This document provides an overview of machine learning and how it can be used by organizations. It begins with definitions of key concepts like data science, advanced analytics, artificial intelligence, statistics, and machine learning. It then discusses why machine learning has become more feasible in recent years due to increases in data, computing power, and attention from researchers. Examples are given of common machine learning applications in areas like computer vision, natural language processing, and personalized recommendations. The document outlines the machine learning process of creating and evaluating statistical models on data to make predictions. It also discusses the roles of people, processes, technology, and data in successfully applying machine learning within an organization.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Machine Learning with Azure and Databricks Virtual WorkshopCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
This is the third in our three part webinar series on cloud-enabled customer insights. Learn how to scale your customer analytics operations up and out with Microsoft Azure Data Lake.
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Machine Learning with Azure and Databricks Virtual WorkshopCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
This is the third in our three part webinar series on cloud-enabled customer insights. Learn how to scale your customer analytics operations up and out with Microsoft Azure Data Lake.
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.
How to use your data science team: Becoming a data-driven organizationYael Garten
Talk given at Strata Hadoop World conference March 2016.
http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305
In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.
The Shifting Landscape of Data IntegrationDATAVERSITY
Enterprises and organizations from every industry and scale are working to leverage data to achieve their strategic objectives — whether they are to be more profitable, effective, risk-tolerant, prepared, sustainable, and/or adaptable in an ever-changing world. Data has exploded in volume during the last decade as humans and machines alike produce data at an exponential pace. Also, exciting technologies have emerged around that data to improve our abilities and capabilities around what we can do with data.
Behind this data revolution, there are forces at work, causing enterprises to shift the way they leverage data and accelerate the demand for leverageable data. Organizations (and the climates in which they operate) are becoming more and more complex. They are also becoming increasingly digital and, thus, dependent on how data informs, transforms, and automates their operations and decisions. With increased digitization comes an increased need for both scale and agility at scale.
In this session, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which platforms have made, or are in the process of making, the leap to this new generation of Data Management and integration capabilities.
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Communicate Data with the Right VisualizationsAnalytics8
While there is an art to dashboard design, the science behind visualization is more important than most people realize. In these webinar slides, we show how to approach data visualization in a systematic way that will unlock the story your data holds.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
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.
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
Supply Chain Intelligence and Analytics Executive Guidelines for SuccessHalo BI
Learn from industry experts about the future of supply chain analytics in 2016. Understand the main concerns of executives in the coming year and where the focus will be across the entire supply chain.
Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Dee...Formulatedby
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Data Science Salon: Enabling self-service predictive analytics at BidtellectFormulatedby
Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold.
Next DSS MIA Event - https://datascience.salon/miami/
During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.
How to use your data science team: Becoming a data-driven organizationYael Garten
Talk given at Strata Hadoop World conference March 2016.
http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305
In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.
The Shifting Landscape of Data IntegrationDATAVERSITY
Enterprises and organizations from every industry and scale are working to leverage data to achieve their strategic objectives — whether they are to be more profitable, effective, risk-tolerant, prepared, sustainable, and/or adaptable in an ever-changing world. Data has exploded in volume during the last decade as humans and machines alike produce data at an exponential pace. Also, exciting technologies have emerged around that data to improve our abilities and capabilities around what we can do with data.
Behind this data revolution, there are forces at work, causing enterprises to shift the way they leverage data and accelerate the demand for leverageable data. Organizations (and the climates in which they operate) are becoming more and more complex. They are also becoming increasingly digital and, thus, dependent on how data informs, transforms, and automates their operations and decisions. With increased digitization comes an increased need for both scale and agility at scale.
In this session, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which platforms have made, or are in the process of making, the leap to this new generation of Data Management and integration capabilities.
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Communicate Data with the Right VisualizationsAnalytics8
While there is an art to dashboard design, the science behind visualization is more important than most people realize. In these webinar slides, we show how to approach data visualization in a systematic way that will unlock the story your data holds.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
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.
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
Supply Chain Intelligence and Analytics Executive Guidelines for SuccessHalo BI
Learn from industry experts about the future of supply chain analytics in 2016. Understand the main concerns of executives in the coming year and where the focus will be across the entire supply chain.
Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Dee...Formulatedby
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Data Science Salon: Enabling self-service predictive analytics at BidtellectFormulatedby
Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold.
Next DSS MIA Event - https://datascience.salon/miami/
During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Data Science for Business Managers - An intro to ROI for predictive analyticsAkin Osman Kazakci
This module addresses critical business aspects related to launching a predictive analytics project. How to establish the relationship with business KPIs is discussed. A notion of data hunt, for planning & acquiring external data for better predictions is introduced. Model quality and it's role for ROI of data and prediction tasks are explained. The module is concluded with a glimpse on how collaborative data challenges can improve predictive model quality in no time.
Economics & Statistics Insights in Data Science by DataPerts TechnologiesRavindra Panwar
DATA is an inevitable part of our life today. These tiny pieces of information from which we derive valuable insights
have their genesis in the domain of ECONOMICS and STATISTICS.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
THINKING ABOUT THINKING
Audience: PM & BA
Level: All
Date: May 26
Time: 11:30 AM - 12:30 PM
Description
Thinking is a big part of a Project Manager’s and Business Analyst's job. But how often have you spent time thinking about thinking? This presentation looks at thinking as a critical soft skill for project managers and how a disciplined approach to thinking improves you effectiveness as a change agent for the company in the role of project manager. The presentation will discuss the Thinking Hats, Five Types of Thinking, and brush into the entire world of Business Analytics. The presentation focuses on how the skills of Strategic Analysis, Tactical Analysis, Predictive Analysis, Data mining work together for the complete business management cycle. To add to the thinking equation, the session will explore the power of Social Media sentiment and how the way people "feel" about things is an important factor in the business equation. Think about it !!!!
1. Participants will understand the relationship between planning, analysis, problem solving, decision making and thinking.
2. Students will be able to explain an "Adapting to Whats Happening Model" that includes Data Recording, Strategic Analysis, Tactical Analysis, Predictive Analysis, and Social Media Sentiment. And how it impacts the business.
3. Students will explore various factors of human bias and how that impacts thinking. The student will understand that bias cannot not be completely eliminated, but should be embraced as a human factor in any thinking exercise. The student will understand that personal perspective/bias is a factor, but not THE factor in thinking.
Slides for three presentations Coolblue's Behind the Scenes Data Science event on 2018-03-22
Speakers:
- Andres Martinez (Data Science @ Coolblue)
- Matthias Schuurmans (forecasts)
- Daan Marechal (recommendations)
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
BrandsLab Marketing Performance Optimization Session 1 | Off the Beaten Path ...Ebiquity-NA
This session uncovers the most under-utilized paths to multi-channel analytics success. From establishing governance structure to identifying technologies, we will help you think more strategically about your business.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Weekly webcast of clario Analytics.
Advanced analytics software goes beyond the reports and dashboard capabilities of traditional BI (business intelligence) tools, helping users answer questions about future events and explore "what-if" scenarios, as well as pull together and analyze unstructured information from a variety of sources.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
How to Monetize Your Data Assets and Gain a Competitive AdvantageCCG
Join us for this session where Doug Laney will share insights from his best-selling book, Infonomics, about how organizations can actually treat information as an enterprise asset.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Power BI Advanced Data Modeling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Solution Architect, Doug McClurg, to learn how to create professional, frustration-free data models that engage your customers.
Join Brian Beesley, Director of Data Science, for an executive-level tour of AI capabilities. Get an inside peek at how others have used AI, and learn how you can harness the power of AI to transform your business.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
Advance Data Visualization and Storytelling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Senior BI Architect, Martin Rivera, taking you through a journey of advanced data visualization and storytelling.
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
Business intelligence dashboards and data visualizations serve as a launching point for better business decision making. Learn how you can leverage Power BI to easily build reports and dashboards with interactive visualizations.
Data Governance and MDM | Profisse, Microsoft, and CCGCCG
CCG will introduce a methodology and framework for DG that allows organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights. In addition, Profisee will introduce a popular component of data governance, MDM.
Enable Better Decision Making with Power BI Visualizations & Modern Data EstateCCG
Self-service BI empowers users to reach analytic outputs through data visualizations and reporting tools. Solution Architect and Cloud Solution Specialist, James McAuliffe, will be taking you through a journey of Azure's Modern Data Estate.
Data Governance with Profisee, Microsoft & CCG CCG
Review CCG's methodology and framework for DG that allows organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. Review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
In addition, Profisee introduces a popular component of data governance, MDM. Profisee is a Master Data Management software company making it easy and affordable for companies of all sizes to build a trusted foundation of data across the enterprise. Leveraging an MDM strategy within the context of Data Governance drives organizational alignment, ensures data quality, and accelerates Digital Transformation
[Webinar] Top Power BI Updates You *Acutally* Need to Know CCG
1)Summary of the over 25 feature improvements made by Power BI in 2019
2) Top ways to leverage the changes in functionality
3) Ways to get buy-in and further utilize your Microsoft Power BI investment
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. CCG
Analytics Solutions & Services
DATA
MANAGEMENT
Data & analytics consultants with a passion for helping clients
overcome business challenges & increase performance by
leveraging modern analytic solutions.
BUSINESS
ANALYTICS
DATA
STRATEGY
3. VOICES OF OUR CUSTOMERS
“CCG to brought the expertise and the vision of to help us execute, to provide
visibility to the data in a manner that we can use it faster.”
- Gary Gray, Business Solutions Executive, Corsicana Mattress Company
“The people we talked to know us. CCG wasn’t trying to fit us into a boilerplate
template but prescribe a tailored solution. Their RapidRoadmap was the basis of
our BI Strategy for the next two years.”
- Kevin Davis, Sr. Director of BI, Kforce
“Many times with CCG, we come to the table with questions or ideas and within a
couple of days or weeks the team comes back with above and beyond what we
actually asked for. They care.”
- Chris Fitzpatrick, Vice President of Business Analytics & Strategy, vineyard vines
“"I'm amazed at the talent at CCG, not just the skillset - they're really good people.
We've already referred them once and will do so again!”
- CIO, Ruth’s Chris Hospitality Group
4. AGENDA
What is Machine Learning?
Why should anyone care about
machine learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?
5. Advanced Analytics (“AA”) enable predictive and prescriptive uses of data by
applying sophisticated math and statistics to automate parts of the analysis.
What is Advanced Analytics?
Traditional analytics focuses on
understanding and explaining the
data that has been collected.
AA focuses on generating new
data in the form of predictions or
decisions, and going the extra step
to automate decision-making
when possible.
6. Advanced Analytics deal with making “best guesses” faster, better, and
more consistent than relying on human SMEs.
Provide insights on existing data using:
• Raw data points
• Summaries of data
• Calculations across existing data fields
• KPIs
The data reported are historical or current facts.
Generally requires the application of basic
mathematics or arithmetic.
Generate new data, including:
• Predicted future values
• Best guesses of missing values
• Suggested next steps
• Categorizations
The data generated are “best guesses” and
contain some uncertainty.
Requires the application of advanced
mathematics, statistics and computing principles.
Traditional Analytics Advanced Analytics
Traditional vs. Advanced Analytics
7. Machine Learning is one of several tools that enable advanced
analytics. It’s more of a HOW than a WHAT.
What is Machine Learning?
Data Science
A broad process for generating insights
that may involve data ingestion from
one or many sources (including external
data, streaming data, or big data), data
processing and cleansing, model
generation using statistical or machine
learning approaches, model selection,
model deployment and maintenance,
and visualization of data.
Advanced Analytics
Apply data science to predictive (what
will happen?) or prescriptive (what
should we do?) business use cases.
Artificial Intelligence /
Cognitive Computing
Apply data science to approximate
human intuition and decision making
(e.g. strategy, creativity, planning) or
human sensory function s (e.g.
computer vision, natural language
understanding, etc.)
Statistics
A branch of math for generating
descriptions of or inferences about a
population, often based on samples
of the population. Inferences may
take the form of “models,” which
are equations that approximate the
data’s inherent relationships.
Machine Learning
Combines computer science with
math concepts to generate models
by rapidly iterating on large
datasets.
Other Analytics Disciplines
(e.g. Data Engineering, Visualization)
Disciplines Process Outputs
Automation /
Robotics /
Intelligent
Devices
Actions
Strategy /
Operations
8. AGENDA
What is Machine Learning?
Why should anyone care about
machine learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?
9. The concepts in Machine Learning are not new.
How has Machine Learning Evolved?
https://www.quantinsti.com/blog/machine-learning-basics
another human.
10. Even though the concepts are decades old,
machine learning has only become feasible at scale in recent years.
Why Machine Learning Now?
Flood of data and decreasing costs of storage
Increasing computational power
Increased attention from researchers
Growth of open source technologies
Support from industries
11. Machine Learning has tons of useful applications you already encounter or
hear about every day.
Analyzing
Images
Understanding
Language
Forming &
Executing Strategy
Personalized
Recommendations
Autonomous
Decisions
Predicting
Asset Values
How is Machine Learning used?
12. Machine Learning isn’t just applicable to high tech.
There are suitable use cases present in most business sectors.
Where is Machine Learning used?
Healthcare
• Claims Fraud
• Real-time mortality risk
for ICU patients
• Response Adapted
Radiotherapy
• Predicting patient
medication adherence
• Translational/precision
medicine
Finance
• Foreclosure/credit risk
• Risk analysis
• Fraud detection
• Demand forecasting
• Anti Money Laundering
• Algorithmic trading
Energy
• Resource allocation
• Load forecasting
• Grid optimization
• Robotics
• Anomaly detection
• Image recognition
• Predictive maintenance
Retail
• Single view of customer
• Customer service analysis
• Inventory planning
• Social media analysis
• Lead scoring
• Marketing campaign
evaluation
13. Machine learning sits at the intersection of statistics and computer science to
help businesses make decisions.
Why Machine Learning Now?
Computational
Power
Statistics
Predictive & Prescriptive Decision Support
Faster More
Accurate
More
PowerfulSelf-Improving Always-On
14. AGENDA
What is Machine Learning?
Why should anyone care about
machine learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?
15. A model is a repeatable, data-driven approach to making a best guess.
It does this by formalizing mathematical relationships between data in the form of either:
– Rules (e.g. predict applicants will default on a loan if Credit Score < 700 and Debt to Income Ratio > 30%)
– Or an equation (e.g. predict Home Price = 100*Square Footage + 2*Average Income in the Area)
Machine Learning works by using “algorithms” to generate “models.”
How does Machine Learning work?
Data Model Statistical Model
16. In the past we’ve told computers how to use data to a answer our
questions.
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month sales =
(prior month +
2 months prior +
3 months prior)
/ 3
Answer
This month’s sales = $3MM?
What’s a model?
17. Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
But we’ve found that if we give the machine historic facts, we can let it find
the right program / model to plug in for future answers.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
What’s a model?
18. Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Once we have our machine-defined program, we can use it with new data to
make better predictions.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
New Data
Prior month sales: $8MM
2 months prior: $6MM
3 months prior: $8MM
Answer
This month’s sales = $5MM
What’s a model?
19. A defined set of steps for solving a problem
Often involves repeating steps
May or may not have an ending condition
– The problem is solved to our satisfaction
• For example – stop when the last 4 iterations have been 95% accurate or better
– The problem hasn’t been solved but we don’t seem to be getting any closer to solving it
• For example – stop if the last 10 iterations have not seen any improvement in accuracy
– The process has run for a long time
• For example – stop after the program has run for 12 hours, regardless of whether progress is still being made
The word algorithm gets used a lot, but it isn’t always defined.
What is an algorithm?
20. Collect the data and randomly create initial decision rules.
Design a method for measurably evaluating how good or bad your hypothesis is.
Update your hypothesis in a way that marginally improves the performance of your decision rules.
Continue this process until either you are satisfied with the results, or your hypothesis can’t improve
anymore with the data available.
Almost all machine learning algorithms follow the same general pattern.
Create a
hypothesis
Evaluate the
hypothesis
Adjust the
hypothesis
Repeat until
convergence
What is an algorithm?
21. AGENDA
What is Machine Learning?
Why should anyone care about
machine learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?
22. There are two main families of algorithms to choose from.
Supervised Learning Unsupervised Learning
There aren’t necessarily “right answers,” we just want to
get a better understanding of our data.
We know the “right answers” for some of the scenarios.
– We may have history we can look back on
– We may be hoping to replicate human decision making
23. Supervised or Unsupervised?
Predict our profits next quarter. Supervised
Identify the number written on a check.
Group our customers into segments.
Supervised
Unsupervised
Predict a user’s rating for a given product. Supervised
Find the most important variables in a dataset. Unsupervised
Identify credit card transactions that are out of the ordinary. Unsupervised
24. Now let’s walk through two of the most popular machine learning
approaches and discuss how the algorithms are applied.
How does an algorithm really work for businesses?
Classification Clustering
25. Use classification when you want to guess a non-numeric value, like a
yes/no answer. We will take a decision tree approach.
Everyone will repay their loan.
Create a
hypothesis
20 outstanding loans
26. Use classification when you want to guess a non-numeric value, like a
yes/no answer. We will take a decision tree approach.
Calculate accuracy as the % of predictions that are correct based on your current set of rules.
Evaluate the
hypothesis
20 outstanding loans
12 repaid, 8 defaulted
Accuracy = 12/20 = 60%
27. Income > 60kIncome < 60k
Use classification when you want to guess a non-numeric value, like a
yes/no answer. We will take a decision tree approach.
Find the next branch by looking for the data split that would have the biggest impact on the purity of
each node. There are several ways to do this mathematically (Gini Index, Information Gain, Chi-
Square).
Adjust the
hypothesis
20 outstanding loans20 outstanding loans
Credit Score > 700Credit Score < 700
20 outstanding loans
DTI > 40%DTI < 40%
70%50%
60% weighted
71%53%
59% weighted
80%73%
75% weighted
28. Use classification when you want to guess a non-numeric value, like a
yes/no answer. We will take a decision tree approach.
Repeat the process for each of your new “leaf” nodes. Stop when you reach an acceptable level of
accuracy, or when your accuracy begins getting worse with independent data.
Repeat until
convergence
20 outstanding loans
DTI > 40%DTI < 40%
Credit Score > 700Credit Score < 700Income > $60kIncome < $60k
100%50% 100%100%
80% weighted
29. Classification is used for lots of problems that copy human intuition. Think
about how you classify information to identify these images!
These use cases are obviously
more complex than our
simple decision tree, but with
more advanced approaches
like convolutional neural
networks these pictures can
definitely be classified by a
machine.
30. Use clustering when there’s no “correct” classification, but you still want to
assign individuals to groups. This algorithm is called k-means clustering.
Imagine Marketing
has asked you to
split these customers
into 3 groups.
How would you do
it?
31. Use clustering when there’s no “correct” classification, but you still want to
assign individuals to groups. This algorithm is called k-means clustering.
I can segment my customers by assigning them to 3 groups. We’ll set down 3 random “anchors” and
assign each customer to its closest anchor.
Create a
hypothesis
32. Use clustering when there’s no “correct” classification, but you still want to
assign individuals to groups. This algorithm is called k-means clustering.
Move the anchors to the center of each cluster. Count how many anchors are actually closer to one of
the other anchors.
Evaluate the
hypothesis
33. Use clustering when there’s no “correct” classification, but you still want to
assign individuals to groups. This algorithm is called k-means clustering.
Re-assign each customer to the group corresponding to the center they’re closest to.
Adjust the
hypothesis
34. Use clustering when there’s no “correct” classification, but you still want to
assign individuals to groups. This algorithm is called k-means clustering.
Repeat until
convergence
Move the anchors again. Continue re-assigning customers and moving the anchors until the anchors
stop moving.
35. This is just the tip of the iceberg. There are several
algorithms available for various types of problems.
36. AGENDA
What is Machine Learning?
Why should anyone care about
machine learning?
How does Machine Learning
work?
Ok but how does it really work?
How can an organization use
Machine Learning?
37. Delivering analytics with Machine Learning requires alignment
across people, process, technology, and data.
Engaging with Machine Learning
Image inspired by Microsoft
People
Process Technology
Data
Guide
Support
Enable
38. Data scientists combine broad skills to integrate data, build
models, and drive business value.
People
Process Technology
Data
39. Let’s look at the Microsoft Team Data Science Process to see how
data scientists spend their time.
People
Process Technology
Data
40. Traditional Analytics
The outputs of the process can be used in traditional analytics,
analyzed directly, or fed into automated decision-making.
Store and access data. Filter and aggregate it. Visualize it.
Show it to the business
so they can take action.
Machine Learning
Filter and aggregate it.
1
𝑁
𝑛=1
𝑁
𝑥
Create a model. Generate new data
(predictions, etc.).
The new data can be
stored with the rest of the
data for use in analytics.
Or it can be visualized
directly to gain insights.
Or it can automate
decisions or actions,
allowing better processes
to run faster and 24/7.
People
Process Technology
Data
41. Model performance naturally degrades over time as relationships in data
shift. Model maintenance is critical to using models on an ongoing basis.
It’s 1995.
Roughly 25% of Americans
own cell phones.
Imagine you want to build a model
predicting an individual’s income. This
model would justifiably give a significant
premium to cell phone owners.
It’s 2007.
Roughly 70% of Americans
own cell phones.
Ownership still has some significance, but
the premium for cell phone ownership
should be much smaller.
A machine learning model would
adapt itself over time, but a hand-
built model would need its
parameters adjusted manually.
It’s 2019.
Over 95% of Americans own
cell phones.
At this point cell phone ownership should
probably be dropped in favor of other data,
like ownership of an electric car.
Even machine learning models can only
learn from the data at their disposal, so
the data acquisition pipeline requires
updates regardless of the approach.
People
Process Technology
Data
42. The sources of data for use in data science can be broad.
People
Process Technology
Data
Data
Warehouses
•Curated &
Governed data
•Big data
•Cloud or on-prem
Data Lakes
•Unstructured &
Semi-structured
data
•Streaming data
•Partially curated
Externally
Procured
Data
•May be purchased
from 3rd party
providers
•May be scraped
from the web
•May require
designing research
experiments
Data scientists typically have the
programming and data integration skills to
use data from anywhere it can be found.
43. The Microsoft technology stack provides a holistic
solution to your Machine Learning needs.
People
Process Technology
Data
45. We can work with your business to deliver custom predictive and
prescriptive analytics across the lifecycle.
What can CCG do?
Use Case Definition
• Develop a backlog of
predictive and
prescriptive use cases
• Refine and prioritize use
cases by value
• Develop a predictive
roadmap
Model Development
• Aggregate data from
across internal and
external data sources
• Develop and test
multiple models to find
the best approach to
making predictions
Model Maintenance
• Monitor and maintain
statistical models to
sustain predictive power
• Develop a model telemetry
dashboard
• Test model design changes
to improve predictive
power
Model Governance & Processes
• Assess existing Data Science capabilities
• Develop standards and processes to help guide data science output
• Build a Data Science Center of Excellence
Model Deployment
• Customize and deploy
pre-existing models from
Azure Cognitive Services
• Deploy custom model as
an API or batch job, or
support deployment in
existing systems
Rapid Insight Prototype Offering Model as a Service Subscription Offering
46. CCG’s Rapid Insight Solution
Actionable Backlog
– Of use cases ripe for predictive
analytics to transform your
business
Detailed Readouts
– The materials we leave behind
will include extensive analysis
of our methodology, findings,
and recommendations
Ownership of the Model
– Just because the project ends
doesn’t mean the model stops
working. Unlike other managed
service providers, what we
produce on your behalf is yours
to keep
Identify Use Cases
– By holding a workshop with
process SMEs to identify
opportunities to supercharge the
business
Summarize the Findings
– So you can understand the
model’s outputs and begin
taking action on what we’ve
learned
Develop a Prototype Model
– To generate forecasts,
classifications, or exploratory
analysis for one of your use
cases using an industry-standard
tool like Azure Machine Learning
Studio or Databricks
Week 1 Weeks 2-5 Week 6
47. Fully Operational Production Model
– Available at all times, in production
– Batch & API integrations
Model Supervision
– Model is monitored for ongoing usability
– Performance dashboard
– Guaranteed accuracy SLAs
Model Retraining & Support
– Scheduled & triggered model re-tuning or re-training
– Add new data features over time
Model as a Service Solution
Set up model as
a web service
Visualize model
performance in a
dashboard
Maintain and
enhance model
51. What is Azure Databricks?
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
Best of Databricks Best of Microsoft
Designed in collaboration with the founders of Apache Spark
One-click set up; streamlined workflows
Interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
Native integration with Azure services (Power BI, SQL DW, Cosmos DB, Blob Storage)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise -grade SLAs)
52. Azure Databricks key audiences & benefits
Unified analytics platform
Integrated workspace
Easy data exploration
Collaborative experience
Interactive dashboards
Faster insights
• Best of Spark & serverless
• Databricks managed Spark
Improved ETL performance
• Zero management clusters, serverless
Easy to schedule jobs
Automated workflows
Enhanced monitoring & troubleshooting
• Automated alerts & easy access to logs
Zero Management Spark
Cluster democratization (serverless)
Fast, collaborative analytics platform
accelerating time to market
No dev-ops required
Enterprise grade security
• Encryption
• End-to-end auditing
• Role-based control
• Compliance
Data scientist Data engineer CDO, VP of analytics
Provided by Microsoft and Databricks under NDA