This hands-on two-day tutor-led course covers one of the most exciting and current topics within the R community. Although traditionally R has not been used for Big Data analytics due to various limitations, recent R packages have provided much-needed connectivity for out-of-memory processing with popular Big Data tools such as Hadoop, Spark, SQL and NoSQL databases etc.
During this training course, you will learn essential know-how on applications of R language to manage, manipulate and analyse Big Data, datasets stored in distributed file systems or large databases, and to write fast, parallel R code to allow scalability of algorithms and data processing. The course also serves as a good introduction to Cloud Computing (Amazon Web Services and Microsoft Azure) and MLOps as you will be presented with the best practices of the Big Data system design which utilises the growing ecosystem of tools that support Big Data analytics including software and engines applicable to large scale statistical and machine learning (h2o, Spark, keras, and tensorflow).
This course is based on the “Big Data Analytics with R” book (with recent edits) authored by the course tutor. You will receive full access to the ebook version of this book before the course. Additionally, all activities (tutorials and exercises) of the course will be performed on the Mind Project Big Data virtual machines and computing clusters which consist of both CPU and GPU-accelerated multi-node Hadoop DFS with Spark engines and Hive databases, separate databases (both SQL and NoSQL including scalable and distributed MongoDB, HBase and CassandraDB) as well as all necessary R packages (e.g. Spark, h2o, keras, tensorflow, data.table, tidyverse etc.) pre-installed for your convenience to allow seamless connectivity of R with various Big Data tools and processing/analytical engines.
This is a 2-day instructor-led online training course with a week-long follow up period. The course will run from 10:00 in the morning to ~17:00 each day and will include a 50-minute break for lunch between morning and afternoon sessions and two 15-minute coffee/tea breaks. Following the course, you will be able to submit your solutions to the homework exercises and you will receive feedback from the tutor.
This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor after the course if you have any questions related to the material presented during the course.
The course will be recorded – you will have access to the video recordings of the course webinars and additional resources such as datasets, R code, academic papers related to the topics of the workshop, and supplementary exercises via Mind Project Learning Platform.
Course dates: Monday-Tuesday, 4th-5th of July 2022, 10:00-17:00 London (UK) time
Deadline for registrations: Friday, 1st of July 2022 @ 17:00 London (UK) time
The programme for this course covers the following concepts and topics:
Use third-party R packages, which support parallel computing in order to increase the speed and processing capabilities of R with both CPUs and GPUs,
Work on large data sets in the Cloud (Microsoft Azure and Amazon EC2) through R deployed on the server,
Implement MapReduce framework through Hadoop straight from R console,
Manage Hadoop Distributed File System, HBase and Hive databases through R,
Connect to and extract, aggregate and manage the data in major relational SQL-based database management systems (RDBMSs) using a variety of R packages,
Apply NoSQL queries to access, transform and manipulate large data sets in MongoDB using R packages,
Improve the data flow and speed of data processing as well as implementation of AI models for large data sets through R’s connectivity with Spark and h2o packages,
- Learn how to design Big Data systems to support scalable and distributed machine learning and AI (with Spark, h2o, keras and tensorflow packages),
Implement selected Big Data tools in the Big Data Product Cycle with R.
Course pre-requisites and further instructions
- You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our computing clusters and the Mind Project Learning Platform (with additional resources). There is no need for you to install any specific R packages as our clusters and virtual machines will be set-up for your convenience.
We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. We suggest that the course is preceded with our “Applied Data Science with R” open-to-public training course.
Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
The primary spoken and written language of the course is English.
By enrolling on one of our online training courses you agree to the Training Terms and Conditions. Please read the Training Terms and Conditions before purchasing this course. Please note we reserve the right to cancel the course in case the number of registered attendees on the course is less than 5 individuals one week before its scheduled start date.
Your course instructor
Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses (in R, Python, SQL and Scala for Spark languages) in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse, ING etc.), research and academia (GSMA, CERN, University of Cambridge, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), insurance (Liberty IT), transport (Steer Group) and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).
Discounts and multiple bookings
We offer 2 types of enrolment options:
- Regular Fee – full-priced enrolment for learners representing commercial organisations or self-funded individuals who do not meet our eligibility criteria for discounted rates (please see below),
- Discounted Fee – applicable to undergraduate and postgraduate students as well as representatives of registered charitable organisations and non-governmental organisations (NGOs) – this category also includes employees of the National Health Service (NHS).
Students and individuals eligible for the Discounted Fee should submit a copy of their student or organisation ID card (with their name and card expiry date visible) when making the purchase of their place on the course for the discount eligibility verification purposes. Alternatively, the discount eligibility can be verified by submitting either i.) a copy of a letter from the university registrar or student’s department confirming your status, or ii.) a copy of a letter from your employer (on a company letter-headed paper with a charity/NGO registration number) which confirms your current position within the organisation.
Apart from the discounted fees for students or employees of charitable organisations and NGOs, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.
- If you book 3 or 4 tickets on any of our tutor-led open-to-public online training courses, you will receive 5% discount on the total price of your booking.
- If you book 5 or more tickets on any of our tutor-led open-to-public online training courses, you will receive 10% discount on the total price of your booking.
All discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for your delegates at a discounted rate.
Arrange this course at your organisation
If your delegates cannot attend this public course, or you are interested in arranging this training course explicitly for your delegates (or at your premises) or simply you need a bespoke, made-to-measure training solution, please request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.
You may email us directly at info(at)mindproject.io and include the following information in your enquiry:
contact details to a person who should receive the quote,
number of delegates you would like to train,
approximate number of online sessions (or half-days / full days for on-site in-house course) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue if not online,
any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,
any other questions you may have.
If you don’t know the answers to questions above or you are at early stages of course planning, we would be happy to arrange an informal chat and help you choose the most suitable and budget-efficient option.