Data Science for the Internet of Things (IoT)
Applications now being accepted.
This unique course aims to create a new breed of engineer, through a solid grounding in IoT and artificial intelligence (AI) synthesised with a practical knowledge of machine learning, cloud and robotics.
The Data Science for IoT course is designed as a general Artificial Intelligence (AI) course with an emphasis on the Internet of Things (IoT. The course aims to equip you with the skills to solve problems, providing you with a toolkit (code) and templates. It's for developers who want to be data scientists with a focus on AI and IoT.
The course explores problem solving for IoT analytics via the following themes:
- Concepts: principles and foundations for AI and IoT
- An AI-based approach with IoT as a vertical
- Deep Learning
- Reinforcement learning
- Unsupervised learning, GANs and Autoencoders
- End-to-end problem solving methodology including continuous improvement and delivery for AI models
- Cloud based AI implementations - Azure and Amazon
- TensorFlow, Keras and TensorFlow embedded
- Autonomous vehicles
- Time series
- Programming sprints
- Skills in platforms like AWS IoT, Azure IoT, TensorFlow
Python (TensorFlow and Keras) is the primary language of the course - but we do not expect you to have full proficiency in it. However, we expect you to have a programming background.
- A problem solving methodology – putting it all together
About the course and its aims:
- The course analyses problem solving for IoT analytics.
- The unique considerations for IoT data (e.g. time series data) are investigated.
- The course covers programming so participants will need to be familiar with some programming languages - but we do not expect familiarity in a specific language. The primary programming language of the course is Python (specifically TensorFlow and Keras).
- Development is based on coding sprints
- The course needs an understanding of maths. We cover maths and statistics foundations as needed.
- Where possible, we use IoT datasets. We cover handling large-scale IoT datasets.
- We focus on skills based/commercial products. This is not an academic course.
- The course also includes an industry programme. The industry programme will be based on use cases incorporating IoT analytics methodology.
- We aim to equip you with skills such as TensorFlow, Keras and AI in general, which can be used outside of IoT applications.
The course takes a problem solving approach and uses specific case studies from industry. Participants are expected to have a mind-set of exploration and to study and learn beyond the class material itself (depending on their existing familiarity with the subject matter).
The course is based on a perspective of both AI and machine learning. AI is driven by deep learning algorithms. Deep learning is a wider case of machine learning based on automatic feature detection. IoT primarily involves data in time series formats (using AI algorithms like recurrent neural networks and long short-term memory (LSTMs)) and image-based data (using convolutional neural networks).
A limited number of participants ensures that all those taking this course gain the maximum possible value.
Previous students have used the course to start a new career, for career progression or to have their skills upgraded by their employer.
All participants finishing the Data Science for IoT course will receive a University of Oxford certificate showing that they have completed the course (see sample and details of requirements further down this page).
You will be fully supported by the tutor who will be available during the week to answer questions. The tutor list below may be subject to change.
The tutor will also offer a number of one-to-one 'surgery sessions' during the course.
All course participants will receive e-book versions of the relevant textbooks as part of the course fee.
The time commitment for the course is 3 - 5 hours face-to-face in Oxford on Saturdays (usually starting at 10:30) and 1 - 2 hours online each week on Tuesdays (usually starting at 19:00). We recommend you allow around 10 - 12 hours study time per week (plus the hours above). There is a minimum attendance requirement of 75%.
Foundations of Data Science, AI and IoT: Part 1
(Tutor: Ajit Jaokar, Course Director)
Python, Tensorflow and Keras for Data Science
(Tutors: Peter Marriott, Director and Technical Consultant, Catalyst Computing Services; Ajit Jaokar, Course Director)
Introduction to the AI sprint
(Tutors: Cheuk Ting Ho, Data Scientist; Barend Botha)
Foundations of Data Science, AI and IoT: Part 2
(Tutor: Ajit Jaokar, Course Director)
Retail case study – and exercise
(Tutor: Devrim Sonmez, Co-founder, Blesh)
Unsupervised learning, Representation learning, and Generative Adversarial Networks
(Tutor: Hamaad Shah, Analytics Expert)
(Tutor: Intel, TBC)
Implementation of AI, IoT and Edge – Azure AI
(Tutor: Paul Foster, Senior Software Engineer, Microsoft)
Inudstry Use Cases
(Tutor: Paul Clarke, Chief Technology Officer, Ocado)
Implementation of AI, IoT and Edge – Amazon AI
(Tutor: Claudiu Pasa, Business Development Lead, Amazon Web Services Internet of Things (EMEA))
CHRISTMAS BREAK (22 December 2018 to 4 January 2019)
AI with Robotics
(Tutor: Ajit Jaokar, Course Director)
Google and AI
Time series – univariate, multivariate and LSTMs
(Tutor: Jean Jacques Bernard, Director, Insight and Customer Strategy, Oracle)
Deep learning with Nvidia Jetson
(Tutor: Bojan Komazec, Senior Software Engineer)
Self-driving cars and affective computing
(Tutors: Parham Vasaiely, Senior Manager, Automated Driving, Jaguar Land Rover; Ajit Jaokar, Course Director)
Note that tutors and content may be subject to revision during the course and the course development process.
Vibhu Gautam studied the Data Science for IoT course in 2017 and now works as a Data Scientist at Micron Memory Japan – read his story here.
The textbooks below are included as part of the course fee:
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow (Sebastian Raschka, Vahid Mirjalili / Packt Publishing, 2nd edition, 2017)
- Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python (Manohar Swamynathan / 1st edition, Apress, 2017)
Further textbooks to be added. Note that you will receive some of these books in digital format.
Participants who satisfy the course requirements will receive a Certificate of Attendance. The sample shown is an illustration only and the wording will reflect the course and dates attended.
To receive a certificate at the end of the course you will need to:
- Achieve a minimum attendance at the Oxford classroom sessions of 75%.
- Answer all the weekly learning quizzes (these are short quizzes designed to ensure you have understood the material in each unit).
- Complete the short exercises that you are given.
Standard course fee: £4995.00
Fees include all course materials and tuition. The price does not include accommodation.
All courses are VAT exempt.
Payment can be made in instalments, if your application is made by 1 September 2018:
Instalment 1: £2,550.00, payment due within 30 days of invoice
Instalment 2: £2,550.00, payment due within 60 days of invoice
Total fee if paid in two instalments: £5,100.00
Total fee if paid in one instalment: £4,995.00
Lead Tutor and Course Director
Ajit's work spans research, entrepreneurship and academia relating to artificial intelligence (AI), the internet of things (IoT), predictive analytics and mobility.
Ajit currently works as a Data Scientist currently in the Bioinformatics space. The technical emphasis of his work includes Time series, Edge Computing and Deep Learning for sequences.
Outside of Oxford University, he has been involved in teaching /academic programs at London School of Economics, Harvard Kennedy School, University of Madrid.
Ajit publishes extensively on KDnuggets and Data Science Central and his book, Data Science for Internet of Things, is included as a course book at Stanford University.
He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), No 19 among top 50 twitter IoT influencers (IoT Institute).
Ajit has been involved with various mobile, telecoms and IoT projects since 1999 including strategic analysis, development, research, consultancy and project management.
In 2009, he was nominated to the World Economic Forum’s ‘Future of the Internet’ council. In 2016 he was involved in a WEF council for systemic risk (IoT, drones etc.). He has worked with cities like Amsterdam and Liverpool on Smart City projects in mayoral level advisory roles. Ajit has been involved in IoT-based roles for the webinos project (Fp7 project). In May 2005 he founded the OpenGardens blog, which is widely respected in the industry. He has spoken at Mobile World Congress (4 times), CTIA, CEBIT, Web 2.0 expo, The European Parliament, Stanford University, MIT Sloan, Fraunhofer FOKUS; University of St Gallen. He has also been involved in transatlantic technology policy discussions.
Ajit is also passionate about teaching data science to young people through space exploration working with Ardusat.
Director of Studies
Dr Cezar Ionescu is Associate Professor of Data Science with the Oxford University Department for Continuing Education. His main interests include functional programming, correctness of scientific computing and machine learning algorithms, and the role of computing science in education.
Guest Speaker and Course Developer
Director & Technical Consultant, Catalyst Computing Services
Peter Marriott is an industry practitioner with over 25 years database and software development experience on production systems. The main focus of his career has been working with data, working on business systems to make the data useful and available in a performant cost-effective way.
13 years ago he founded the consultancy Catalyst Computing Services, working with a wide range of clients in a variety of market sectors. Their first IoT project was in 2006. Peter has been involved working throughout the whole project development cycle of IoT: from prototyping proof of concepts; designing systems architecture; development; deployment and training of support staff.
Peter runs training courses in cloud computing and database technologies for clients, gives career talks for undergraduates and speaks at various IT user groups.
Guest Speaker and Course Developer
JJ Bernard has an MSc in Engineering from the Ecole Centrale de Marseille and an MBA from the University of Cambridge. He is also a certified Lean Six Sigma Black Belt.
He currently works at Oracle as a strategy consultant, advising client on their technology investment with regards to their business strategy.
Previously, he has worked as a project manager for Orphoz, a subsidiary of McKinsey & Company, focusing on defining and executing transformation projects. He has worked in diverse environments such as manufacturing, FMCG, public sector, steelmaking, mining, healthcare and automative.
He has also worked for Accenture, both on business and IT transformation projects, for the financial services industry and the public sector.
Business Development Lead, Amazon Web Services Internet of Things (EMEA)
Claudiu is a senior professional with 20 years of experience in strategy, business analytics, data-driven decision systems and large-scale technology transformations. He is currently the Business Development Lead for Amazon Web Services Internet of Things in Europe, Middle East and Africa.
He has significant experience in Internet of Things/ machine to machine technology and digital transformation using could computing platforms, from digital customer experience and digital processes to entire new lean enterprise business models.
Claudiu has a degree in Economics, an Executive MBA and a Master in Computer Science with focus on data analytics and expert systems/ artificial intelligence.
Chief Technology Officer, Ocado
Paul Clarke is Chief Technology Officer at Ocado, the world's largest online-only grocery retailer.
Paul joined Ocado in 2006. After establishing new teams for Simulation and Mobile development, Paul then co-wrote the first of Ocado’s award winning mobile apps. In his current role, Paul heads up Ocado Technology, whose 950+ software engineers and other IT specialists are responsible for building all the software and IT infrastructure that powers Ocado, and now Morrisons’ online grocery business too.
Paul read Physics at St John's College, Oxford before then entering the computer industry. He has worked in software engineering, consultancy, interim management and a number of software start-ups.
In what little spare time he has alongside his work and family, Paul loves to invent and build stuff, design PCBs, write software and generally tinker.
Co-founder of Blesh
Devrim Sonmez is regarded as a high performing executive and entrepreneur with over 19 years of experience at intersection of digital and technology with various industries, over 10 years of Executive Board experience and taking a strategic role in securing M&A deals worth over $170 million in technology.
In 2013, he became the CEO and co-founder of Blesh Inc. which is an award winning and innovative start up in IoT space developing IoT platforms and solutions through its own research and development. It is among the top Global 5 Mobile Proximity Solution providers and is considered the first global beacon solution provider of Google Physical Web technology since 2014 with exports more than 30 countries.
He is still the Cofounder and the Board Member of Blesh. Alongside these positions, Mr. Sonmez has sat on the board of several more companies and associations including IOT.ATL a program of US Metro Atlanta Chamber that intends to boost the region’s tech sector in the burgeoning industry of Internet of Things products and software, the highly prestigious TUBISAD (Turkish Informatics Industrialists Association) and English Ninjas (An innovative mobile startup for English practicing), where his influence plays an integral part in grow and success.
Devrim holds a Mathematics Bachelor of Science from the leading Turkish university in Ankara, the Middle Eastern Technical University graduating in 1998 and a Master of Business Administration from the Koc University in Istanbul, graduating in 2005.
Senior Software Engineer, Microsoft
Since joining Microsoft in 1994, Paul Foster has worked across a wide range of sectors and customers, providing a mix of technical and strategic guidance around the creative use of technology in relation to their business needs.
Paul is currently a principal software engineer at Microsoft. As an established public speaker across Europe and having spent a considerable amount of time working on the cutting edge of technology providing leadership and inspiration on topics like Smart Devices, Cloud Computing, Education and App Development.
Paul is currently focusing on the building of next generation sensor webs which automate the gathering of data from disparate sources, and how to enable the creative analysis of this data to start a new era of perception.
For a short time Paul was a member of a high-wire flying trapeze circus troupe, is a keen roboticist and an international marathon runner.
Vision Design Engineer, Programmable Solutions Group, Intel
Jahanzeb Ahmad joined Intel Programmable Solutions Group (PSG) formally known as Altera in 2013 and is currently working as vision design engineer. His current responsibilities include architecting and developing machine learning architectures and solutions for autonomous vehicles and surveillance.
Previously, Jahanzeb spent 2 years working as a consultant/contractor developing FPGA based video compression/decompression, conversion and capturing systems.
Prior to joining PSG, Jahanzeb did his PhD in computer vision and medical engineering from University of The West of England, Bristol, UK. Jahanzeb also holds a BEng in Computer Engineering and a Masters in Communications Engineering.
Imperial College London; Organiser, Google Developer Group (GDG) Cloud London
Surya is pursuing an MEng in EEE from Imperial College London, specialising in Machine Learning and Embedded Systems. He started his journey in data science at a biotechnology startup, where he worked on characterising hand tremors of Parkinson’s patients to tune the product’s control algorithm (which stabilises the tremors). He spent last summer working on implementing neural network based applications on Android.
He also leads workshops and events at the Cloud Google Developer Group in London, with a specific focus on Machine Learning frameworks, such as Tensorflow.
Surya can usually be found tinkering with embedded hardware and software in the Robotics Lab at Imperial College.
Barend Botha is a UK-based consultant working predominantly in the field of data visualisation.
He is particularly interested in the growing future potential and roles that data analytics and visualisation will play across IoT verticals in in combination with Artificial Intelligence and the resulting products and insights for business and consumers alike.
He draws upon past experience in research, design, development, management and marketing across various domains and disciplines.
Senior Software Engineer, Avast Software
Bojan Komazec has been working in the IT industry for over 13 years. He currently holds the position of Senior Software Engineer in Avast Software where he has been developing various security and privacy products.
Bojan's interests span from code craftsmanship and cyber security to Artificial Intelligence and Internet of Things. He is an active blogger and speaker at several IT Meetup groups where he enjoys sharing experience and knowledge.
Bojan studied Electrical Engineering and Telecommunications and in 2004 received master's degree from University of Novi Sad, Serbia.
Technical Evangelist for Artificial Intelligence and the Internet of Things, Intel
Roy Allela is a Technical Evangelist for Artificial Intelligence and the Internet of Things at Intel. He is also an Intel Innovator and he has a strong passion for the application of nascent technologies to solving everyday problems.
He is a Royal Academy of Engineering 2018 LIF Fellow and he emerged the Global Winner of the 2017 Edition of American Society of Mechanical Engineers ISHOW competition.
He has a background in Microprocessor Technology and Instrumentation from The University of Nairobi.
Hamaad is a principal data scientist with expertise and experience in machine learning and quantitative analytics applied to banking and insurance. He has extensive expertise in deep learning and Bayesian inference, among other areas of machine learning, applied to various financial services use cases such as Asset Liability Management (ALM) and actuarial pricing, among other financial services use cases. Hamaad holds an MSc in Applicable Mathematics from the London School of Economics and Political Science (LSE) and a BSc in Economics from the University of Manchester.
Please complete the self-assessment pre-course questionnaire. The information requested is to allow us to get a fuller picture of your skills and background.
We do not expect you to have knowledge or experience in all of the areas on this questionnaire!
Upon approval from the academic review panel, we'll send you an application form by email.
Note that this process could take up to two weeks.
Complete the application form, and send it to us by post or email. Please do not send credit card details by email.
We'll invoice you for the course fees.
Submit payment. Your place on the course will be confirmed only after we have received payment.
Terms and conditions
Terms and conditions for applicants and students on this course
Sources of funding
Information on financial support
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