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Data Science Africa 2017

Arusha, Tanzania

The last few years have witnessed an explosion in the quantity and variety of data available in Africa, produced either as a by-product of digital services, from sensors or measuring devices, satellites and from many other sources. A number of practical fields have been transformed by the ability to collect large volumes of data: for example, bioinformatics with the development of high throughput sequencing technology capable of measuring gene expression in cells, or agriculture with the widespread availability of high quality remote sensing data. For other data sources – such as mobile phone usage records from telecoms operators, which can be used to measure population movement and economic activity – we are just beginning to understand the practical possibilities.

Data science seeks to exploit advances in machine learning and statistics to make sense of the growing amounts of data available from various sources. In Africa, a number of problems in areas such as healthcare, agriculture, disaster response and wildlife conservation would benefit greatly if domain experts were exposed to data science techniques. These skills would allow practitioners to extract useful information from these abundant sources of raw data

Summer School on Machine Learning and Data Science
Dates: 17 July - 19 July 2017
Venue: Nelson Mandela African Institute of Science and Technology, Tanzania
 

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, IBM Research, Facebook, Pulse Lab Kampala and the AI and Data Science (AIR) lab-Makerere University.

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 are encouraged to have some background in programming particularly programming with Python.

The summer school is now fully subscribed, and registration has closed.

School programme outline:

Draft Lecture Schedule

Stuff to install..

To ensure we hit the ground running, it is essential you install the prerequiste software and test it out and make sure it is working on your computer. The venue for the summer school will have some computers on which the software will have been installed but you are advised to come with your own laptop with the software installed.

Luckily all the software required has already been prepackaged in a bundle called Anaconda. You can download the various versions of the software for your laptop OS and architecture from the Anaconda website .

Stuff to do..

To ensure that the software is working fine on your machine and to get you up and running, download the following jupyter notebook and do the exercises in there. If you have not used jupyter notebook before, make friends with Google - a friend in need …

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.

Overview

The first day of the data science school will introduce the jupyter notebook and overview the use of python for analyzing data. We will introduce the machine learning technique of classification and perform lab practicals exploring these techniques.

Time

Activity

Material

08:00-08:30

Arrival and Registration

08:30-09:00

Opening Remarks

09:00-10:30

Lecture 1: Introduction to Machine Learning

10:30-11:00

Break

11:00-12:30

Lecture 2: Introduction to Jupyter and Python

12:30-13:30

Lunch

13:30-15:00

Practical Session 1

15:00-15:30

Break

15:30-17:00

Lecture 3: Introduction to Classification

17:00-18:00

Practical Session 2

Overview

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

Time

Activity

Material

09:00-10:30

Lecture 4: Introduction to data science applications in health / Introduction to IoT session I
Athanasios Anastasiou (Swansea) / Jan Jongboom (ARM)

10:30-11:00

Break

11:00-12:30

Practical Session 3 (Health Data Science / IoT)

12:30-13:30

Lunch

13:30-15:00

Lecture 5: Data Visualisation
Athanasios Anastasiou (Swansea)

15:00-15:30

Break

15:30-17:00

Practical Session 4

17:00-18:00

Lecture 6: Introduction to IoT session II / Introduction to Reinforcment learning
Jan Jongboom (ARM)

Overview

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

Time

Activity

Material

09:00-10:30

Lecture 7: Bayesian Methods

10:30-11:00

Break

11:00-12:30

Practical Session 5 / Building Sensor Systems for Data Collection

12:30-13:30

Lunch

13:30-15:00

Lecture 8: Introduction to Deep learning
Andreas Damianou (Amazon)

15:00-15:30

Break

15:30-17:00

Practical Session 6 / Building Sensor Systems for Data Collection

17:00-18:00

Panel Discussion and Wrap Up

Data Science in Africa Workshop
Dates: 20 July - 21 July 2017
Venue: Nelson Mandela African Institute of Science and Technology, Tanzania
General Chair: Dina Machuve dina.machuve@nm-aist.ac.tz
Program Chair: Ciira Maina ciira.maina@dkut.ac.ke

Important Dates

26 May 2017: Abstract Submission Deadline

26 June 2017: Closing Date for Registration

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. 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.

Workshop programme outline:

Draft Schedule

Time

Presentation

09:00-09:45

Keynote 1

09:45-10:30

Keynote 2

10:30-10:30

Break

11:00-12:30

Data Science for Agriculture

12:30-13:30

Lunch

13:30-15:00

Data Science for Sustainable Cities

15:00-15:30

Break

15:30-17:00

Data Collection and Curation

17:00-17:30

Keynote 3

17:30-18:00

Networking

Time

Presentation

09:00-09:45

Keynote 4

09:45-10:30

Keynote 5

10:30-11:00

Break

11:00-12:30

Data Science for Health

12:30-13:30

Lunch

13:30-15:00

Data Science for Poverty Alleviation and the Environment

15:00-15:30


15:30-17:00

Data Science Training and Mentorship in Africa

17:00-17:30

Networking

17:30-18:00

Keynote 6

Collaborators

Sponsorship

Gold Sponsors

Silver Sponsors

Sponsoring Data Science Africa 2017 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