BEGIN:VCALENDAR VERSION:2.0 PRODID:-//OpenACalendar//NONSGML OpenACalendar//EN X-WR-CALNAME:Scotland Data Science & Technology MeetUp: What can Data Scien tists learn from DevOps & Modelling: the good\, the bad and... - Open Tech Calendar BEGIN:VEVENT UID:4976@otc.opentechcalendar.co.uk URL:https://opentechcalendar.co.uk/event/4976-what-can-data-scientists-lear n-from-devops SUMMARY:Scotland Data Science & Technology MeetUp: What can Data Scientists learn from DevOps & Modelling: the good\, the bad and... DESCRIPTION:Speaker 1 - Yuri Aleksandrov\, Aridhia -\n\nhttps://www.linkedi n.com/in/yuricomputing (https://www.linkedin.com/in/yuricomputing)\n\nYuri is a data scientist at Aridhia\, where he assists healthcare researchers with managing collaborative research\, stratified medicine\, and biomedica l research through the use of biomedical informatics and analytics platfor m. He holds a degree in Criminology from the University of Abertay Dundee and MSc in Data Engineering from the Dundee University. Yuri is passionate about using data to solve problems in various areas ranging from retail t o healthcare. He describes himself as "multipotentialite" and his interest range spans multiple areas\, such as entrepreneurship\, science and techn ology\, in particular\, machine learning and blockchain. He is always open to discuss projects and ideas\, so feel free to get in touch at https://w ww.linkedin.com/in/yuricomputing/ or aleksandrov.yuri@gmail.com\n\nPresent ation 1-\n"What can Data Scientists learn from DevOps"\n\nThis talk will c over how data scientists can integrate DevOps and agile methodologies in t heir workflows\, to improve management of user/client requirements\, quali ty and accuracy of data and outputs produced by their models. In recent ye ars data science projects and models significantly grew in complexity (e.g Deep Learning)\, became fully operational and deployable stand-alone prod ucts and often involve a large team collaboration. This closely resembles traditional Software Development process\, as opposed to "load your data a nd do some stats stuff". However\, many data scientists emerge from non-so ftware engineering backgrounds (including myself) and the caveats of worki ng with code at scale are often learned on the go. Here\, we will discuss how introducing DevOps like methodologies help to streamline projects\, en able better collaboration and help to gain more from data science in busin ess.\n\nSpeaker 2 - Mike Chantler\, Heriot-Watt University\n\nhttps://www. linkedin.com/in/mikechantler\n\nMike is a professor of computer science at the Watt. He has worked in various forms of machine learning and visualis ation over the last thirty years. He became passionate about helping peopl e make better use of their data and ideas after spending lots of time part icipating in many advisory team meetings.\n\n"Topic Modelling: the good\, the bad and the ugly"\n\nTopic modelling is an extraordinary public domain technology that is capable of generating highly intuitive overviews of\, and browsing mechanisms for\, large document sets. For instance\, it is ca pable of automatically summarising and categorising hundreds of thousands of unstructured product descriptions or large project portfolio drawn from disparate sources.\n\nIt does this by automatically generating sets of hi ghly intuitive categories\, or 'topics'\, together with the categorisation of the documents (into the topics).\n\nFor instance two of sixty topics g enerated from a database of 35\,000 UK research project descriptions are:\ n\n"Virus\, Disease\, Infection\, Vaccine\, Parasite\, Chicken ..."\n\nand \n\n"Climate\, Change\, Urban\, Risk\, City\, Resilience\, Infrastructure ..."\n\nAnd from the allocation of these topics to projects we can estimat e that £169M of UK Research Councils spend was associated with the 'Climat e' topic\, while the equivalent monies for EU projects with UK involvement was £296M.\n\nNote that this type of comparison often becomes extremely e xpensive\, and not to mention highly political\, if no data-driven mechani sm for creating a common categorisation or classification is employed.\n\n It has a wide range of uses from strategic planning to recommender engines .\n\nIn this presentation I'll illustrate the power of topic modelling usi ng a Brexit inspired example\, give insight as to how it works\, and point out some of the pitfalls and gotchas for the unwary.\n\nThere will be no maths or theory.\n\n6:30 PM - 7.00 PM: Networking\n\n7.00PM - 7:30 PM: Yur i Aleksandrov\, Aridhia\n\n7:30 PM - 8.00 PM: Mike Chantler\, Heriot-Watt University\n\n8.00 PM - 8:30 PM: Q &\; A session\n\n8.30 PM - 9.00 PM: Networking and Drinks\nhttps://opentechcalendar.co.uk/event/4976-what-can- data-scientists-learn-from-devops\nPowered by Open Tech Calendar X-ALT-DESC;FMTTYPE=text/html:
Speaker 1 - Yuri Aleksandrov\,
Aridhia -
https://www.linkedin.com/in/yuricomputing (https://www.li
nkedin.com/in/yuricomputing)
Yuri is a data scientist at Aridhia\,
where he assists healthcare researchers with managing collaborative resear
ch\, stratified medicine\, and biomedical research through the use of biom
edical informatics and analytics platform. He holds a degree in Criminolog
y from the University of Abertay Dundee and MSc in Data Engineering from t
he Dundee University. Yuri is passionate about using data to solve problem
s in various areas ranging from retail to healthcare. He describes himself
as &ldquo\;multipotentialite&rdquo\; and his interest range spans multipl
e areas\, such as entrepreneurship\, science and technology\, in particula
r\, machine learning and blockchain. He is always open to discuss projects
and ideas\, so feel free to get in touch at https://www.linkedin.com/in/y
uricomputing/ or aleksandrov.yuri@gmail.com
Presentation 1-
&quo
t\;What can Data Scientists learn from DevOps"\;
This talk will
cover how data scientists can integrate DevOps and agile methodologies in
their workflows\, to improve management of user/client requirements\, qua
lity and accuracy of data and outputs produced by their models. In recent
years data science projects and models significantly grew in complexity (e
.g Deep Learning)\, became fully operational and deployable stand-alone pr
oducts and often involve a large team collaboration. This closely resemble
s traditional Software Development process\, as opposed to "\;load you
r data and do some stats stuff"\;. However\, many data scientists emer
ge from non-software engineering backgrounds (including myself) and the ca
veats of working with code at scale are often learned on the go. Here\, we
will discuss how introducing DevOps like methodologies help to streamline
projects\, enable better collaboration and help to gain more from data sc
ience in business.
Speaker 2 &ndash\; Mike Chantler\, Heriot-Watt U
niversity
https://www.linkedin.com/in/mikechantler
Mike is a
professor of computer science at the Watt. He has worked in various forms
of machine learning and visualisation over the last thirty years. He beca
me passionate about helping people make better use of their data and ideas
after spending lots of time participating in many advisory team meetings.
&ldquo\;Topic Modelling: the good\, the bad and the ugly&rdquo\;
Topic modelling is an extraordinary public domain technology that is
capable of generating highly intuitive overviews of\, and browsing mechan
isms for\, large document sets. For instance\, it is capable of automatica
lly summarising and categorising hundreds of thousands of unstructured pro
duct descriptions or large project portfolio drawn from disparate sources.
It does this by automatically generating sets of highly intuitive
categories\, or &lsquo\;topics&rsquo\;\, together with the categorisation
of the documents (into the topics).
For instance two of sixty topic
s generated from a database of 35\,000 UK research project descriptions ar
e:
&ldquo\;Virus\, Disease\, Infection\, Vaccine\, Parasite\, Chick
en &hellip\;&rdquo\;
and
&ldquo\;Climate\, Change\, Urban\,
Risk\, City\, Resilience\, Infrastructure &hellip\;&rdquo\;
And fro
m the allocation of these topics to projects we can estimate that £\;
169M of UK Research Councils spend was associated with the &lsquo\;Climate
&rsquo\; topic\, while the equivalent monies for EU projects with UK invol
vement was £\;296M.
Note that this type of comparison often be
comes extremely expensive\, and not to mention highly political\, if no da
ta-driven mechanism for creating a common categorisation or classification
is employed.
It has a wide range of uses from strategic planning t
o recommender engines.
In this presentation I&rsquo\;ll illustrate
the power of topic modelling using a Brexit inspired example\, give insigh
t as to how it works\, and point out some of the pitfalls and gotchas for
the unwary.
There will be no maths or theory.
6:30 PM &ndash
\; 7.00 PM: Networking
7.00PM &ndash\; 7:30 PM: Yuri Aleksandrov\,
Aridhia
7:30 PM &ndash\; 8.00 PM: Mike Chantler\, Heriot-Watt Unive
rsity
8.00 PM &ndash\; 8:30 PM: Q &\;amp\; A session
8.30
PM &ndash\; 9.00 PM: Networking and Drinks
More info: https://opentechcalendar.co.uk/event/4976-what-can-data-scientists -learn-from-devops
Powe red by Open Tech Calendar
DTSTART:20170427T173000Z DTEND:20170427T200000Z LAST-MODIFIED:20170409T000900Z SEQUENCE:22049457 DTSTAMP:20170303T230514Z END:VEVENT END:VCALENDAR