by Damon Civin, Principal Data Scientist at Arm
Conversation in Tanzania often starts with the phrase "Karibu Sana," which translates to "you are welcome." In many other places, you'd have to say "thank you" ("Asante sana") before being told you're welcome. The Data Science Africa 2017 conference (DSA2017) was full of welcome surprises and unique features.
The first welcome surprise was being able to retrieve Jan's lost box of Mbed boards and sensors. No government officials necessary, just one Jan on a mission, making his way into the arrivals part of the airport and grabbing his box.
In many more important ways, the Data Science Africa conference is a welcome change to the conferences I am used to. Here's why.
(Slides and notebooks from the talks can be found here, videos here)
Day 1 kicked off with introductory sessions on data science. Neil Lawrence (Amazon) gave a characteristically inspiring overview of the field, followed by introductions to Jupyter notebook and the art and science of classification by Ernest Mwebaze (Makerere University) and Martin Mubangizi (UN Global Pulse). The students (many of whom had spent upward of 10 hours on a bus overnight the night before) accelerated through the practical sessions, and many were coding and experimenting well into the evening.
Day 2 was Arm's time to shine with the IoT. Jan did an amazing job of getting the tutorial ready (in the pub) and building a dance routine into it...
and the students loved it!
Here are some of the students explaining their work themselves:
The willingness to collaborate, learn and experiment was infectious and led to more coding into the night.
John Quinn (UN Global Pulse) started the third day with a fascinating introduction to spatial data analysis using QGIS and GPy. This isn't something usually taught in data science courses, though it is powerful and useful! His tutorial explained the concepts through relevant, practical applications, such as eregional vegetation health, disease outbreaks, air quality and commute times.
His quote above was also my favourite of the conference - all data science practitioners should take it to heart. Even Ralf Herbrich, who runs machine learning at Amazon, remarked:
"One of the best things about #dsa2017 is customer focus in the form of putting the problems and use cases first and tech as a tool."
Ralf went on to give an insightful talk on uses of machine learning at Amazon. A few highlights for this community:
He explained further that power efficient GPUs will become necessary to sustain this business model as the industry tackles more predictions and more complex problems. One reason this is interesting is because neural networks are used for translation, and the quality of product descriptions contributes directly to profit for online retailers. Also, AI is better than humans at predicting strawberry freshness now.
Moustapha Cisse of Facebook presented new work on the frontiers of machine learning, especially learning from less data, and how to fool neural networks. Also, after his talk, I may well be a PyTorch convert. It's so similar to NumPy that moving to large scale on GPU is much easier if you are used to small data situations.
The talks at the workshop focused on solving real-world problems through data science.
Morris Agaba (NMAIST) presented work on biodiversity and isolating genetic causes for mutations.
Daniel Mutembesa and Ernest Mwebaze (Makerere University) spoke about using mobile apps to gather agricultural data to monitor and classify plant pests and diseases. They are finishing the pilot program and looking to scale it. Though the mobile data networks in East Africa are strong, the phones available to most people are feature phones, not smart phones, which makes app development significantly more difficult. The rate of adoption of smart phones will be a major factor in the pace of field-based data science innovation.
Dina Machuve (NMAIST, organiser of DSA2017 and all-round hero) presented some upcoming work on predicting banana diseases from weather data and providing the insights to framers through mobile apps. Check out this interview with her!
Here is the talk I gave about using Arm Mbed for data science. The rest of the talks mentioned above (and more) are on the playlist, too.
As a nerdy screentanned math/computer guy, it's rare to get the opportunity to go into the field and do some actual science. This is the first conference I've been to where the attendees used what they had learned by doing some fieldwork. Jan has written a blog about it that you can read here.
Strong mobile internet, developing cities opportunity for IoT
Here is the plan for next year, as written by Ciira wa Maina, program chair of Data Science Africa:
"Next year, there will be two events: DSA 2018 Nyeri in Kenya and DSA 2018 Abuja in Nigeria representing our goal to consolidate the regional community in East Africa while also expanding to other regions. DSA 2018 Nyeri will focus on training approximately 20 participants who will then be able to serve as trainers in their local contexts using an “end-to-end” approach.
We would like to expose data science trainers to this entire pipeline of data collection, analysis, and communication of results using relevant examples from agriculture and conservation. This is motivated by the successful trial of internet of things training sponsored by ARM mbed at the recently concluded DSA 2017 in Arusha Tanzania. We plan to have participants deploy sensor systems at the DeKUT conservancy and farm which will collect data that they will then analyse. This represents a departure from the previous DSA events where techniques were taught with reference to existing data sets. Here the participants will engage in data collection as well. We believe that this will give them deeper insight into the practise of data science."
It's great to see the continued growth of Data Science Africa, and I'm pleased that Arm has been part of enabling it. Ciira wa Maina and Dina Machuve did an awesome job of planning and running this conference, and I'm grateful to them for giving us the opportunity to participate.
Damon leads a Data Science team at Arm, the chip design company that is shaping the smart connected world. The team is based in Cambridge, UK and San Jose, USA and works globally, across all parts of the company, from commercial operations to hardware engineering and IoT services. Damon has an MSc in Applied Mathematics from the University of Pretoria and a PhD in Mathematical Analysis from the University of Cambridge. After graduating, he worked as a machine-learning developer at a cyber-security startup in Cambridge UK before joinng Arm in 2015. Find him on Twitter, Medium, LinkedIn and the Arm community.