A presentation delivered by Robert Brooks at the Police Foundation's annual conference 'Policing and Justice for a Digital Age' (December 2016) on using big data and predictive analysis.
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
P 02 internal_data_first_2017_04_22_v6Vishwa Kolla
Data is the new oil and Analytics is the combustion engine. Internal data plays a special role in every organization. See how one can become internal data rich and move the value needle. Through what we call thoughtful data engineering, we found good data trumped good models time and again.
Data Science Salon: Digital Transformation: The Data Science CatalystFormulatedby
Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression).
Next DSS MIA Event - https://datascience.salon/miami/
Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line.
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
P 02 internal_data_first_2017_04_22_v6Vishwa Kolla
Data is the new oil and Analytics is the combustion engine. Internal data plays a special role in every organization. See how one can become internal data rich and move the value needle. Through what we call thoughtful data engineering, we found good data trumped good models time and again.
Data Science Salon: Digital Transformation: The Data Science CatalystFormulatedby
Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression).
Next DSS MIA Event - https://datascience.salon/miami/
Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line.
Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Le...Formulatedby
Presented by Yashas Vaidya, Sr Data Scientist at DataIku
Next DSS MIA Event - https://datascience.salon/miami/
The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.
Data Science Salon: Building a Data Science CultureFormulatedby
Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups.
Next DSS MIA Event - https://datascience.salon/miami/
There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.
P 02 ta_in_uw_transformation_2017_06_13_v5Vishwa Kolla
Text Analytics can be fun, useful and distracting. It is not just about the tools, but about how to use tools to drive business outcome. In this deck, you will get a sneak peak into some uses of text analytics in Life Insurance Transformation
Big Data: selling the Business Case to the businessJ On The Beach
Big Data: selling the Business Case to the business by Eline Brandt & Javier de la Torre Medina
Big Data, every company loves the idea of it, but often, selling the Business Case is a challenge. So how to build a successful Business Case for your Big Data initiative for the Business Users? This presentation is based on the most common objections one gets, and how to deal with them. We'll go through one of my most surprising projects, look at the lessons learned and how can we optimize the Business Case?
Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurtur...Vishwa Kolla
We are at a point of inflection of embedding Advanced Analytics everywhere.
If you are interested in learning about:
1) Why should we cross the Cigital Chasm
2) Which of the areas should one focus
3) Which of the problems should one focus on
4) What are the opportunities / challenges / mitigations
then this is for you.
Automation Isn't Enough: You Need Robotics or AIDatavail
You need processes that are dynamic, and change over time based on real-time data from many data sources. Predictive analytics, machine learning, self-documenting hardware, point and click integrations, and automated regression and performance testing is the future for ERP, EPM, analytics, and integration professionals. The future is here and you need to be in the know.
Effective Solutions for Your Supply Chain RisksHalo BI
Have you considered the supply chain risks associated with your business? Here we’ll identify the most common risks, the departments they affect, and their potential financial impact on your company. We’ll also address some solutions for preparation, prediction, and management that can have a huge impact on your business.
Your supply chain shapes investment plans, production processes, manufacturing decisions, and more for the entire company. Using top-tier management tools ensures accuracy and provides you with a comprehensive view of your supply chain risks. Learn more about real supply chain impacts and innovative risk solutions below. http://halobi.com/2015/08/effective-solutions-for-your-supply-chain-risks
The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.
Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Le...Formulatedby
Presented by Yashas Vaidya, Sr Data Scientist at DataIku
Next DSS MIA Event - https://datascience.salon/miami/
The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.
Data Science Salon: Building a Data Science CultureFormulatedby
Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups.
Next DSS MIA Event - https://datascience.salon/miami/
There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.
P 02 ta_in_uw_transformation_2017_06_13_v5Vishwa Kolla
Text Analytics can be fun, useful and distracting. It is not just about the tools, but about how to use tools to drive business outcome. In this deck, you will get a sneak peak into some uses of text analytics in Life Insurance Transformation
Big Data: selling the Business Case to the businessJ On The Beach
Big Data: selling the Business Case to the business by Eline Brandt & Javier de la Torre Medina
Big Data, every company loves the idea of it, but often, selling the Business Case is a challenge. So how to build a successful Business Case for your Big Data initiative for the Business Users? This presentation is based on the most common objections one gets, and how to deal with them. We'll go through one of my most surprising projects, look at the lessons learned and how can we optimize the Business Case?
Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurtur...Vishwa Kolla
We are at a point of inflection of embedding Advanced Analytics everywhere.
If you are interested in learning about:
1) Why should we cross the Cigital Chasm
2) Which of the areas should one focus
3) Which of the problems should one focus on
4) What are the opportunities / challenges / mitigations
then this is for you.
Automation Isn't Enough: You Need Robotics or AIDatavail
You need processes that are dynamic, and change over time based on real-time data from many data sources. Predictive analytics, machine learning, self-documenting hardware, point and click integrations, and automated regression and performance testing is the future for ERP, EPM, analytics, and integration professionals. The future is here and you need to be in the know.
Effective Solutions for Your Supply Chain RisksHalo BI
Have you considered the supply chain risks associated with your business? Here we’ll identify the most common risks, the departments they affect, and their potential financial impact on your company. We’ll also address some solutions for preparation, prediction, and management that can have a huge impact on your business.
Your supply chain shapes investment plans, production processes, manufacturing decisions, and more for the entire company. Using top-tier management tools ensures accuracy and provides you with a comprehensive view of your supply chain risks. Learn more about real supply chain impacts and innovative risk solutions below. http://halobi.com/2015/08/effective-solutions-for-your-supply-chain-risks
The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Big data is not only transforming the way we make decisions, but also the way that organizations recruit, manage and develop their people, driving engagement, innovation and productivity to new peaks.
Explore the future of cloud-based human capital management, and demonstrate how what you do today will influence and energize your company for decades to come.
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
PwC's recently released Responsible AI Diagnostic surveyed around 250 senior business executives from May to June 2019. The survey says that 84% of CEOs agree that AI-based decisions need to be explainable in order to be trusted. In the past few years, Deep learning has shown remarkable results in various applications, which makes it one of the first choices for many AI use cases. However, deep learning models are hard to explain, and since the majority of CEOs expect AI solutions to be explainable, deep learning has a serious challenge. Daniel Kahneman, in his book thinking fast and slow, presented two different systems the human brain uses to form thoughts and decisions: System 1: fast, intuitive and hard to explain System 2: slow, conscious and easy to explain In this talk I will present: A) PwC Responsible AI Survey B) A proposed deep learning framework that mimics the two systems of thinking C) The recent advances in the neural symbolic learning field.
Shaping Tomorrow is the world’s first, multi-award winning, and only AI-driven, systems thinking model that delivers strategic foresight and anticipatory thinking in real-time.
Explainability for Natural Language ProcessingYunyao Li
NOTE: Please check out the final version here with small but important updates and links to downloadable version and recording: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249992241
Updated version on our popular tutorial on "Explainability for Natural Language Processing" as a tutorial at KDD'2021.
Title: Explainability for Natural Language Processing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Presenter: Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
A presentation given by Will Linden, Acting Director of the Violence Reduction Unit, Scotland for the Police Foundation's Annual Conference 2017 'Networked Policing: effective collaboration between the police, partners and communities'.
Networked policing - the Greater Manchester Experience CSSaunders
A presentation by Chief Constable Ian Hopkins of Greater Manchester Police given at the Police Foundation's annual conference 2017 'Networked Policing: effective collaboration between the police, partners and communities'.
Joining up what we've got or designing for what is needed?CSSaunders
A presentation by David Kelly, Programme Manager (Place-Based Integration), Greater Manchester Police given at the Police Foundation's Annual Conference 2017.
Networked policing: learning and working across organisational boundaries to ...CSSaunders
A presentation given by Professor Adam Crawford, Director of Leeds Social Sciences Unit, University of Leeds at the Police Foundation's annual conference 2017 'Networked Policing: effective collaboration between the police, partners and communities'.
A process server is a authorized person for delivering legal documents, such as summons, complaints, subpoenas, and other court papers, to peoples involved in legal proceedings.
What is the point of small housing associations.pptxPaul Smith
Given the small scale of housing associations and their relative high cost per home what is the point of them and how do we justify their continued existance
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Understanding the Challenges of Street ChildrenSERUDS INDIA
By raising awareness, providing support, advocating for change, and offering assistance to children in need, individuals can play a crucial role in improving the lives of street children and helping them realize their full potential
Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
Many ways to support street children.pptxSERUDS INDIA
By raising awareness, providing support, advocating for change, and offering assistance to children in need, individuals can play a crucial role in improving the lives of street children and helping them realize their full potential
Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
ZGB - The Role of Generative AI in Government transformation.pdfSaeed Al Dhaheri
This keynote was presented during the the 7th edition of the UAE Hackathon 2024. It highlights the role of AI and Generative AI in addressing government transformation to achieve zero government bureaucracy
Presentation by Jared Jageler, David Adler, Noelia Duchovny, and Evan Herrnstadt, analysts in CBO’s Microeconomic Studies and Health Analysis Divisions, at the Association of Environmental and Resource Economists Summer Conference.
4. PwC
Data, analytics, technology and information
management are all evolving at a rapid pace
that is set to accelerate in the future…
and it will spare no industry
1980’s
1990’s
2000’s
2010+
1970’s
Reports
OLTP
Punchcards
Data
Processing
Decision
Support
Websites
Audio
Finance Management
Analyst
Model
f(x)
X1
X2
X3
Y1
Y2
Multivariate
Analysis
Business
Intelligence
Predictive
Modeling
Information
Worker
Simulation &
Visualization
Social
Media
The Data
Scientist
Embedded
Analytics
Mobile
The Data
Warehouse
The Data
Warehouse
Appliance
Big
Data
RDBMS
Smart Phones &
Tablets
Increasing pace of evolution
Background
Advances in Data & Analytics over time
Access to a large wealth of
modelling algorithms and
techniques
Cheap(er) storage and
computing power (e.g. cloud
based solutions)
Exponential development of
data available (internal and
external to organisations)
A significant change in paradigm:
4
5. PwC
Background
Policing data
5
of staff records
1,000s
of
addresses
millions
of victims
millions
of ANPR hits
billions
of vehicle records
100s
of phone records
100,000s
of financial records
100,000s
of offender records
100,000s of witness statements
millions
of intelligence reports
100,000s
of calls
millions
of crime reports
millions
6. PwC
Background
Internet of Things
Converging and connected technology…
6
Smart devices
Sensors
Biometrics
Wireless Connectivity
Nanotechnology
Analytics
Robotics
• A multi-trillion dollar emerging
industry
• 50 billion connected devices by 2020,
generating 40k exabytes of data
• 54% of global top performing
companies are investing more in sensor
technologies
• Identified by WEF as a phenomenon
that will dramatically transform
economic activity (including
insurance)
Wearables
Sources: PwC Digital IQ survey, IDC, Business Insider, World Economic Forum
Data storage
7. PwC
Background
Creating the internet of…everything!
7
*50 billion connected devices by 2020, generating 40k exabytes of data
Smart sensors & connected devices everywhere*
8. PwC
Background
What is predictive modelling?
• Using past data to find patterns
• Most well known applications is
credit scoring
• Statistical models used to
segment areas to together
• Principally using GLM
(generalised linear modelling)
• Evolving data science towards
algorithmic Machine Learning
• Who
• When
• What
• To which group
should we …
8
Predictive models Questions
9. PwC
Background
Types of machine learning
9
Supervised Learning:
pre-labelled data trains a
model to predict new
outcomes
Example: Sorting
LEGO blocks by
matching them with
the colour of the bags
Unsupervised Learning:
Non-labelled data self
organises to predict new
outcomes (e.g. clustering)
Reinforcement Learning:
feedback to algorithm
when it does something
right or wrong
Example:
Child gets
feedback ‘on the
job’ when it does
something right
or wrong
11. PwC
Key requirements
What is needed to make it work?
The question you are try to answer
Data
Tools and systems
11
12. PwC
People
Culture
Senior buy-in and support
Ensure clear
communication
Ensure outputs are simple
and easy to interpret
Skillset
Processes
Identifying the right
individuals
Establish training
Collaboration including
experts in other areas
The Key Requirements
Systems
13. PwC
Response
Integrate with existing
processes
Keep the output simple
Understand the
limitations
Calculation
Key variables and
correlation
Business and expert
judgement and
challenge
Ethics on using personal
data
The Key Requirements
People Processes Systems
14. PwC
Software
Consider users
Start with a proof of
concept
Consider open-source
Data
Merging multiple
datasets
Align with other
analytics/ business
intelligence
Consider sources: Direct,
Indirect and External
The Key Requirements
People Processes SystemsPeople
16. PwC
The Application
Predictive models: Professional Gamblers
What’s the problem?
Tighter regulation and smaller profit margins
require betting companies to be more selective
about their customers.
How we helped?
16
Identify the customer
Determine the cut-off
Understand the customer
17. PwC
The Application
Predictive models: Predictive Asset Maintenance
What’s the problem?
A power company needs to reduce the
amount of network downtime from assets
that fail.
How we helped?
17
Highlight assets with a
high risk of failure
Integrate with existing
maintenance schedule
Use real-time data feeds
18. PwC
The Application
Predictive models: Talent retention
What’s the problem?
A media company wanted to understand
and manage the loss of talent in the
organisation.
How we helped?
18
Predict those at high risk
of leaving
New performance
management system
Targeted interventions
