Ninsiima (AIR Lab, Makerere University)

10:30-11:00

Break

11:00-12:00

IoT Field Work
Ciira Maina; Gezehagn Eggi; Jan; Damon

12:00-14:00

IoT Field Work
Ciira Maina; Gezehagn Eggi; Jan; Damon

14:00-15:00

Lunch

15:00-16:00

Deep Learning Fundamentals
Ernest Mwebaze (Google); Ben Akera (AIR Lab, Makerere University)

16:00-16:30

Break

16:30-17:30

Fundamentals of Bioinformatics
Helen Nigussie (AAU), Michael Mayhew (Inflammatix), Dina Machuve (NM-AIST)

Summer School Day 3

Time

Activity

Material

08:30-09:30

Modeling with Gaussian Processes
Michael Mayhew (Inflammatix), Charles Saidu (AUST, Nigeria)

09:30-10:30

Data Science Challenges & Hackathons
Herilalaina Rakotoarison (University of Paris-Saclay)

10:30-11:00

Break

11:00-12:00

Reinforcement Learning
Billy Okal (Voyage)

12:00-13:00

Time Series Analysis
Daniel Mutembesa (AI Research Lab, Makerere University)

13:00-14:00

Lunch

14:00-14:30

Time Series Analysis
Daniel Mutembesa (AI Research Lab, Makerere University)

14:30-15:30

Spatial Analysis
John Quinn (Google AI, Makerere University)

15:30-16:00

Break

16:00-17:00

Data Engineering and Infrastructure
Silas Labedo & Pius Mugagga (UN Global Pulse)

17:00-18:00

DSA Addis Ababa SS feedback and Overview
Organizing Team

Data Science in Africa Workshop
Dates: 06 June - 07 June 2019
Venue: Addis Ababa University, Ethiopia

Call for Registration

The workshop will be organized around paper presentations and interactive panel discussions. We invite participants interested in presenting work at the workshop to submit a short abstract describing the application of data science methods to problems relevant to Africa. Please use the "Submit Abstract" button below. You will be notified whether your work will be presented orally or as a poster. These may include, for example, the following areas:

  • Data Science for the Sustainable Development Goals
  • Healthcare
  • Agriculture
  • Wildlife conservation
  • Disaster response
  • Geospatial modelling
  • Telecommunications data modelling
  • Economic monitoring

During the panel discussions, we will unite a wide range of stakeholders, including data scientists, representatives from government, development practitioners and the private sector; this will provide a unique setting in which innovative solution driven ideas can thrive.

Participants will also develop a framework for attracting young African talent, mentors and researchers from academia, the public sector and the private sector in Africa to engage in activities geared towards harnessing big data and real-time analytics for the public good.

If you indicate interest in being contacted about future Google events and opportunities, information collected in this form may be used in consideration for future job opportunities at Google. Information used in consideration for future job opportunities at Google is subject to the Applicant & Candidate Privacy Policy (http://www.google.com/about/careers/privacy).

Workshop programme outline:

Draft Schedule

Workshop Day 1

Time

Presentation

08:00-09:00

Arrival and Registration

09:00-09:10

Welcome remarks
Program Chair

09:10-09:30

Opening remarks
Addis Ababa University Official

09:30-10:15

Keynote 1 - Predicting Demographics using 50 million Images
Timnit Gebru (Google AI)

10:15-11:00

Keynote 2 - Machine Learning Systems Design: The Three Ds of Machine Learning
Neil Lawrence (Amazon UK & University of Sheffield)

11:00-11:30

Break

11:30-12:00

Talk 1- Data Science to Improve Population Health
Elaine Nsoesie (Boston University)

12:00-12:20

Talk 2-Bioinformatics: Its role for agricultural research and food security in Africa
Helen Nigussie (Addis Ababa University)

12:20-12:40

Talk 3- ZINDI, Data Science Competition Platform in Africa
Celina Lee (Zindi)

12:40-13:10

Keynote 3 - TBC
Damon Civin (ARM)

13:10-14:00

Lunch

14:00-14:30

Keynote 4: Building Stronger Data Ecosystems Building Stronger Data Ecosystems
Victor Ohuruogu (Global Partnership for Sustainable Development Data)

14:30-14:50

Talk 4- TBC
Wondwossen Mulugeta (AAU)

14:50-15:10

Talk 5- TBC
Vukosi Marivate (University of Pretoria)

15:10-15:30

Talk 6- TBC
Melkamu Beyene (AAU)

15:30-16:00

Break

16:00-16:40

Panel 1-Career in Data Science
Instructors from Summer School

16:40-17:00

Day 1 Wrap Up
Billy Okal

Workshop Day 2

Time

Presentation

08:00-09:00

Arrival and Registration

09:00-09:30

Opening Remarks
Dean-College of Natural and Computational Sciences

09:30-10:00

Keynote 5: TBC
Billy Okal (Voyage)

10:00-10:20

Talk 7- Data Science 4 Sustainable Development Goals
Martin Mubangizi (Pulse Lab Kampala)

10:20-10:40

Talk 8: TBC
Dereje Teferi (AAU)

10:40-11:00

Talk 9: TBC
Michael Mayhew (Inflammatix)

11:00-11:30

Break

11:30-12:00

Keynote 6: Mechanism Design for Social Good
Eric Sodomka (Facebook Research, USA)

12:00-12:20

Talk 10: TBC
John Quinn (Google AI, Ghana)

12:20-13:00

Spotlight Talks:
1. Design and Implementation of the SAPRIN Research Data Infrastructure
2. Machine Learning Approach to Enhanced Tax Compliance Risk Management

Speakers:
1. Taurayi Mudzana, MRC-South Africa
2. Phebian and Amos, Kenya Revenue Authority

13:00-14:00

Lunch

14:00-14:30

Keynote 7-Why we need AI in Africa?
Moustapha Cisse (Google AI, Ghana)

14:30-15:30

Spotlight Talks
Various

15:30-16:00

Break

16:00-16:20

Panel 2- Lessons from DSA 2019

16:20-16:30

Closing
Michael Melese

The workshop is now fully subscribed, and registration has closed.

Collaborators

Sponsorship

Gold Sponsors

Silver Sponsors

Bronze Sponsors

Sponsoring Data Science Africa 2019 Event is a great way to communicate your commitment to support the achievement of the sustainable development goals. To become a sponsor, please contact Data Science Africa at info@datascienceafrica.org