With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work. Today, the widespread availability of sophisticated open-source statistical computing and data visualization environments provides the actuarial profession with an unprecedented opportunity to deepen its expertise as well as broaden its horizons, living up to its potential as a profession of creative and flexible data scientists.
This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.
Slide deck presenting objectives of Big Data Working group of Institute of Actuaries in Belgium.
The goal of the group is to discuss:
- Impact of Big Data on insurance sector and the
actuarial profession;
- Present challenges and good practices when working
with Big Data;
- Educate actuarial profession about Big Data.
Contact me at mat@motosmarty.com
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierCapgemini
The C-suite could soon start to feel a little crowded, with Chief Digital Officers, Chief Innovation Officers, Chief Risk Officers
and Chief Data Officers joining the more established functional leaders. To avoid C-suite proliferation, companies need to
decide whether to elevate a new functional role to “chief” based on the strategic importance of the issue for the organization and its sector. For example, in many organizations, marketing will be so essential to performance that few would deny the need for a CMO. In financial services, data has become so mission-critical that the role of Chief Data Officer is simply essential.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?Capgemini
This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.
Big Data: Real-life Examples of Business Value GenerationCapgemini
This presentation looks at real-world cases of how organizations are using, or planning to use, big data technology to drive value. It looks at the different ways in which the technology is being used in a business context. Examples are drawn from Retail, Telco, Financial Services and Consumer goods.
It also develops a range of business scenarios from simple cost reduction through to new business models specifically looking at how the business case has been built and what value has been realized.
First presented by Richard Brown, Capgemini Program Lead for Business Information Management, at the IP Expo – Big Data Summit 2014.
http://www.capgemini.com/big-data-analytics
Companies that want to turn excellent customer experience into growth need to master Customer Journeys. Customer Journeys (the set of interactions a customer has with a brand to complete a task) and less moments of truth are what matter for a customer. Companies that master not only see an improvement in customer experience, loyalty, and operational productivity; they also see above-market growth.
INFOGRAPHIC: Fixing the Insurance Industry - how big data can transform custo...Capgemini
Insurers are facing a moment of truth. Customer satisfaction levels have hit worryingly low levels. According to a survey conducted by Capgemini in 2014, less than a third of customers globally are satisfied with the services of their insurance providers. Traditional insurers also face competition from new entrants who are determined to meet customer expectations. Non-traditional competitors, such as ecommerce majors and technology startups, are leveraging their data-rich customer interactions to create and sell insurance products.
Surprisingly, insurers seem to have overlooked the impact of Big Data on improving customer experience as they often focus their Big Data efforts on detecting fraudulent claims and improving underwriting profitability. In fact, only 12% of insurers consider the enhancement of customer experience as a top Big Data priority. This is startling given the poor levels of customer satisfaction in the insurance industry.
Staying ahead in the cyber security game - Sogeti + IBMRick Bouter
Cyber security is center stage in the world today, thanks to almost continuous revelations about incidents and breaches. In this context of unpredictability and insecurity, organizations are redefining their approach to security, trying to find the balance between risk, innovation and cost. At the same time, the field of cyber security is undergoing many dramatic changes, demanding organizations embrace new practices and skill sets.
Cyber security risk is now squarely a business risk – dropping the ball on security can threaten an organization’s future – yet many organizations continue to manage and understand cyber security in the context of the it department. This has to change.
Slide deck presenting objectives of Big Data Working group of Institute of Actuaries in Belgium.
The goal of the group is to discuss:
- Impact of Big Data on insurance sector and the
actuarial profession;
- Present challenges and good practices when working
with Big Data;
- Educate actuarial profession about Big Data.
Contact me at mat@motosmarty.com
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierCapgemini
The C-suite could soon start to feel a little crowded, with Chief Digital Officers, Chief Innovation Officers, Chief Risk Officers
and Chief Data Officers joining the more established functional leaders. To avoid C-suite proliferation, companies need to
decide whether to elevate a new functional role to “chief” based on the strategic importance of the issue for the organization and its sector. For example, in many organizations, marketing will be so essential to performance that few would deny the need for a CMO. In financial services, data has become so mission-critical that the role of Chief Data Officer is simply essential.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?Capgemini
This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.
Big Data: Real-life Examples of Business Value GenerationCapgemini
This presentation looks at real-world cases of how organizations are using, or planning to use, big data technology to drive value. It looks at the different ways in which the technology is being used in a business context. Examples are drawn from Retail, Telco, Financial Services and Consumer goods.
It also develops a range of business scenarios from simple cost reduction through to new business models specifically looking at how the business case has been built and what value has been realized.
First presented by Richard Brown, Capgemini Program Lead for Business Information Management, at the IP Expo – Big Data Summit 2014.
http://www.capgemini.com/big-data-analytics
Companies that want to turn excellent customer experience into growth need to master Customer Journeys. Customer Journeys (the set of interactions a customer has with a brand to complete a task) and less moments of truth are what matter for a customer. Companies that master not only see an improvement in customer experience, loyalty, and operational productivity; they also see above-market growth.
INFOGRAPHIC: Fixing the Insurance Industry - how big data can transform custo...Capgemini
Insurers are facing a moment of truth. Customer satisfaction levels have hit worryingly low levels. According to a survey conducted by Capgemini in 2014, less than a third of customers globally are satisfied with the services of their insurance providers. Traditional insurers also face competition from new entrants who are determined to meet customer expectations. Non-traditional competitors, such as ecommerce majors and technology startups, are leveraging their data-rich customer interactions to create and sell insurance products.
Surprisingly, insurers seem to have overlooked the impact of Big Data on improving customer experience as they often focus their Big Data efforts on detecting fraudulent claims and improving underwriting profitability. In fact, only 12% of insurers consider the enhancement of customer experience as a top Big Data priority. This is startling given the poor levels of customer satisfaction in the insurance industry.
Staying ahead in the cyber security game - Sogeti + IBMRick Bouter
Cyber security is center stage in the world today, thanks to almost continuous revelations about incidents and breaches. In this context of unpredictability and insecurity, organizations are redefining their approach to security, trying to find the balance between risk, innovation and cost. At the same time, the field of cyber security is undergoing many dramatic changes, demanding organizations embrace new practices and skill sets.
Cyber security risk is now squarely a business risk – dropping the ball on security can threaten an organization’s future – yet many organizations continue to manage and understand cyber security in the context of the it department. This has to change.
Technology Innovation Trends In Insurance | Navdeep Arora Navdeep Arora
This presentation talks about ‘Technology Innovation Trends In Insurance’. This covers seven themes that are reshaping supply & demand of #insurance globally, premium & profit pools are migrating across the #insurance value chain. Explains how value creation opportunities are different across mature & developing markets, how #technology & digital capabilities that target value chain effectiveness (not just scale efficiencies) offer compelling #investment opportunities. Also has #InsurTech levers and start-up examples in non-life insurance and life insurance.
Market Research Reports, Inc. has announced the addition of “Big Data in Global Retail Market 2021” research report to their offering. See more at - http://mrr.cm/U6V
What Does Good Risk Culture Actually Look Like?accenture
At RiskMinds International 2015, Rafael Gomes presented "What Does Good Risk Culture Actually Look Like?" and addressed risk culture and conduct in practice. Get more information from Rafael’s blog post, which describes how financial services can recognize, measure, and communicate good risk culture: http://bit.ly/1RFBrzF
Applied Innovation for the UnorganizationCapgemini
Many organizations are not designed to innovate effectively or fast. They lack the necessary processes, talent, risk tolerance and leadership alignment, as well as a culture that encourages, rewards and promotes innovation. In order to innovate effectively, the traditional organizations need to embrace the “Unorganization” structure to meet today’s market demands.
Capgemini’s new and tested global Applied Innovation Exchange network, breaks the traditional model; instead taking a multi-party collaborative approach and balancing external and internal thinking to assist organizations in cultivating and applying innovation within their enterprises, and specifically to their most strategic business processes.
Presented by Lanny Cohen, Group Chief Technology Officer, Capgemini at Sogeti’s VINT Symposium
Learn more on Capgemini’s Applied Innovation Exchanges: https://www.capgemini.com/applied-innovation-exchange
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...Capgemini
Are banks and insurers a safe pair of hands when it comes to customer data? Our global survey of more than 180 senior data privacy and security professionals – as well as 7,600 consumers – found that less than a third (29%) of these organizations offer both strong data privacy practices and a sound security strategy. Just one in five (21%) are highly confident that they can detect a cybersecurity breach.
This picture has so far not unduly affected consumers’ perceptions of the industry. We found that 83% of consumers trust banks and insurers when it comes to data. And while one in four institutions have reported being victim of a hack, just 3% of consumers believe their own bank or insurer has ever been breached. However, with the pending General Data Protection Regulation (GDPR) regulations, this trust factor is likely to change as transparency increases. Financial organizations have to reveal a data breach 72 hours after the incident.
Banks and insurance firms have a clear incentive therefore to fortify their defences. As well as avoiding the prohibitive fines and penalties that will result from compromised data, protecting privacy offers a strategic business advantage. Addressing security concerns will drive greater adoption of low-cost digital channels. We found that security concerns deter nearly half of consumers (47%) from using digital channels. It will also reduce churn and attract competitors’ customers – 74% of consumers would switch their bank or insurer in the event of a data breach.
Preparing to be a trusted data steward is no easy task, however. It means raising the bar on multiple dimensions:
• Aligning data practices with consumers’ expectations
• Finding innovative ways of providing non-intrusive security to consumers
• Building the capabilities required to monitor cyber risks on a real-time basis
• Revisiting the data governance model.
Building your reputation for data privacy and robust security is definitely challenging. But, those who strike the right chord with consumers will enjoy a competitive advantage over their peers. The winners will be those who triumph in the trust game.
Can Financial Institutions be the next Digital Masters? Capgemini says YESCapgemini
With Millennials coming of age it is even more important than ever for Financial Institutions to provide a strong digital experience. In order to deliver that Financial Institutions need to become Digital Masters - accelerating their digital business, turn data into insights, transform the customer experience and embrace the mobile mind shift.
Capgemini’s Trends in Transformation powered by HPE is your Jedi Master. Are you ready to become a Digital Master? Join us to start your journey.
Presented at HPE Discover Las Vegas 2016.
Guide to Data Analytics: The Trend That's Reshaping the Insurance IndustryApplied Systems
Information you need is in your management system –- you just have to understand how to use it. Read this guide to learn what data analytics is, how it's impacting the insurance industry, why it's important for independent agencies and brokerages, and how to create your own data analytics strategy.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
Hosted by Digital Insurance
The insurance industry is at a tipping point: A next-generation of insurer is disrupting traditional business models by building future-proof business systems that power superior customer experiences and generate new revenue streams.
With the proper application of technology and information, traditional insurers can quickly adapt to changing industry dynamics and win against these next-gen insurers.
This on-demand webinar will explore how to:
Reinvent traditional insurance products and services
Drive customer experience improvements by unlocking more information and using it faster
Mitigate risk and ensure successful modernization efforts
Discover how traditional insurers can win more business by delivering customer-centric underwriting, policy administration, and claims management applications faster than ever.
Close the AI Action Gap in Financial ServicesCognizant
Banks and financial institutions are making strides with artificial intelligence -- but they've been slow to scale it. Here are four steps to realize AI's full potential throughout the enterprise.
Madison Park Group Member Management Software Market Update - Nonprofit & Ass...Madison Park Group
We are pleased to present our review of the nonprofit & association software market for the first half of 2018.
Madison Park Group is a unique investment banking firm that takes a "strategy first" approach to advising software companies. Our partners have developed and advised numerous successful companies as operators, investors and investment bankers.
Jonathan Adler and Michael Magruder spearhead the firm's efforts in the broader member management software market.
How digital technologies can change hospitals globally: https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/global-digital-hospital-of-the-future.html?icid=target-homepage-promo-lshc-digital-hospital
It should be my presentation at R in Insurance CASS conference at 15th July 2013. It shows how R can be successfully applied to price life, non - life and reinsurance contracts.
Technology Innovation Trends In Insurance | Navdeep Arora Navdeep Arora
This presentation talks about ‘Technology Innovation Trends In Insurance’. This covers seven themes that are reshaping supply & demand of #insurance globally, premium & profit pools are migrating across the #insurance value chain. Explains how value creation opportunities are different across mature & developing markets, how #technology & digital capabilities that target value chain effectiveness (not just scale efficiencies) offer compelling #investment opportunities. Also has #InsurTech levers and start-up examples in non-life insurance and life insurance.
Market Research Reports, Inc. has announced the addition of “Big Data in Global Retail Market 2021” research report to their offering. See more at - http://mrr.cm/U6V
What Does Good Risk Culture Actually Look Like?accenture
At RiskMinds International 2015, Rafael Gomes presented "What Does Good Risk Culture Actually Look Like?" and addressed risk culture and conduct in practice. Get more information from Rafael’s blog post, which describes how financial services can recognize, measure, and communicate good risk culture: http://bit.ly/1RFBrzF
Applied Innovation for the UnorganizationCapgemini
Many organizations are not designed to innovate effectively or fast. They lack the necessary processes, talent, risk tolerance and leadership alignment, as well as a culture that encourages, rewards and promotes innovation. In order to innovate effectively, the traditional organizations need to embrace the “Unorganization” structure to meet today’s market demands.
Capgemini’s new and tested global Applied Innovation Exchange network, breaks the traditional model; instead taking a multi-party collaborative approach and balancing external and internal thinking to assist organizations in cultivating and applying innovation within their enterprises, and specifically to their most strategic business processes.
Presented by Lanny Cohen, Group Chief Technology Officer, Capgemini at Sogeti’s VINT Symposium
Learn more on Capgemini’s Applied Innovation Exchanges: https://www.capgemini.com/applied-innovation-exchange
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...Capgemini
Are banks and insurers a safe pair of hands when it comes to customer data? Our global survey of more than 180 senior data privacy and security professionals – as well as 7,600 consumers – found that less than a third (29%) of these organizations offer both strong data privacy practices and a sound security strategy. Just one in five (21%) are highly confident that they can detect a cybersecurity breach.
This picture has so far not unduly affected consumers’ perceptions of the industry. We found that 83% of consumers trust banks and insurers when it comes to data. And while one in four institutions have reported being victim of a hack, just 3% of consumers believe their own bank or insurer has ever been breached. However, with the pending General Data Protection Regulation (GDPR) regulations, this trust factor is likely to change as transparency increases. Financial organizations have to reveal a data breach 72 hours after the incident.
Banks and insurance firms have a clear incentive therefore to fortify their defences. As well as avoiding the prohibitive fines and penalties that will result from compromised data, protecting privacy offers a strategic business advantage. Addressing security concerns will drive greater adoption of low-cost digital channels. We found that security concerns deter nearly half of consumers (47%) from using digital channels. It will also reduce churn and attract competitors’ customers – 74% of consumers would switch their bank or insurer in the event of a data breach.
Preparing to be a trusted data steward is no easy task, however. It means raising the bar on multiple dimensions:
• Aligning data practices with consumers’ expectations
• Finding innovative ways of providing non-intrusive security to consumers
• Building the capabilities required to monitor cyber risks on a real-time basis
• Revisiting the data governance model.
Building your reputation for data privacy and robust security is definitely challenging. But, those who strike the right chord with consumers will enjoy a competitive advantage over their peers. The winners will be those who triumph in the trust game.
Can Financial Institutions be the next Digital Masters? Capgemini says YESCapgemini
With Millennials coming of age it is even more important than ever for Financial Institutions to provide a strong digital experience. In order to deliver that Financial Institutions need to become Digital Masters - accelerating their digital business, turn data into insights, transform the customer experience and embrace the mobile mind shift.
Capgemini’s Trends in Transformation powered by HPE is your Jedi Master. Are you ready to become a Digital Master? Join us to start your journey.
Presented at HPE Discover Las Vegas 2016.
Guide to Data Analytics: The Trend That's Reshaping the Insurance IndustryApplied Systems
Information you need is in your management system –- you just have to understand how to use it. Read this guide to learn what data analytics is, how it's impacting the insurance industry, why it's important for independent agencies and brokerages, and how to create your own data analytics strategy.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
Hosted by Digital Insurance
The insurance industry is at a tipping point: A next-generation of insurer is disrupting traditional business models by building future-proof business systems that power superior customer experiences and generate new revenue streams.
With the proper application of technology and information, traditional insurers can quickly adapt to changing industry dynamics and win against these next-gen insurers.
This on-demand webinar will explore how to:
Reinvent traditional insurance products and services
Drive customer experience improvements by unlocking more information and using it faster
Mitigate risk and ensure successful modernization efforts
Discover how traditional insurers can win more business by delivering customer-centric underwriting, policy administration, and claims management applications faster than ever.
Close the AI Action Gap in Financial ServicesCognizant
Banks and financial institutions are making strides with artificial intelligence -- but they've been slow to scale it. Here are four steps to realize AI's full potential throughout the enterprise.
Madison Park Group Member Management Software Market Update - Nonprofit & Ass...Madison Park Group
We are pleased to present our review of the nonprofit & association software market for the first half of 2018.
Madison Park Group is a unique investment banking firm that takes a "strategy first" approach to advising software companies. Our partners have developed and advised numerous successful companies as operators, investors and investment bankers.
Jonathan Adler and Michael Magruder spearhead the firm's efforts in the broader member management software market.
How digital technologies can change hospitals globally: https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/global-digital-hospital-of-the-future.html?icid=target-homepage-promo-lshc-digital-hospital
It should be my presentation at R in Insurance CASS conference at 15th July 2013. It shows how R can be successfully applied to price life, non - life and reinsurance contracts.
Major corporate groups with sophisticated risk management frameworks often utilise self-funding and captives to manage the volatility of their financial results.
Loss reserving estimates the ultimate cost of historical self-insured claims to help clients ensure adequate funding for long term liabilities.
Wind flow simulations on forested zone have been performed with Computational Fluid Dynamics (CFD) software meteodyn WT, which allows introducing a custom forest canopy model. The influence of parameter changes on results is investigated. The calibration of model parameters is done by minimizing the error between the CFD results and the vertical wind profiles given by the European standard Eurocode 1 (EC1), applied to standard terrains for high roughness cases. The calibrated model shows good coherence with EC1. To check the validity of the forest modeling in the real case, CFD simulation has been performed on a site with heterogeneous forest covering. The computed wind characteristics are then compared to met mast measurement. The comparison shows good agreement on wind shear and turbulence intensity between the simulation results and the measured data.
Learn the built-in mathematical functions in R. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
A project to create at least two predictive Machine Learning models to analyze a business situation.
Description of Business Situation - The hiring managers of Pas de Poissen sought the guidance of a consulting firm to determine which of the nationality of the foreign workforce, entering Canada, would have the highest probability that a judge would approve their appeal to remain, and subsequently be employable in the country.
Establishing a model to best determine which candidates to hire provided exceptional cost saving opportunities. In the past, if the company was informed that one of their new foreign national workers was not granted an appeal, and was actively on a fishing deployment, at times lasting for over 45 days, the trawler was forced to return to port. A vessel having to return equated to missed opportunistic revenue, as it could no longer fish, and unexpected fuel expenses to return to homeport. Furthermore, the penalty for knowing employing an illegal foreign worker was harsh from both the Canadian and U.S fisheries enforcement agencies.
Deliverables -
A description of the business problem we are addressing
How and where we obtained the data, and the steps we went through to insure that it was "clean"
A summary of modeling steps, with reference to the predictive models in the project file
Assessment of the accuracy of models, with reference to project file results
Our interpretation of the results of our analysis
What we learnt, and how might it inform the business situation that we chose to analyze
Source: Rattle Library
Name: “Green: Refugee Appeal”
Predictive Models : "Forest Model" and "Boosting Model"
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
It seems the world is all fascinated with amazing insight from Big Data... but we all know what really matters is the VALUE unlocked from those insights...
Too often we assume that smart people will know what to do if the Masters of Data Science unloads new wisdom on the business. The reality is we have to empower the ultimate people who have to act on these new insights with processes and business levers that also smarter.
In this presentation, we explore what is the difference between insight and value... the difference between a finding that is interesting, and a finding that has impact.
The presentation captures a career of learnings in Big Data and Advanced Analytics as the Lead Partner who established and led Deloitte's Advanced Analytics practice in WA
Original: Lean Data Model Storming for the Agile EnterpriseDaniel Upton
This original publication, aimed at data project leaders, describes a set of methods for agile modeling and delivery of an enterprise data warehouse, which together make it quicker to deliver, faster to load, and more easily adaptable to unexpected changes in source data, business rules or reporting/analytic requirements.
With this set of methods, the parts of data warehouse development that used to be the most resistant to sprint-sized / agile work breakdown -- data modeling and ETL -- are now completely agile, so that this tasking, too, can now be sized purely based on customer requirements, rather than the dictates of a traditional data warehouse architecture.
If You Are Not Embedding Analytics Into Your Day To Day Processes, You Are Do...Dell World
Becoming data-driven requires analytics to be embedded throughout the organization in different functional areas and different operational processes. But how do you provide more and more people with the ability to run any analytics on any data anywhere– without breaking the bank? In this session, you’ll see real-world examples of Dell customers who have successfully embedded analytics across processes and operations to drive innovation.We will also demonstrate how embedding analytics enables faster innovation and improves collaboration between data scientists, business analysts, and business stakeholders, leading to a competitive advantage.
In our sixth annual Technology Trends report, we outline eight trends that could potentially disrupt the way businesses engage their customers, how work gets done, and how markets and industries evolve.
Stay ahead of the technology curve with Deloitte's Tech Trends reports: http://deloi.tt/2wfFfFB
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Presented to eRum (Budapest), May 2018
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe the doAzureParallel package, a backend to the "foreach" package that automates the process of spawning a cluster of virtual machines in the Azure cloud to process iterations in parallel. This will include an example of optimizing hyperparameters for a predictive model using the "caret" package.
By David Smith. Presented at Microsoft Build (Seattle), May 7 2018.
Your data scientists have created predictive models using open-source tools, proprietary software, or some combination of both, and now you are interested in lifting and shifting those models to the cloud. In this talk, I'll describe how data scientists can transition their existing workflows — while using mostly the same tools and processes — to train and deploy machine learning models based on open source frameworks to Azure. I'll provide guidance on keeping connections to data sources up-to-date, evaluating and monitoring models, and deploying applications that make use of those models.
Presentation delivered by David Smith to NY R Conference https://www.rstats.nyc/, April 2018:
Minecraft is an open-world creativity game, and a hit with kids. To get kids interested in learning to program with R, we created the "miner" package. This package is a collection of simple functions that allow you to connect with a Minecraft instance, manipulate the world within by creating blocks and controlling the player, and to detect events within the world and react accordingly.
The miner package is intended mainly for kids, to inspire them to learn R while playing Minecraft. But the development of the package also provides some useful insights into how to build an R package to interface with a persistent API, and how to instruct others on its use. In this talk I'll describe how to set up your own Minecraft server, and how to use and extend the package. I'll also provide a few examples of the package in action in a live Minecraft session.
While Python is a widely-used tool for AI development, in this talk I'll make the case for considering R as a platform for developing models for intelligent applications. Firstly, R provides a first-class experience working deep learning frameworks with its keras integration. Equally importantly, it provides the most comprehensive suite of statistical data analysis tools, which are extremely useful for many intelligent applications such as transfer learning. I'll give a few high-level examples in this talk, and we'll go into further detail in the accompanying interactive code lab.
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe several techniques available in R to speed up workloads like these, by running multiple iterations simultaneously, in parallel.
Many of these techniques require the use of a cluster of machines running R, and I'll provide examples of using cloud-based services to provision clusters for parallel computations. In particular, I will describe how you can use the SparklyR package to distribute data manipulations using the dplyr syntax, on a cluster of servers provisioned in the Azure cloud.
Presented by David Smith at Data Day Texas in Austin, January 27 2018.
A look at the changing perceptions of R, from the early days of the R project to today. Microsoft sponsor talk, presented by David Smith to the useR!2017 conference in Brussels, July 5 2017.
Predicting Loan Delinquency at One Million Transactions per SecondRevolution Analytics
Real-time applications of predictive models must be able to generate predictions at the rate that transactions are generated. Previously, such applications of models trained using R needed to be converted to other languages like C++ or Java to achieve the required throughput. In this talk, I’ll describe how to use the in-database R processing capabilities of Microsoft R Server to detect fraud in a SQL Server database of loan records at a rate exceeding one million transactions per second. I will also show the process of training the underlying gradient-boosted tree model on a large training set using the out-of-memory algorithms of Microsoft R.
Presented by David Smith at The Data Science Summit, Chicago, April 20 2017.
The ability to independently reproduce results is a critical issue within the scientific community today, and is equally important for collaboration and compliance in business. In this talk, I'll introduce several features available in R that help you make reproducibility a standard part of your data science workflow. The talk will include tips on working with data and files, combining code and output, and managing R's changing package ecosystem.
Presented by David Smith, R Community Lead (Microsoft), at Monktoberfest October 2016.
The value of open source isn’t just in the software itself. The communities that form around open source software provide just as much value and sometimes even more: in ongoing development, in documentation, in support, in marketing, and as a supply of ready-trained employees. Companies who build on open source tend to focus on the software, but neglect communities at their peril.
In this talk, I share some of my experiences in building community for an open-source software company, Revolution Analytics, and perspectives since the acquisition by Microsoft in 2015.
R is more than just a language. Many of the reasons why R has become such a popular tool for data science come from the ecosystem surrounding the R project. R users benefit from the many resources and packages created by the community, while commercial companies (including Microsoft) provide tools to extend and support R, and services to help people use R.
In this talk, I will give an overview of the R Ecosystem and describe how it has been a critical component of R’s success, and include several examples of Microsoft’s contributions to the ecosystem.
(Presented to EARL London, September 2016)
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
Hadoop is famously scalable. Cloud Computing is famously scalable. R – the thriving and extensible open source Data Science software – not so much. But what if we seamlessly combined Hadoop, Cloud Computing, and R to create a scalable Data Science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based Web Service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms at scale.
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Yes of course, you can easily start mining pi network coin today and sell to legit pi vendors in the United States.
Here the telegram contact of my personal vendor.
@Pi_vendor_247
#pi network #pi coins #legit #passive income
#US
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
1. Actuarial Science as Data Science
Actuarial Modeling in R
Revolution Analytics Webinar Jim Guszcza, FCAS, MAAA
Deloitte Consulting LLP
University of Wisconsin-Madison
March 28, 2012
3. Agenda
Introduction
Actuarial Science and Data Science
R Background
Case Studies
• Fitting a complex size of loss model
• Loss Reserving
• Bayesian Hierarchical Modeling
• Revolution: Tweedie Regression on big data