Digital Transformation for Tech Leaders
Posted by Superadmin on January 17 2019 17:10:43

Digital Transformation for Tech Leaders

 

 

Technology leaders need to be familiar with the technologies that will drive their digital transformation. This learning path introduces these technologies to those responsible for driving their tech strategy. Topics include cloud computing, big data and data science, machine learning and artificial intelligence, blockchain, the Internet of Things, software project management, and security policies. Also explore our Digital Transformation for Leaders and Digital Transformation in Practice learning paths.
Understand the critical technologies driving digital transformation.
Identify the technical skills needed to move toward a digital-first strategy.
Plan for the implementation of your

 

 

 

 

 

 

1

Learning Cloud Computing: Core Concepts with David Linthicum

1h 4m • COURSE
Migrating to the cloud? Get an overview of cloud computing and the key concepts that you should consider when making a move to the cloud. There are three types of cloud solutions: software as a service, infrastructure as a service, and platform as a service. Instructor David Linthicum helps you evaluate these solutions, including Amazon Web Services, Google Cloud Platform, Salesforce.com, and Office 365, as well as the data and applications that are best suited to the cloud. He explains how to select a cloud provider and plan a migration, and reviews the security considerations and typical day-to-day operations and tools IT administrators need to keep their cloud-based infrastructure up and running.
Topics include: • Types of clouds: SaaS, IaaS, and PaaS • Identifying the data and applications to move to the cloud • Migrating planning • Selecting a provider • Cloud security • Cloud operations
2

Cloud Architecture: Core Concepts with David Linthicum

1h 1m • COURSE
Are you new to cloud computing? If so, this course can help you bolster your cloud computing skillset by familiarizing you with the business and tech-related basics of creating a cloud architecture. Join cloud-computing luminary David Linthicum as he discusses what cloud architecture is and why a strong architecture is crucial. After covering the fundamentals, he moves on to identifying the business and technical requirements of building a cloud architecture, and goes over cloud parts and how to work from requirements to a solution. The course concludes by leading you through how to build your first architecture.
Topics include: • Cloud architecture basics • What problems need to be solved? • The "as is" and "to be" states • Cloud storage, CPUs, and databases • Building your first architecture
3

Cloud Architecture: Advanced Concepts with David Linthicum

58m 44s • COURSE
State-of-the-art technology is changing the way we design for the cloud. New architectural patterns are emerging as a result of breakthroughs such as microservices, containers, and serverless computing. Want to know how these advanced concepts will affect your cloud strategy and solution? This platform-neutral course is targeted at IT professionals who already know the basics of architecting cloud solutions and wish to understand the impact of new technologies on their business. Learn how independently deployable, modular services—microservices—will affect the structure of your cloud-based applications; how serverless and composite architectures such as AWS Lambda and Azure Functions free up engineers to focus on features, not infrastructures; and how new high-performance solutions allow you to "lease" computing storage and power. Instructor David Linthicum also discusses DevOps integration and advanced architecture strategies, such as isolating change and volatility in a single domain.
Topics include: • Microservices and containers • Complex, disturbed, serverless, and composite architectures • DevOps integration • High-performance solutions
4

Big Data Foundations: Techniques and Concepts with Barton Poulson

2h 12m • COURSE
Big data is big news. But what is big data, and how do we use it? Simply put, big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Spreadsheets and relational databases just don't cut it with big data. In this course, Barton Poulson tells you the methods that do work, introducing all the techniques and concepts involved in capturing, storing, manipulating, and analyzing big data, including data mining and predictive analytics. He explains big data's relationship to data science, statistics, and programing; its uses in marketing, scientific research, and tools like Amazon's recommendation engine; and the ethical issues that lie behind its use.
Lynda.com is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.
Topics include:: • Evaluate the demand for data science in business, research, and consumer technology. • Assess the careers and skills in data science. • Review the ethical issues in data science. • Explore data visualization with graphing tools. • Discover how data scientists use tools such as Hadoop and Excel.
5

Artificial Intelligence Foundations: Thinking Machines with Doug Rose

1h 27m • COURSE
Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast.
This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
Topics include:: • The history of AI • Machine learning • Technical approaches to AI • AI in robotics • Integrating AI with big data • Avoiding pitfalls
6

Machine Learning and AI Foundations: Value Estimations with Adam Geitgey

1h 4m • COURSE
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
Topics include: • Setting up the development environment • Building a simple home value estimator • Finding the best weights automatically • Working with large data sets efficiently • Training a supervised machine learning model • Exploring a home value data set • Deciding how much data is needed • Preparing the features • Training the value estimator • Measuring accuracy with mean absolute error • Improving a system • Using the machine learning model to make predictions
7

Machine Learning and AI Foundations: Decision Trees with Keith McCormick

1h 16m • COURSE
Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
Topics include: • Using the SPSS Modeler • Building a CHAID model • Adding a second model with C&RT • Analysis notes • Using a lift and gains chart • Exploring algorithms • Building a tree interactively • The Bonferonni adjustment • Handling nominal, ordinal, and continuous variables • Examining the CHAID tree • The Gini coefficient • Weighing purity and balance • Understanding pruning • Examining the C&RT tree • Applying stopping rules • Using the Auto Classifier tuning trick
8

Machine Learning & AI: Advanced Decision Trees with Keith McCormick

1h 16m • COURSE
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Topics include: • Understanding QUEST functions and applications • C5.0 concepts and practical applications • Understanding information gain • Random forests • Boosting and bagging • Costs and priors
9

with Jonathan Reichental

57m 13s • COURSE
We're on the precipice of a radical and disruptive new way of conducting all manner of transactions over the Internet. While still in its infancy, blockchain technology demands attention. In this introductory-level course, learn what blockchain is and what it might mean to you. Jonathan Reichental—named one of the world's top 100 CIOs in 2017—dives into blockchain technology from a conceptual perspective. The course is primarily non-technical by design, intended for those working in business leadership positions, data science, and IT management.
Jonathan begins by describing some of the current challenges with the Internet, including existing risks and security problems such as identity management. Next, he describes how traditional online databases function, so that you have a basis for how the blockchain redesigns this function. He then describes how the blockchain becomes a potential solution for many of the existing limitations of online databases. Since the blockchain has its genesis in Bitcoin—the digital currency—he provides some background on that too. He also discusses how blockchain technology actually offers new capabilities beyond simply solving old problems. To wrap up the course, Jonathan shares steps you can take in your organization to understand the implications of the blockchain.
Topics include: • Risk and security challenges • Rethinking the traditional database • What is the blockchain? • What problems does the blockchain solve? • Transforming transactions • Examples of the blockchain in action • Obstacles to blockchain adoption • Risks to existing solutions and enterprises
10

with Jonathan Reichental

1h 31m • COURSE
Blockchain is an exciting new technology that is redefining how we store, update, and move data. Learn about the blockchain and the implications of decentralized, encrypted data storage for business and society, in this course with Dr. Jonathan Reichental. He begins by explaining exactly how the blockchain works, including the concepts of cryptography, mining, distribution, and smart contracts. Next, he describes how the blockchain enables the most popular cryptocurrency, bitcoin. Learn how new bitcoin is generated, how it is transacted, and the requirements for participating as either an exchange, a trader, or a miner. The course also covers other popular technology manifestations of blockchain, such as Hyperledger and Ripple. Dr. Reichental then puts blockchain technology in context in several different industries, including energy and security, and helps learners understand how to think about the blockchain in ways that can be applied in any field.
Topics include: • Blockchain basics • Public and private keys • How blockchain enables bitcoin • Blockchain and the electrical grid • Blockchain and identity management • Risks of blockchain
11

IoT Foundations: Fundamentals with Bruce Sinclair

38m 9s • COURSE
The Internet of Things (IoT) is more than just smart or connected devices. In this course, learn what IoT is, and how it works from a technical standpoint. Bruce Sinclair provides a broad overview of IoT, explaining each of its main components. He also goes into the software-defined product—the digital twin of the physical product—as well as the hardware-defined product, explaining how sensors and embedded systems help to gather data. Plus, he dives into the network fabric, and explains what external systems are and why it's important to consider them when designing an IoT product.
Topics include: • IoT value modeling • The software-defined product • The hardware-defined product • Embedded systems • Connected sensors • The network fabric • OT IT and fog networks • Analytics and big data • Data services
12

IoT Foundations: Standards and Ecosystems with Zahraa Khalil

1h 32m • COURSE
The continuous development of IoT ecosystems promises a future in which our homes, cars, and cities are more connected. But as this technology evolves, it has become increasingly important to tackle the communication and compatibility challenges facing the billions of connected devices already out in the world. In this course, learn about IoT standards and ecosystems. Instructor Zahraa Khalil provides a high-level look at the IoT ecosystem, discussing IoT markets, technology, and standards. She also goes over the challenges facing the development of IoT—including security, privacy, and legal issues—the IoT standards development process, and IoT protocols for device management. Plus, she shares examples of IoT ecosystems in action by exploring case studies of smart cities.
Topics include: • IoT architecture models and components • IoT ecosystem • IoT technology, markets, and standards • IoT technology • Security, privacy, and legal issues • IoT device management • IoT protocols for device management • IoT device management challenges and applications Smart cities
13
>Agile Development Practices with Harrison Ferrone
26m 29s • COURSE
The agile methodology has become a staple in the software development industry for its rapid development capabilities, iterative workflow, and improved team dynamics. In this course, Harrison Ferrone covers agile practices aimed at addressing the complexities and limitations unique to mobile projects. He takes you from the concept and ideation phase all the way to continuous integration and deployment, highlighting best practices and efficient planning. By the end of this course, you will have a foundation for bringing agile into your development process and streamlining your existing project pipeline.
LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.
Topics include: • Planning and design • Developing flexible architecture • Documentation considerations • When to optimize • Determining velocity • Agile programming techniques • Refactoring • Testing and handling bugs • Structuring a release pipeline
14

Implementing an Information Security Program with Kip Boyle

2h 31m • COURSE
Building and operating an information security program at your organization can be challenging. The scope can be vast and complex. Thinking of all the ways an organization can fail and coming up with actionable measures you can take to prevent issues, mitigate risk, or recover from events is a large undertaking. In this course, Kip Boyle, president of Cyber Risk Opportunities, guides you through the entire process of creating an information security program, rolling it out to your organization, and maintaining it for continuous risk management.
Topics include: • Goals and components of an information security program • Measuring and managing information risks • Reducing risks to an acceptable level • Using a workflow to organize your work • Communicating progress with executives and stakeholders • Demonstrating compliance

Learning Cloud Computing: Core Concepts with David Linthicum

1h 4m • COURSE
Migrating to the cloud? Get an overview of cloud computing and the key concepts that you should consider when making a move to the cloud. There are three types of cloud solutions: software as a service, infrastructure as a service, and platform as a service. Instructor David Linthicum helps you evaluate these solutions, including Amazon Web Services, Google Cloud Platform, Salesforce.com, and Office 365, as well as the data and applications that are best suited to the cloud. He explains how to select a cloud provider and plan a migration, and reviews the security considerations and typical day-to-day operations and tools IT administrators need to keep their cloud-based infrastructure up and running.
Topics include: • Types of clouds: SaaS, IaaS, and PaaS • Identifying the data and applications to move to the cloud • Migrating planning • Selecting a provider • Cloud security • Cloud operations

0. Introduction
00_01_Welcome
00_02_Target_Audience
00_03_Learning_Supports



1. Cloud Computing Basics
01_01_Basic_Concepts
01_02_PPH_Clouds
01_03_Types_of_Clouds
01_04_IAAS
01_05_SAAS
01_06_PAAS
01_07_IAAS_Case



2. Cloud Computing Planning
02_01_Apps_to_Move
02_02_Data_to_Move
02_03_TCO
02_04_Migration_Plan
02_05_Select_Cloud_Provider
02_06_Why_Cloud_Security
02_07_New_Skills
02_08_First_Cloud_Project



3. Cloud Security
03_01_Plan_Cloud_Security
03_02_Security_Requirements
03_03_Select_Technology
03_04_Implement_Ops



4. Cloud Operations
04_01_Cloud_Operations
04_02_Tech_and_Tools
04_03_Monitor_Mangage



5. Conclusion
05_01_Next_steps



Cloud Architecture: Core Concepts with David Linthicum

1h 1m • COURSE
Are you new to cloud computing? If so, this course can help you bolster your cloud computing skillset by familiarizing you with the business and tech-related basics of creating a cloud architecture. Join cloud-computing luminary David Linthicum as he discusses what cloud architecture is and why a strong architecture is crucial. After covering the fundamentals, he moves on to identifying the business and technical requirements of building a cloud architecture, and goes over cloud parts and how to work from requirements to a solution. The course concludes by leading you through how to build your first architecture.
Topics include: • Cloud architecture basics • What problems need to be solved? • The "as is" and "to be" states • Cloud storage, CPUs, and databases • Building your first architecture

0. Introduction
001 Welcome
002 What you should know



1. Cloud Architecture Introduction
003 Cloud architecture basics
004 Cloud architecture - Example 1
005 Cloud architecture - Example 2
006 Skills, tools, and processes



2. Understand the Basic Needs
007 What problems need to be solved_
008 The 'as is' state
009 The 'to be' state



3. Its All about the Cloud Parts
010 Cloud storage
011 Cloud CPUs
012 Cloud databases
013 Additional cloud services



4. From Requirements to Solutions
014 Storage level
015 Data level
016 Processing level
017 Network level
018 Application level
019 Making sense of it all



5. Build your First Architecture
020 Step 1 - Define your requirements
021 Step 2 - Define your desired end state
022 Step 3 - Mapping 'as is' to be true
023 Step 4 - Create your final architecture



6. Conclusion
024 Next steps



Cloud Architecture: Advanced Concepts with David Linthicum

58m 44s • COURSE
State-of-the-art technology is changing the way we design for the cloud. New architectural patterns are emerging as a result of breakthroughs such as microservices, containers, and serverless computing. Want to know how these advanced concepts will affect your cloud strategy and solution? This platform-neutral course is targeted at IT professionals who already know the basics of architecting cloud solutions and wish to understand the impact of new technologies on their business. Learn how independently deployable, modular services—microservices—will affect the structure of your cloud-based applications; how serverless and composite architectures such as AWS Lambda and Azure Functions free up engineers to focus on features, not infrastructures; and how new high-performance solutions allow you to "lease" computing storage and power. Instructor David Linthicum also discusses DevOps integration and advanced architecture strategies, such as isolating change and volatility in a single domain.
Topics include: • Microservices and containers • Complex, disturbed, serverless, and composite architectures • DevOps integration • High-performance solutions

0. Introduction
01 - Advanced cloud architecture concepts
02 - What you should know



1. Advanced Architecture Patterns
03 - Microservices
04 - Complex, disturbed, and serverless
05 - DevOps
06 - Purpose built vs. general use



2. Leveraging Microservice Architecture
07 - Microservices 101
08 - Use cases
09 - Microservices examples



3. Moving to Complex, Serverless and Widely Distributed Architecture
10 - Complex architectures
11 - Distributed architectures
12 - Serverless architectures
13 - Composite architectures



4. Moving to DevOps Integration
14 - The role of architecture with DevOps
15 - The process
16 - The tools
17 - DevOps example



5. Other Concepts to Consider
18 - High-performance computing
19 - High-performance data transfer
20 - Coupling vs. cohesion
21 - Placing things that change into a domain



6. Conclusion
22 - Next steps



Big Data Foundations: Techniques and Concepts with Barton Poulson

2h 12m • COURSE
Big data is big news. But what is big data, and how do we use it? Simply put, big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Spreadsheets and relational databases just don't cut it with big data. In this course, Barton Poulson tells you the methods that do work, introducing all the techniques and concepts involved in capturing, storing, manipulating, and analyzing big data, including data mining and predictive analytics. He explains big data's relationship to data science, statistics, and programing; its uses in marketing, scientific research, and tools like Amazon's recommendation engine; and the ethical issues that lie behind its use.
Lynda.com is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.
Topics include:: • Evaluate the demand for data science in business, research, and consumer technology. • Assess the careers and skills in data science. • Review the ethical issues in data science. • Explore data visualization with graphing tools. • Discover how data scientists use tools such as Hadoop and Excel.

01. Introduction
01_01-Welcome



02. What is Big Data
02_01-The three Vs of big data
02_02-Volume
02_03-Velocity
02_04-Variety
02_05-Does big data need all three?



03. How is Big Data Used
03_01-Understanding big data for consumers
03_02-Understanding big data for business
03_03-Understanding big data for research



04. Big Data and Data Science
04_01-Ten ways big data is different from small data
04_02-The three facets of data science
04_03-Types and skills in data science
04_04-Data science without big data
04_05-Big data without data science



05. Ethics in Big Data
05_01-Challenges with anonymity
05_02-Challenges with confidentiality



06. Sources and Structures of Big Data
06_01-Human-generated data
06_02-Machine-generated data
06_03-Structured data
06_04-Unstructured data



07. Storing Big Data
07_01-Distributed storage and the cloud
07_02-Cloud computing: IaaS, PaaS, SaaS, and DaaS
07_03-A brief introduction to Hadoop



08. Preparing data for analysis
08_01-Challenges with data quality
08_02-ETL: Extract, transform, load
08_03-Additional Vs of big data



09. Big Data Analysis
09_01-Monitoring and anomaly detection
09_02-Data mining and text analytics
09_03-Predictive analytics
09_04-Big data visualization
09_05-The role of Excel in big data



10. Conclusion
10_01-Next steps



Artificial Intelligence Foundations: Thinking Machines with Doug Rose

1h 27m • COURSE
Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast.

This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
Topics include:
  • The history of AI
  • Machine learning
  • Technical approaches to AI
  • AI in robotics
  • Integrating AI with big data
  • Avoiding pitfalls

01 Introduction
001 Welcome



02. What Is Artificial Intelligence?
002. Define general intelligence
003.The history of AI
004.Strong vs. weak AI
005.Plan AI



03. The Rise of Machine Learning
006.Machine learning
007.Artificial neural networks
008.Perceptrons



04. Finding the Right Approach
009.Match patterns
010Data vs. reasoning
011.Unsupervised learning
012.Backpropagation
013.Regression



05. Common AI Programs
014.Robotics
015.Natural language processing
016.The Internet of Things



06. Mixing with Other Technologies
017.Big data
018.Data science



07. Avoiding Pitfalls
019.Pitfalls
020.Next steps



Machine Learning and AI Foundations: Value Estimations with Adam Geitgey

1h 4m • COURSE
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
Topics include:
  • Setting up the development environment
  • Building a simple home value estimator
  • Finding the best weights automatically
  • Working with large data sets efficiently
  • Training a supervised machine learning model
  • Exploring a home value data set
  • Deciding how much data is needed
  • Preparing the features
  • Training the value estimator
  • Measuring accuracy with mean absolute error
  • Improving a system
  • Using the machine learning model to make predictions

SECTION 1 INTRODUCTION
01_01-Welcome
01_02-What you should know
01_03-Using the exercise files
01_04-Set up the development environment



SECTION 2 WHAT IS MACHINE LEARNING AND VALUE PREDICTION
02_01-What is machine learning
02_02-Supervised machine learning for value prediction
02_03-Build a simple home value estimator
02_03-Build a simple home value estimator
02_05-Cool uses of value prediction



SECTION 3 AN OVERVIEW OF BUILDING A MACHINE LEARNING SYSTEM
03_01-Introduction to NumPy, scikitlearn, and pandas
03_02-Think in vectors_ How to work with large data sets efficiently
03_03-The basic workflow for training a supervised machine learning model
03_04-Gradient boosting_ A versatile machine learning algorithm



SECTION 4 TRAINING DATA
04_01-Explore a home value data set
04_02-Standard conventions for naming training data
04_03-Decide how much data you need



SECTION 5 FEATURES
05_01-Feature engineering
05_02-Choose the best features for home value prediction
05_03-Use as few features as possible_ The curse of dimensionality



SECTION 6 CODING OUR SYSTEM
06_01-Prepare the features
06_02-Training vs. testing data
06_03-Train the value estimator
06_04-Measure accuracy with mean absolute error



SECTION 7 IMPROVING OUR SYSTEM
07_01-Overfitting and underfitting
07_02-The brute force solution_ Grid search
07_03-Feature selection



SECTION 8 USING THE ESTIMATOR IN A REAL-WORLD PROGRAM
08_01-Predict values for new data
08_01-Predict values for new data



SECTION 9 CONCLUSION
09_01-Wrapup



Machine Learning and AI Foundations: Decision Trees with Keith McCormick

1h 16m • COURSE
Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
Topics include: • Using the SPSS Modeler • Building a CHAID model • Adding a second model with C&RT • Analysis notes • Using a lift and gains chart • Exploring algorithms • Building a tree interactively • The Bonferonni adjustment • Handling nominal, ordinal, and continuous variables • Examining the CHAID tree • The Gini coefficient • Weighing purity and balance • Understanding pruning • Examining the C&RT tree • Applying stopping rules • Using the Auto Classifier tuning trick

1. Introduction
01_01 Welcome
01_02 What you should know
01_03 Using the exercise files



02. Decision Trees in IBM SPSS Modeler
02_01-Decision tree options in SPSS Modeler
02_02-Building a quick CHAID model
02_03-Adding a second model with CRT
02_04-Analysis nodes
02_05-Lift and gains chart



03. Understanding CHAID
03_01-What is an algorithm
03_02-Chisquared overview
03_03-Buliding a tree interactively
03_04-Bonferonni adjustment
03_05-What is level of measurement
03_06-How CHAID handles nominal variables
03_07-How CHAID handles ordinal variables
03_08-How CHAID handles continuous variables
03_09-A quick look at the complete CHAID tree



04. Understanding CRT
04_01-What is the Gini coefficient
04_02-How does CRT weigh purity and balance
04_03-How CRT handles nominal, ordinal, and continuous variables
04_04-How CRT handles missing data
04_05-Understanding pruning
04_06-A quick look at the complete CRT tree



05. Improving Your Model
05_01-Stopping rules in CHAID and CRT
05_02-Exhaustive CHAID
05_03-The Auto Classifier tuning trick



06. Conclusion
06_01-Next steps



Machine Learning & AI: Advanced Decision Trees with Keith McCormick

1h 16m • COURSE
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Topics include: • Understanding QUEST functions and applications • C5.0 concepts and practical applications • Understanding information gain • Random forests • Boosting and bagging • Costs and priors

01. Introduction
01. Welcome
02. What you should know
03. Using the exercise files



2. 1. Understanding QUEST
04. Overview
05. How QUEST handles nominal variables
06. How QUEST handles ordinal and continuous variables
07. How QUEST handles missing data
08. Pruning in QUEST
09. Stopping rules in QUEST



3. 2. Understanding C5.0
10. ID3 and C4.5
11. Winnowing attributes
12. Rule sets
13. Understanding information gain
14. Pruning in C5.0
15. How C5.0 handles missing data



4. 3. Advanced Topics
16. Ensembles
17. What is bagging
18. Using bagging for feature selection
19. Random forests/div>
20. What is boosting
21. Costs and priors



5. Conclusion
22 - Next steps



Blockchain Basics with Jonathan Reichental

57m 13s • COURSE
We're on the precipice of a radical and disruptive new way of conducting all manner of transactions over the Internet. While still in its infancy, blockchain technology demands attention. In this introductory-level course, learn what blockchain is and what it might mean to you. Jonathan Reichental—named one of the world's top 100 CIOs in 2017—dives into blockchain technology from a conceptual perspective. The course is primarily non-technical by design, intended for those working in business leadership positions, data science, and IT management.
Jonathan begins by describing some of the current challenges with the Internet, including existing risks and security problems such as identity management. Next, he describes how traditional online databases function, so that you have a basis for how the blockchain redesigns this function. He then describes how the blockchain becomes a potential solution for many of the existing limitations of online databases. Since the blockchain has its genesis in Bitcoin—the digital currency—he provides some background on that too. He also discusses how blockchain technology actually offers new capabilities beyond simply solving old problems. To wrap up the course, Jonathan shares steps you can take in your organization to understand the implications of the blockchain.
Topics include: • Risk and security challenges • Rethinking the traditional database • What is the blockchain? • What problems does the blockchain solve? • Transforming transactions • Examples of the blockchain in action • Obstacles to blockchain adoption • Risks to existing solutions and enterprises

0. Introduction
01. Welcome and introduction



1. 1. Trusting the Internet
02. Risk and security challenges with using the Internet today
03. Rethinking the traditional database



2. 2. What Is the Blockchain
04. What problems does the blockchain solve
05. The birth of the blockchain in bitcoin
06. What new opportunities does the blockchain enable



3. 3. Transforming Transactions
07. Examples of the blockchain in action today
08. Thinking about the future of blockchain innovation



4. 4. Challenges Ahead
09. The potential obstacles to blockchain adoption
10. Risks to existing solutions and enterprises



5 Conclusion
11 Next Steps



Blockchain: Beyond the Basics with Jonathan Reichental

1h 31m • COURSE
Blockchain is an exciting new technology that is redefining how we store, update, and move data. Learn about the blockchain and the implications of decentralized, encrypted data storage for business and society, in this course with Dr. Jonathan Reichental. He begins by explaining exactly how the blockchain works, including the concepts of cryptography, mining, distribution, and smart contracts. Next, he describes how the blockchain enables the most popular cryptocurrency, bitcoin. Learn how new bitcoin is generated, how it is transacted, and the requirements for participating as either an exchange, a trader, or a miner. The course also covers other popular technology manifestations of blockchain, such as Hyperledger and Ripple. Dr. Reichental then puts blockchain technology in context in several different industries, including energy and security, and helps learners understand how to think about the blockchain in ways that can be applied in any field.
Topics include: • Blockchain basics • Public and private keys • How blockchain enables bitcoin • Blockchain and the electrical grid • Blockchain and identity management • Risks of blockchain

1.Introduction
01.Welcome
02.What you should know



1. 1. Trusting the Internet
03.Blockchain overview
04.Blockchain - The basics
05.Blockchain - Beyond the basics



3.2. How Does Blockchain Work
06.What are public and private keys
07.Introducing nonces, hash functions, and mining
08.The immutable distributed ledger



4.3. How Blockchain Enables Bitcoin
09.The birth of bitcoin
10.How bitcoin is created and managed
11.Financial services and bitcoin
12.Altcoins, altchains, and ICOs



5.4. The Wider Blockchain Universe
13.Blockchain in practice - Hyperledger and Ripple
14.Using Ethereum to write smart contracts



6.5. Implementing Blockchain
15.The energy sector and blockchain
16.Identity management and blockchain



7.6. Risks
17.The risks of blockchain



8 Conclusion
18.Next steps



IoT Foundations: Fundamentals with Bruce Sinclair

38m 9s • COURSE
The Internet of Things (IoT) is more than just smart or connected devices. In this course, learn what IoT is, and how it works from a technical standpoint. Bruce Sinclair provides a broad overview of IoT, explaining each of its main components. He also goes into the software-defined product—the digital twin of the physical product—as well as the hardware-defined product, explaining how sensors and embedded systems help to gather data. Plus, he dives into the network fabric, and explains what external systems are and why it's important to consider them when designing an IoT product.
Topics include: • IoT value modeling • The software-defined product • The hardware-defined product • Embedded systems • Connected sensors • The network fabric • OT IT and fog networks • Analytics and big data • Data services

1. Introduction
01. Welcome
02. What you should know
03. Architectural overview



2. 1. The Software-Defined Product
04. Software overview
05. Cybermodel
06. Application
07. IoT value modeling



3. 2. The Hardware-Defined Product
08. Hardware overview
09. Sensors
10. Embedded systems
11. Connected sensors



4. 3. The Network Fabric
12. Overview and standards
13. OT IT and fog networks
14. IoT product cloud
15. IoT platform



5. 4. External Systems
16. Overview
17. Analytics and big data
18. Data services
19. Business systems
20. Other IoT products



06. Conclusion
21. Main takeaways
22. What was not included
23. Next steps



IoT Foundations: Standards and Ecosystems with Zahraa Khalil

1h 32m • COURSE
The continuous development of IoT ecosystems promises a future in which our homes, cars, and cities are more connected. But as this technology evolves, it has become increasingly important to tackle the communication and compatibility challenges facing the billions of connected devices already out in the world. In this course, learn about IoT standards and ecosystems. Instructor Zahraa Khalil provides a high-level look at the IoT ecosystem, discussing IoT markets, technology, and standards. She also goes over the challenges facing the development of IoT—including security, privacy, and legal issues—the IoT standards development process, and IoT protocols for device management. Plus, she shares examples of IoT ecosystems in action by exploring case studies of smart cities.
Topics include: • IoT architecture models and components • IoT ecosystem • IoT technology, markets, and standards • IoT technology • Security, privacy, and legal issues • IoT device management • IoT protocols for device management • IoT device management challenges and applications Smart cities

1. Introduction
01. Welcome
02. What you should know



2. 1. IoT Overview
04. Software overview
05. Cybermodel
06. Application
07. IoT value modeling
007 IoT architecture example - Ehealth



3. 2. The IoT EcoSystem
008 Introduction to the IoT ecosystem
009 IoT technology
010 IoT markets
011 IoT standards
012 IoT application example - Smart homes



4. 3. IoT Challenges
013 Security
014 Privacy
015 Complexity and standards
016 Legal issues



5. 4. IoT Standards
017 Standardization process
018 Standardization development process
019 IEEE IoT standards
020 IoT commuication protocols, part 1
021 IoT communication protocols, part 2



5. 4. IoT Device Management
022 IoT device management fundamentals
023 IoT protocols for device management
024 IoT device management challenges
025 IoT device management applications



6. 5. IoT Action: Smart Cities
026 Smart cities
027 Case studies - Singapore and Barcelona
028 Case studies - London, San Francisco, and Oslo



06. Conclusion
029 Next steps



Agile Development Practices with Harrison Ferrone

26m 29s • COURSE
The agile methodology has become a staple in the software development industry for its rapid development capabilities, iterative workflow, and improved team dynamics. In this course, Harrison Ferrone covers agile practices aimed at addressing the complexities and limitations unique to mobile projects. He takes you from the concept and ideation phase all the way to continuous integration and deployment, highlighting best practices and efficient planning. By the end of this course, you will have a foundation for bringing agile into your development process and streamlining your existing project pipeline.
LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.
The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.
Topics include: • Planning and design • Developing flexible architecture • Documentation considerations • When to optimize • Determining velocity • Agile programming techniques • Refactoring • Testing and handling bugs • Structuring a release pipeline

1. Introduction
01. Welcome
02. What you should know



2. 1. Planning
003 Getting involved early
004 Picking your tools
005 To document or not



3. 2. Design
006 Flexible architecture
007 Avoiding premature optimization



4. 3. Development
008 Determining velocity
009 Agile programming techniques
010 Refactor, then refactor more



5. 4. Testing
011 Choosing your testing regimen
012 Handling leapfrogging bugs



5. 4. Release
013 Continous Integration
014 Structuring a release pipeline



06. Conclusion
015 The big picture



Implementing an Information Security Program with Kip Boyle

2h 31m • COURSE
Building and operating an information security program at your organization can be challenging. The scope can be vast and complex. Thinking of all the ways an organization can fail and coming up with actionable measures you can take to prevent issues, mitigate risk, or recover from events is a large undertaking. In this course, Kip Boyle, president of Cyber Risk Opportunities, guides you through the entire process of creating an information security program, rolling it out to your organization, and maintaining it for continuous risk management.
Topics include: • Goals and components of an information security program • Measuring and managing information risks • Reducing risks to an acceptable level • Using a workflow to organize your work • Communicating progress with executives and stakeholders • Demonstrating compliance

01. Introduction
01_01-Welcome
01_02-What you should know
01_03-Information security overview
01_04-Cybersecurity overview
01_05-Cyber resilience overview
01_06-Risk management overview



02. Information Security Program Goals
02_01-Achieve your customers expectations
02_02-Cyber attack and failure resilience
02_03-Comply with laws and regulations
02_04-Support executives and the BOD



03. Information Security Program Components
03_01-Essential functions of a program
03_02-Determine your role
03_03-Build a team
03_04-The need for management
03_05-The need for leadership



04. Structure an Information Security Program
04_01-Sources of controls
04_02-Organize around cyber resilience
04_03-Design an information security program



05. Measure Information Risks
05_01-Plan to measure information risks
05_02-Use a datadriven cyber risk management method
05_03-Understand the 0 to 10 scale
05_04-Set target scores for each control
05_05-Decide where to measure information risk
05_06-Create a score key for experts
05_07-Prepare to collect scores from experts
05_08-Set up a score collection workflow
05_09-Collect scores from your systems



06. Understand Information Risks
06_01-The questions that drive us
06_02-Determine resilience
06_03-Determine the top five risks
06_04-Understand the leadership landscape



07. Manage Information Risks
07_01-Generate ideas to manage top risks
07_02-Estimate costs
07_03-Estimate benefits
07_04-Prepare proposals



08. Demonstrate Compliance and Progress
08_01-Communicate with executives
08_02-Communicate with stakeholders
08_03-Communicate with auditors



09. Use a Workflow to Organize Work
09_01-Determine measurement frequency
09_02-Build on baseline measurements
09_03-Construct an annual program of work



10. Conclusion
10_01-Next steps