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, 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 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.

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.

Anaconda

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. Please download the Python 3.6 version. Instructions on how to install are next to the download links on 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 (right click and ‘save as’) and do the exercises in there. To access it you’ll need to run a jupyter notebook (instructions).

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 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
Neil Lawrence - Amazon and University of Sheffield

slides

10:30-11:00

Break

11:00-12:30

Lecture 2: Introduction to Jupyter and Python
Ernest Mwebaze - Makerere

ipynb

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
Martin Mubangizi - UN Global Pulse

ipynb

17:00-18:00

Practical Session 2

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.

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)

slides ipynb

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)

slides

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

Time

Activity

Material

09:00-10:00

Lecture 7: Spatial Data Analysis
John Quinn - UN Global Pulse

ipynb

10:00-10:30

Lecture 8: Machine Learning at Amazon
Ralf Herbrich - Amazon

10:30-11:00

Break

11:00-12:30

Practical Session 5 / Building Sensor Systems for Data Collection
John Quinn - UN Global Pulse / Jan Jongboom (ARM)

12:30-13:30

Lunch

13:30-15:00

Lecture 9: Introduction to Deep learning
Andreas Damianou (Amazon)

slides ipynb

15:00-15:30

Break

15:30-17:00

Practical Session 6: Deep learning with Pytorch / Building Sensor Systems for Data Collection
Moustapha Cisse - Facebook / Jan Jongboom (ARM)

ipynb

17:00-18:00

Panel Discussion and Wrap Up

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

Data Science in Africa Workshop
Dates: 20 July - 21 July 2017
Venue: Nelson Mandela African Institute of Science and Technology, Tanzania

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

Workshop Day 1

Time

Presentation

08:00-09:00

Arrival and Registration

09:00-09:30

Workshop Opening

09:30-10:00

Keynote 1: Who are health data scientists
Athanasios Anastasiou - Swansea University

10:00-10:20

Using spatial features of human settlement to predict epidemic properties
Rahman Sanya - Makerere University

slides

10:20-10:40

Understanding maternal health service utilization
Stephen Kibusi - University of Dodoma

10:40-11:10

Coffee Break

11:10-11:30

Machine learning for targeted communication in emergency
Rita Zagoni - Africa's Voices Foundation

slides

11:30-11:40

Crowd sourcing ‘Big’ clinical data from small health facilities
Moses Thiga - Kabarak University

slides

11:40-11:50

Data Revolution: A fitting Model for Development countries
Zaituni Kaijage

slides

11:50-12:00

Enabling Data Revolution
Charles Bundu - DLab

slides

12:00-12:10

How Data Science is solving life-threatening problems in Africa plus the way forward
Opetunde Adepoju - Ladoke Akintola University of Technology

12:10-13:00

Health Data Science Panel

13:00-14:00

Lunch Break

14:00-14:30

Keynote 2: Understanding Africa's Wildlife Heritage Through the lens of Genome Data
Morris Agaba - Sarissa Limited

slides

14:30-15:00

Keynote 3: Habari Node's Experience creating a Datacenter and Cloud Services Infrastructure
Erik Rowberg - Habari Node Limited

slides

15:00-15:20

Mining voter sentiments from Twitter data for the 2016 Uganda Presidential elections
Isaac Mukonyezi - Uganda Technology and Management University

15:20-15:40

Using Social Media for Public Safety Monitoring
Vukosi Marivate - CSIR (South Africa)

slides

15:40-16:00

Algorithmic opportunities in revealing trends of food crisis from news online articles
Andrew Lukyamuzi - Mbarara University of Science and Technology

16:00-16:20

Mobile Phone Data for Disasters Management
David Pastor - itdUPM

16:20-17:00

Panel Discussion - Mining Social Networks
Ralf Herbrich - Amazon

Workshop Day 2

Time

Presentation

09:00-09:30

Keynote 4: IoT data and insights for everyone
Damon Civin - ARM

09:30-10:00

Keynote 5: Addressing challenges through geospatial modelling in Kenya
Charles Mundia - Dedan Kimathi University of Technology

slides

10:00-10:20

KAZNET: Leveraging digital and crowdsourcing technology for livestock market data collection
Munenobu Ikegami - International Livestock Research Institute

slides

10:20-10:40

Sensing with Farmers; crowdsourced adhoc crop surveillance
Daniel Mutembesa - AI Research labs, Makerere University

10:40-11:00

A time series review of forest production and trade trends across the tropical region
Fridah Nyakundi - International Center for Tropical Agriculture - CIAT

11:00-11:30

Coffee Break

11:30-11:40

Convolutional Neural Network for Appliance Recognition in Energy Disaggregation
Anthony Faustine - Nelson Mandela African Institute of Science and Technology

slides

11:40-11:50

Images - the all important developing world data format
Ernest Mwebaze - Makerere University

slides

11:50-12:00

Modeling Wireless Sensor Network For Forest Temperature and Relative Humidity Monitoring in Usambara Mountains - A review
Ramadhani Sinde - Nelson Mandela African Institute of Science and Technology

slides

12:00-12:10

A Weather Forecasting Model for Farmers in Arusha
Dina Machuve - Nelson Mandela African Institute of Science and Technology

slides

12:10-12:20

Jaguza Livestock App
Ronald Katamba

12:20-12:30

Air quality monitoring in Uganda
Mike Smith - University of Sheffield

slides

12:30-12:40

Bank At Hause – Factor Xchange
Nsibambi Kyabainze - Makerere University

slides

12:40-14:00

Lunch Break

14:00-14:30

Keynote 6:
TBA

14:30-14:50

Monitoring economic indicators in Sub-Saharan Africa
Reuben Cummmings - Nerevu Development

14:50-15:10

Price predication for the agricultural commodities.
Sangappa Biradar - SDM College of Engineering and Technology

15:10-15:30

Prediction Modelling of Academic Performance, a Data Mining Approach
Mvurya Mgala - Technical University of Mombasa

slides

15:30-15:40

Challenges facing data management for community based education and services programs
Joice Baliddawa - Moi University College of Health Sciences

15:40-15:50

Radio mining and rapid-deployment speech technology for humanitarian early warning in Uganda
John Quinn - UN Global Pulse

16:20-17:30

Panel Discussion - Opportunities for Collaboration around Africa

Fieldwork Day 1

Time

Presentation

10:00-12:30

Cow Tracking
Damon Civin - ARM

ipynb

12:30-15:00

Chicken Coop
Damon Civin - ARM

ipynb

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

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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