Programma

[Video introduction] Regression, decision trees, neural networks—along with many other supervised learning techniques—provide powerful predictive insights. Once built, the models can produce key indicators to optimize the allocation of organizational resources.
New users of these established techniques are often impressed with how easy it all seems to be. Modeling software to build these models is widely available but often results in disappointing results. Many fail to even recognize that proper problem definition was the problem. They likely conclude that the data was not capable of better performance.
The deployment phase includes proper model interpretation and looking for clues that the model will perform well on unseen data. Although the predictive power of these machine-learning models can be very impressive, there is no benefit unless they inform value-focused actions. Models must be deployed in an automated fashion to continually support decision-making for residual impact. The instructor will show how to interpret supervised models with an eye toward decisioning automation.

The seminar
In this half-day seminar, Keith McCormick will overview the two most important and foundational techniques in supervised machine learning, and explain why 70-80% or more of everyday problems faced in established industries can be addressed with one particular machine learning strategy. The focus will be on highly practical techniques for maximizing your results whether you are brand new to predictive analytics or you’ve made some attempts but have been disappointed in the results so far. Veteran users of these techniques will also benefit because a comparison will be made between these traditional techniques and some features of newer techniques. We will explore that while tempting, the newer techniques are rarely the best fit except in a handful of niche application areas that many organizations will not face (at least not in the short term). Participants will leave with specific ideas to apply to their current and future projects.

Learning Objectives

• When to apply supervised or unsupervised modeling methods
• Options for inserting machine learning into the decision making of your organization
• How to use multiple models for value estimation and classification
• How to properly prepare data for different kinds of supervised models
• Interpret model coefficients and output to translate across platforms and languages, including the widely used Predictive Modeling Markup Language (PMML)
• Explore the pros and cons of “black box” models including ensembles
• How data preparation must be automated in parallel with the model if deployment is to succeed
• Compare model accuracy scores to model propensity scores that drive decisions at deployment.

Who is it for?

• Analytic Practitioners
• Data Scientists
• IT Professionals
• Technology Planners
• Consultants; Business Analysts
• Analytic Project Leaders.

Course Description

1. How to choose the best machine learning strategy

• How supervised learning compares to other options
• The reality and the hype regarding machine learning
• What are the classic traditional machine learning techniques?
• The two main types of supervised machine learning

2. Decision Trees: Still the best choice for many everyday challenges

• Exploring and interpreting insights with a completed decision tree model
• A brief primer on the various types of decision tree algorithms
• Strategic considerations and advantages of decision trees
• Deployment and bringing you ML models into production

3. Introducing the CART decision tree

• CART under the hood
• Processing various variable types with CART
• Understanding pruning
• How CART handles missing data with “surrogates”
• The “Roshoman effect” in machine learning

4. Additional Supervised Techniques

• Comparing linear regression to neural networks
• How to embrace the benefits of neural networks without actually using them
• Regression trees: how to use decision trees to address regression problems
• What are “ensemble” methods and why are they so popular?
• Keeping your solutions practical and transparent

[Video introduction] By the end of this workshop your team will have a sound understanding of how data and analytics can expand, enhance and strengthen your business and your relationships with clients. You’ll have some practical guidelines for strategy, messaging and design which can get you started on your own analytics journey.

Learning objectives

• The nature of data as asset
• The importance of analytics to realising that value
• Ways in which data and analytics can be developed as a line of business
• Simple models for data and analytics as a line of business
• The value of benchmarking
• How to encourage and support internal and external communities
• How to be strategically more agile when creating data and analytics lines of business
• Addressing ethics and governance concerns.

Course Description

1. Introduction: Data as a resource, analytics as a differentiator

We believe that data without analytics is a wasted resource; analytics without action is a wasted effort. We review the value of data to software companies and the potential for analytics as a new line of business.

 2. Case studies

Real-world examples of software companies who have developed analytic products and services using a gameplan methodology.
 
3. Three simple models to get you started

Although there are many ways in which you can leverage data as a resource and analytics as an offering, we have found three to be relatively easy and effective to start with. We’ll review the components and technologies of each, with some guidelines for success and pitfalls to avoid.

• User dashboards
• Predictive analytics and alerting
• Benchmarking and associative analytics

4. Communities of practice and tools of choice

When you introduce analytics as a line of business, users and their social interactions, whether in the office or online, will be critical to your success. We show how communities of practice develop around the tools we choose – and we describe how to ensure your tool is chosen.

 5. Governance and privacy

In any discussion of data and analytics today, concerns about privacy and compliance always come to the surface. We’ll introduce the subject with enough detail for you take the first, important, practical steps to being well governed for today’s regulatory environment.

 6. Narratives and gameplans

These are simple tools for mapping and aligning strategy. However, although simple, they offer subtle and effective capabilities for planning features and releases and for aligning teams such as marketing and management around a vision.

Who’s it for? 

• CIOs, CTOs, analytics leaders and data management leaders
• Data scientists, and data analysts.

[Video introduction] Whether you call it a conceptual data model, a domain map, a business object model, or even a “thing model,” a concept model is invaluable to process and architecture initiatives. Why? Because processes, capabilities, and solutions act on “things” – Settle Claim, Register Unit, Resolve Service Issue, and so on. Those things are usually “entities” or “objects” in the concept model, and clarity on “what is one of these things?” contributes immensely to clarity on what the corresponding processes are.
After introducing methods to get people, even C-level executives, engaged in concept modelling, we’ll introduce and get practice with guidelines to ensure proper naming and definition of entities/concepts/business objects. We’ll also see that success depends on recognising that a concept model is a description of a business, not a description of a database. Another key – don’t call it a data model!
Drawing on almost forty years of successful modelling, on projects of every size and type, this session introduces proven techniques backed up with current, real-life examples.

Topics include:

• Concept modelling essentials – things, facts about things, and the policies and rules governing things
• “Guerrilla modelling” – how to get started on concept modelling without anyone realising it
• Naming conventions and graphic guidelines – ensuring correctness, consistency, and readability
• Concept models as a starting point for process discovery
• Practical examples of concept modelling supporting process work, architecture work, and commercial software selection.

[Video introduction] Adopting the DataOps Methodology is helping agile teams deliver data and analytics faster and more manageable in modern data infrastructure and ecosystems. DataOps is critical for companies to become resilient with data and analytics delivery in a volatile and uncertain global business environment. Going beyond DevOps for continuous deployments, DataOps leverages principles from other disciplines to evolve data engineering and management.

Companies need data and analytics more than ever to be agile and competitive in today’s fast-changing environment. DataOps can be an enterprise-wide initiative or an independent agile delivery team working to improve how they deliver data analytics for their customer. Gaining traction takes time and ongoing support.

This seminar will cover:

• The challenges in current data environments and IT
• What DataOps is and how it differs from other approaches
• Which principles and technologies to focus on initially
• How to adopt DataOps to speed analytics development and delivery
• How to continuously engineer, deploy, and operationalize data pipelines with automation and monitoring
• Setting expectations and planning for DataOps maturity.

Course Description

1. Understanding why we need to change

• How business Analytics has changed from diagnostic to predictive
• How data sources are increasing
• The impact of data integration on Data Management
• Changes in IT development methodologies and organizations
• Supporting new data products
• How DataOps is emerging as the next era
• Reviewing the Agile Manifesto
• Important aspect of DevOps
• Review statistical process control for DataOps
• How DataOps can embed Data Quality and Data Governance
• Defining DataOps and the DataOps Manifesto
• Comparing DevOps to DataOps

2. Making DataOps Work

The 7 key concepts to focus on for DataOps

• How Connectors can make a difference
• How engineered data pipelines will work
• How “data drift” will impact data work
• Set up repositories for Data Governance and Data Quality
• The role of data hubs and MDM
• How to set up measurements correctly
• Leveraging DataOps Platform instrumentation

The 2 key processes to focus on for DataOps

• Components needed to deliver on business ideation
• Building data and Analytics deliverables with DataOps

3. Managing DataOps: defining Metrics and Maturity Models

• Defining Metrics for Data and Analytics delivery
• Key DataOps metrics
• How to leverage reusability metrics
• Reviewing metrics for process improvement
• Maturity stage of DataOps adoption
• CMMI-based Maturity Model
• IBM Maturity Model.

[Video introduction] Regression, decision trees, neural networks—along with many other supervised learning techniques—provide powerful predictive insights. Once built, the models can produce key indicators to optimize the allocation of organizational resources.
New users of these established techniques are often impressed with how easy it all seems to be. Modeling software to build these models is widely available but often results in disappointing results. Many fail to even recognize that proper problem definition was the problem. They likely conclude that the data was not capable of better performance.
The deployment phase includes proper model interpretation and looking for clues that the model will perform well on unseen data. Although the predictive power of these machine-learning models can be very impressive, there is no benefit unless they inform value-focused actions. Models must be deployed in an automated fashion to continually support decision-making for residual impact. The instructor will show how to interpret supervised models with an eye toward decisioning automation.

The seminar
In this half-day seminar, Keith McCormick will overview the two most important and foundational techniques in supervised machine learning, and explain why 70-80% or more of everyday problems faced in established industries can be addressed with one particular machine learning strategy. The focus will be on highly practical techniques for maximizing your results whether you are brand new to predictive analytics or you’ve made some attempts but have been disappointed in the results so far. Veteran users of these techniques will also benefit because a comparison will be made between these traditional techniques and some features of newer techniques. We will explore that while tempting, the newer techniques are rarely the best fit except in a handful of niche application areas that many organizations will not face (at least not in the short term). Participants will leave with specific ideas to apply to their current and future projects.

Learning Objectives

• When to apply supervised or unsupervised modeling methods
• Options for inserting machine learning into the decision making of your organization
• How to use multiple models for value estimation and classification
• How to properly prepare data for different kinds of supervised models
• Interpret model coefficients and output to translate across platforms and languages, including the widely used Predictive Modeling Markup Language (PMML)
• Explore the pros and cons of “black box” models including ensembles
• How data preparation must be automated in parallel with the model if deployment is to succeed
• Compare model accuracy scores to model propensity scores that drive decisions at deployment.

Who is it for?

• Analytic Practitioners
• Data Scientists
• IT Professionals
• Technology Planners
• Consultants; Business Analysts
• Analytic Project Leaders.

Course Description

1. How to choose the best machine learning strategy

• How supervised learning compares to other options
• The reality and the hype regarding machine learning
• What are the classic traditional machine learning techniques?
• The two main types of supervised machine learning

2. Decision Trees: Still the best choice for many everyday challenges

• Exploring and interpreting insights with a completed decision tree model
• A brief primer on the various types of decision tree algorithms
• Strategic considerations and advantages of decision trees
• Deployment and bringing you ML models into production

3. Introducing the CART decision tree

• CART under the hood
• Processing various variable types with CART
• Understanding pruning
• How CART handles missing data with “surrogates”
• The “Roshoman effect” in machine learning

4. Additional Supervised Techniques

• Comparing linear regression to neural networks
• How to embrace the benefits of neural networks without actually using them
• Regression trees: how to use decision trees to address regression problems
• What are “ensemble” methods and why are they so popular?
• Keeping your solutions practical and transparent

[Video introduction] By the end of this workshop your team will have a sound understanding of how data and analytics can expand, enhance and strengthen your business and your relationships with clients. You’ll have some practical guidelines for strategy, messaging and design which can get you started on your own analytics journey.

Learning objectives

• The nature of data as asset
• The importance of analytics to realising that value
• Ways in which data and analytics can be developed as a line of business
• Simple models for data and analytics as a line of business
• The value of benchmarking
• How to encourage and support internal and external communities
• How to be strategically more agile when creating data and analytics lines of business
• Addressing ethics and governance concerns.

Course Description

1. Introduction: Data as a resource, analytics as a differentiator

We believe that data without analytics is a wasted resource; analytics without action is a wasted effort. We review the value of data to software companies and the potential for analytics as a new line of business.

 2. Case studies

Real-world examples of software companies who have developed analytic products and services using a gameplan methodology.
 
3. Three simple models to get you started

Although there are many ways in which you can leverage data as a resource and analytics as an offering, we have found three to be relatively easy and effective to start with. We’ll review the components and technologies of each, with some guidelines for success and pitfalls to avoid.

• User dashboards
• Predictive analytics and alerting
• Benchmarking and associative analytics

4. Communities of practice and tools of choice

When you introduce analytics as a line of business, users and their social interactions, whether in the office or online, will be critical to your success. We show how communities of practice develop around the tools we choose – and we describe how to ensure your tool is chosen.

 5. Governance and privacy

In any discussion of data and analytics today, concerns about privacy and compliance always come to the surface. We’ll introduce the subject with enough detail for you take the first, important, practical steps to being well governed for today’s regulatory environment.

 6. Narratives and gameplans

These are simple tools for mapping and aligning strategy. However, although simple, they offer subtle and effective capabilities for planning features and releases and for aligning teams such as marketing and management around a vision.

Who’s it for? 

• CIOs, CTOs, analytics leaders and data management leaders
• Data scientists, and data analysts.

[Video introduction] Whether you call it a conceptual data model, a domain map, a business object model, or even a “thing model,” a concept model is invaluable to process and architecture initiatives. Why? Because processes, capabilities, and solutions act on “things” – Settle Claim, Register Unit, Resolve Service Issue, and so on. Those things are usually “entities” or “objects” in the concept model, and clarity on “what is one of these things?” contributes immensely to clarity on what the corresponding processes are.
After introducing methods to get people, even C-level executives, engaged in concept modelling, we’ll introduce and get practice with guidelines to ensure proper naming and definition of entities/concepts/business objects. We’ll also see that success depends on recognising that a concept model is a description of a business, not a description of a database. Another key – don’t call it a data model!
Drawing on almost forty years of successful modelling, on projects of every size and type, this session introduces proven techniques backed up with current, real-life examples.

Topics include:

• Concept modelling essentials – things, facts about things, and the policies and rules governing things
• “Guerrilla modelling” – how to get started on concept modelling without anyone realising it
• Naming conventions and graphic guidelines – ensuring correctness, consistency, and readability
• Concept models as a starting point for process discovery
• Practical examples of concept modelling supporting process work, architecture work, and commercial software selection.

[Video introduction] Adopting the DataOps Methodology is helping agile teams deliver data and analytics faster and more manageable in modern data infrastructure and ecosystems. DataOps is critical for companies to become resilient with data and analytics delivery in a volatile and uncertain global business environment. Going beyond DevOps for continuous deployments, DataOps leverages principles from other disciplines to evolve data engineering and management.

Companies need data and analytics more than ever to be agile and competitive in today’s fast-changing environment. DataOps can be an enterprise-wide initiative or an independent agile delivery team working to improve how they deliver data analytics for their customer. Gaining traction takes time and ongoing support.

This seminar will cover:

• The challenges in current data environments and IT
• What DataOps is and how it differs from other approaches
• Which principles and technologies to focus on initially
• How to adopt DataOps to speed analytics development and delivery
• How to continuously engineer, deploy, and operationalize data pipelines with automation and monitoring
• Setting expectations and planning for DataOps maturity.

Course Description

1. Understanding why we need to change

• How business Analytics has changed from diagnostic to predictive
• How data sources are increasing
• The impact of data integration on Data Management
• Changes in IT development methodologies and organizations
• Supporting new data products
• How DataOps is emerging as the next era
• Reviewing the Agile Manifesto
• Important aspect of DevOps
• Review statistical process control for DataOps
• How DataOps can embed Data Quality and Data Governance
• Defining DataOps and the DataOps Manifesto
• Comparing DevOps to DataOps

2. Making DataOps Work

The 7 key concepts to focus on for DataOps

• How Connectors can make a difference
• How engineered data pipelines will work
• How “data drift” will impact data work
• Set up repositories for Data Governance and Data Quality
• The role of data hubs and MDM
• How to set up measurements correctly
• Leveraging DataOps Platform instrumentation

The 2 key processes to focus on for DataOps

• Components needed to deliver on business ideation
• Building data and Analytics deliverables with DataOps

3. Managing DataOps: defining Metrics and Maturity Models

• Defining Metrics for Data and Analytics delivery
• Key DataOps metrics
• How to leverage reusability metrics
• Reviewing metrics for process improvement
• Maturity stage of DataOps adoption
• CMMI-based Maturity Model
• IBM Maturity Model.

[Video introduction] Regression, decision trees, neural networks—along with many other supervised learning techniques—provide powerful predictive insights. Once built, the models can produce key indicators to optimize the allocation of organizational resources.
New users of these established techniques are often impressed with how easy it all seems to be. Modeling software to build these models is widely available but often results in disappointing results. Many fail to even recognize that proper problem definition was the problem. They likely conclude that the data was not capable of better performance.
The deployment phase includes proper model interpretation and looking for clues that the model will perform well on unseen data. Although the predictive power of these machine-learning models can be very impressive, there is no benefit unless they inform value-focused actions. Models must be deployed in an automated fashion to continually support decision-making for residual impact. The instructor will show how to interpret supervised models with an eye toward decisioning automation.

The seminar
In this half-day seminar, Keith McCormick will overview the two most important and foundational techniques in supervised machine learning, and explain why 70-80% or more of everyday problems faced in established industries can be addressed with one particular machine learning strategy. The focus will be on highly practical techniques for maximizing your results whether you are brand new to predictive analytics or you’ve made some attempts but have been disappointed in the results so far. Veteran users of these techniques will also benefit because a comparison will be made between these traditional techniques and some features of newer techniques. We will explore that while tempting, the newer techniques are rarely the best fit except in a handful of niche application areas that many organizations will not face (at least not in the short term). Participants will leave with specific ideas to apply to their current and future projects.

Learning Objectives

• When to apply supervised or unsupervised modeling methods
• Options for inserting machine learning into the decision making of your organization
• How to use multiple models for value estimation and classification
• How to properly prepare data for different kinds of supervised models
• Interpret model coefficients and output to translate across platforms and languages, including the widely used Predictive Modeling Markup Language (PMML)
• Explore the pros and cons of “black box” models including ensembles
• How data preparation must be automated in parallel with the model if deployment is to succeed
• Compare model accuracy scores to model propensity scores that drive decisions at deployment.

Who is it for?

• Analytic Practitioners
• Data Scientists
• IT Professionals
• Technology Planners
• Consultants; Business Analysts
• Analytic Project Leaders.

Course Description

1. How to choose the best machine learning strategy

• How supervised learning compares to other options
• The reality and the hype regarding machine learning
• What are the classic traditional machine learning techniques?
• The two main types of supervised machine learning

2. Decision Trees: Still the best choice for many everyday challenges

• Exploring and interpreting insights with a completed decision tree model
• A brief primer on the various types of decision tree algorithms
• Strategic considerations and advantages of decision trees
• Deployment and bringing you ML models into production

3. Introducing the CART decision tree

• CART under the hood
• Processing various variable types with CART
• Understanding pruning
• How CART handles missing data with “surrogates”
• The “Roshoman effect” in machine learning

4. Additional Supervised Techniques

• Comparing linear regression to neural networks
• How to embrace the benefits of neural networks without actually using them
• Regression trees: how to use decision trees to address regression problems
• What are “ensemble” methods and why are they so popular?
• Keeping your solutions practical and transparent

[Video introduction] By the end of this workshop your team will have a sound understanding of how data and analytics can expand, enhance and strengthen your business and your relationships with clients. You’ll have some practical guidelines for strategy, messaging and design which can get you started on your own analytics journey.

Learning objectives

• The nature of data as asset
• The importance of analytics to realising that value
• Ways in which data and analytics can be developed as a line of business
• Simple models for data and analytics as a line of business
• The value of benchmarking
• How to encourage and support internal and external communities
• How to be strategically more agile when creating data and analytics lines of business
• Addressing ethics and governance concerns.

Course Description

1. Introduction: Data as a resource, analytics as a differentiator

We believe that data without analytics is a wasted resource; analytics without action is a wasted effort. We review the value of data to software companies and the potential for analytics as a new line of business.

 2. Case studies

Real-world examples of software companies who have developed analytic products and services using a gameplan methodology.
 
3. Three simple models to get you started

Although there are many ways in which you can leverage data as a resource and analytics as an offering, we have found three to be relatively easy and effective to start with. We’ll review the components and technologies of each, with some guidelines for success and pitfalls to avoid.

• User dashboards
• Predictive analytics and alerting
• Benchmarking and associative analytics

4. Communities of practice and tools of choice

When you introduce analytics as a line of business, users and their social interactions, whether in the office or online, will be critical to your success. We show how communities of practice develop around the tools we choose – and we describe how to ensure your tool is chosen.

 5. Governance and privacy

In any discussion of data and analytics today, concerns about privacy and compliance always come to the surface. We’ll introduce the subject with enough detail for you take the first, important, practical steps to being well governed for today’s regulatory environment.

 6. Narratives and gameplans

These are simple tools for mapping and aligning strategy. However, although simple, they offer subtle and effective capabilities for planning features and releases and for aligning teams such as marketing and management around a vision.

Who’s it for? 

• CIOs, CTOs, analytics leaders and data management leaders
• Data scientists, and data analysts.

[Video introduction] Whether you call it a conceptual data model, a domain map, a business object model, or even a “thing model,” a concept model is invaluable to process and architecture initiatives. Why? Because processes, capabilities, and solutions act on “things” – Settle Claim, Register Unit, Resolve Service Issue, and so on. Those things are usually “entities” or “objects” in the concept model, and clarity on “what is one of these things?” contributes immensely to clarity on what the corresponding processes are.
After introducing methods to get people, even C-level executives, engaged in concept modelling, we’ll introduce and get practice with guidelines to ensure proper naming and definition of entities/concepts/business objects. We’ll also see that success depends on recognising that a concept model is a description of a business, not a description of a database. Another key – don’t call it a data model!
Drawing on almost forty years of successful modelling, on projects of every size and type, this session introduces proven techniques backed up with current, real-life examples.

Topics include:

• Concept modelling essentials – things, facts about things, and the policies and rules governing things
• “Guerrilla modelling” – how to get started on concept modelling without anyone realising it
• Naming conventions and graphic guidelines – ensuring correctness, consistency, and readability
• Concept models as a starting point for process discovery
• Practical examples of concept modelling supporting process work, architecture work, and commercial software selection.

[Video introduction] Adopting the DataOps Methodology is helping agile teams deliver data and analytics faster and more manageable in modern data infrastructure and ecosystems. DataOps is critical for companies to become resilient with data and analytics delivery in a volatile and uncertain global business environment. Going beyond DevOps for continuous deployments, DataOps leverages principles from other disciplines to evolve data engineering and management.

Companies need data and analytics more than ever to be agile and competitive in today’s fast-changing environment. DataOps can be an enterprise-wide initiative or an independent agile delivery team working to improve how they deliver data analytics for their customer. Gaining traction takes time and ongoing support.

This seminar will cover:

• The challenges in current data environments and IT
• What DataOps is and how it differs from other approaches
• Which principles and technologies to focus on initially
• How to adopt DataOps to speed analytics development and delivery
• How to continuously engineer, deploy, and operationalize data pipelines with automation and monitoring
• Setting expectations and planning for DataOps maturity.

Course Description

1. Understanding why we need to change

• How business Analytics has changed from diagnostic to predictive
• How data sources are increasing
• The impact of data integration on Data Management
• Changes in IT development methodologies and organizations
• Supporting new data products
• How DataOps is emerging as the next era
• Reviewing the Agile Manifesto
• Important aspect of DevOps
• Review statistical process control for DataOps
• How DataOps can embed Data Quality and Data Governance
• Defining DataOps and the DataOps Manifesto
• Comparing DevOps to DataOps

2. Making DataOps Work

The 7 key concepts to focus on for DataOps

• How Connectors can make a difference
• How engineered data pipelines will work
• How “data drift” will impact data work
• Set up repositories for Data Governance and Data Quality
• The role of data hubs and MDM
• How to set up measurements correctly
• Leveraging DataOps Platform instrumentation

The 2 key processes to focus on for DataOps

• Components needed to deliver on business ideation
• Building data and Analytics deliverables with DataOps

3. Managing DataOps: defining Metrics and Maturity Models

• Defining Metrics for Data and Analytics delivery
• Key DataOps metrics
• How to leverage reusability metrics
• Reviewing metrics for process improvement
• Maturity stage of DataOps adoption
• CMMI-based Maturity Model
• IBM Maturity Model.