- Telling Human Stories With Data
We want to invite you to participate in the FREE ODSC Webinar! Date: September 4th Time: 11 am - 12 pm BST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/5174684751357268493 Robust data analysis underpins every business decision, public sector project and non-profit initiative. But data in its raw form often fails to convince crucial lay audiences – either due to its complexity, or due to suspicion and mistrust. And you can’t help guide the world in the right direction if you alienate key decision-makers or the public. This talk, delivered by journalist and data visualization specialist Alan Rutter, will cover an audience-centered approach to visualizing data. It will introduce tried-and-tested techniques for communicating data-driven stories effectively to people from a broad range of backgrounds, and deal with some of the common problems that practitioners encounter. Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Time Out,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/
- Improving Reliability of High-Consequence Systems using Data Sharing&Integration
WeWork One St Peter's Square
Speaker: Dr. Ernest Edifor, Senior Lecturer at Manchester Metropolitan University https://www2.mmu.ac.uk/business-school/about-us/our-staff/otehm/profile/index.php?id=2623 Topic: Improving the Reliability of High-Consequence Systems using Data Sharing and Integration Schedule: 6:00pm - 6:30pm - ODSC&WeWork Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Dr. Ernest Edem Edifor is a senior lecturer at the Manchester Metropolitan University. In 2014, he completed a Ph.D. on the probabilistic analysis of dynamic safety-critical systems using temporal fault trees. He has been involved in the development of various bespoke websites and databases for running clinical trials across Europe. He has taught extensively on the subject of data management in Masters education. His current research interests are in reliability engineering, augmented reality risk assessment, supply chain risk assessment and Internet of Things risk analysis. He is the author of “7 Keys to Academic Success” (ISBN:[masked]), which is a study guide to pupils, and the director of a Ghana-based charity for supporting underprivileged schools and pupils. Abstract: All businesses, institutions and organizations require their systems to run reliably. Although the failures of some systems are not life-threatening (e.g. parking ticket system of a museum), the failures of other systems (e.g. appointment scheduling system of a hospital) can be catastrophic. High-consequence systems are systems that can have huge reputational damage, high financial loss and/or catastrophic consequences on human life and/or the environment if they should fail. Measuring and improving the reliability of such systems is of the utmost importance to designers, analysts, stakeholders and users of these systems. However, measuring and improving reliability requires some form of failure, repair or maintenance data, which is not always available, especially for new systems. In this talk, the speaker will propose a technological framework for tackling these challenges in high-consequence environments. The proposed framework will include the use of data sharing and integration approaches for gathering system operating environment data that are necessary for reliability analysis. Only the technical requirements of systems will be discussed; non-technical requirements, such as political, social, environmental, etc., which are critical to businesses and their systems, are outside the scope of this talk. The proposed framework will be applied to a hypothetical business case study and the findings will be discussed. This meetup is supported by WeWork. WeWork is the platform for creators. We provide the space, community and services you need to create your life’s work. To learn more send an email to [masked] or call[masked]. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london
- Drinks with Data Scientists
Join our first Drinks with Data Scientists! Enjoy this great opportunity to connect with your fellow Data Scientists, share knowledge, experiences and mainly have some fun with like-minded people. Let's have an amazing networking time together! Starting at 7pm Place: The Piccadilly Tavern - 71-75 London Rd, Piccadilly, Manchester M1 2BS, UK Invite your friends, or come by yourself and make new ones! • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • East Conference Apr 30 - May 4: https://odsc.com/boston
- LIVESTREAMING & VIDEO ACCESS - ODSC West 2018
- WEBINAR: ODSC West Online Warm-Up (Free)
ODSC West is getting closer! We want to invite you to participate in ODSC West's Warm-Up. To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/3338809288646589441 After our great Part 1, we are bringing 4 new speakers from our ODSC West Conference to present 30 minutes sessions. Matthew Rubashkin, Ph.D. AI Program Director at Insight Data Science: Building an image search service from scratch Michael Mahoney, Ph.D. - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap George Williams, Director of Data Science at GSI Technology, Inc: Visual Search: The Next Frontier of Search Joshua Cook, Curriculum Developer at Databricks: Engineering for Data Science Nisha Talagala, CTO/VP of Engineering at ParallelM: Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use Full Agenda Detail Session 1 - Building an image search service from scratch Speaker: Matthew Rubashkin, PhD Abstract: We are bringing a workshop on how you would go about building your representations, both for image and text data, and efficiently do similarity search. By the end of this workshop, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset. Session 2 - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap (30 Minutes) Speaker: Michael Mahoney, PhD Abstract: In this session, we will describe some of the underlying randomized linear algebra techniques. We'll describe Alchemist, a system for interfacing between Spark and existing MPI libraries that are designed to address this performance gap. We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system. We'll also describe related work on communication-avoiding machine learning, optimization-based methods that can call these algorithms, and extending Alchemist to provide an ipython notebook <=> MPI interface. Session 3 - Visual Search: The Next Frontier of Search (30 Minutes) Speaker: George Williams Abstract: In this session, you will learn the latest state-of-the-art visual search research and techniques as the speakers will share their in-depth knowledge on the subject, how to scale your visual search solution to address the billion-scale problem and how to train models that provide more specific and accurate results for visually rich categories. Session 4 - Engineering for Data Science (30 Minutes) Speaker: Joshua Cook Abstract: This talk will discuss Docker as a tool for the data scientist, in particular in conjunction with the popular interactive programming platform, Jupyter, and the cloud computing platform, Amazon Web Services (AWS). Using Docker, Jupyter, and AWS, the data scientist can take control of their environment configuration, prototype scalable data architectures, and trivially clone their work toward replicability and communication. This talk will toward developing a set of best practices for Engineering for Data Science. Speaker: Nisha Talagala Abstract: In this hands-on workshop, attendees will learn how to take Machine Learning and Deep Learning programs into a production use case and manage the full production lifecycle. This workshop is targeted for data scientists, with some basic knowledge of Machine Learning and/or Deep Learning algorithms, who would like to learn how to bring their promising experimental results on ML and DL algorithms into production success.
- ODSC Europe Online Warm-Up (Free)
We are very excited to our ODSC Europe next month! As we get closer to the conference, we want to invite you to participate in ODSC Europe's Online Warm-Up. To access this webinar, please register using the link below: https://register.gotowebinar.com/register/656457212932939779 We will features 4 speaker from our upcoming ODSC Europe conference in London each of which will present a 30 minute sessions including: Jeffrey Yau, PhD - Multivariate time series forecasting using statistical and machine learning models Alan Ruter - Telling Stories with Data Dr. Jan Freyberg - Interactive data visualisation in python Dr. Colin Gillespie - Getting to grips with the Tidyverse ® Full Agenda Detail Session 1 - Interactive data visualisation in python (30 Minutes) Speaker: Dr. Jan Freyberg Bio: Dr Jan Freyberg is a data scientist at ASI. He has worked on data science projects in the private and public sector. Jan completed a PhD and a fellowship studying brain activity, vision and consciousness in autism at the University of Cambridge and King’s College London Abstract: Creating interactive visualisations adds a layer of complexity to the data science workflow: during modelling and data exploration, interactivity is effectively achieved by re-running chunks of code with different parameters. In this overview, we will introduce the python libraries that make this extra development as frictionless as possible. Libraries to be used include ipywidgets, plotly, and plotly dash. Session 2 - Telling Stories with Data (30 Minutes) Speaker: Alan Ruter Bio: Alan Rutter is the co-founder of consultancy Clever Boxer. He has taught data visualisation techniques to thousands of students, and for organisations including the Home Office, Department of Health, Biotechnology and Biosciences Research Council, Capita, Novartis and Kings College London. Abstract: This session will introduce an audience-centred approach to visualising data. It will cover with tried-and-tested techniques for communicating data-driven stories effectively to people from a broad range of backgrounds. Session 3 - Getting to Grips with the Tidyverse ® (30 Minutes) Speaker: Dr. Colin Gillespie Bio: Dr. Colin Gillespie is Senior lecturer (Associate Professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics and also regularly employed as a consultant. Abstract: This overview aims to give a gentle introduction to the ideas behind some of the standard algorithms used in predictive analytics. The goal is to dispel the "magic" around common methods, such as lasso, logistic regression, naive bayes and others. Session 4 - Multivariate time series forecasting using statistical and machine learning models (30 Minutes) Speaker: Jeffrey Yau, PhD Bio: Jeffrey is the Chief Data Scientist at AllianceBernstein, a global investment firm managing over $500 billions. Graduated with a Ph.D. in economics from the University of Pennsylvania. Abstract: This overview introduces the formulation Vector Autoregressive (VAR) Models, one of the most important class of multivariate time series statistical models, and neural network-based techniques, demonstrates how they are implemented in practice, and compares their advantages and disadvantages used in practice.