Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support
Know the various types of data analytics with examples, products, services, and exercises by means of introducing artificial intelligence, machine learning, robotics, chatbots, Internet of Things, and Web/Internet-related enablers with uCertify’s course Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support.
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- Delivery method: eLearning
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Test Prep
140+ Pre Assessment Questions |
140+ Post Assessment Questions |
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Outline
Lessons 1:
Preface
- What’s New in the Eleventh Edition?
- Plan of the Course
- Resources, Links, and the Teradata University Network Connection
Lessons 2:
Overview of Business Intelligence, Analytics, Da…icial Intelligence: Systems for Decision Support
- Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
- Changing Business Environments and Evolving Needs for Decision Support and Analytics
- Decision-Making Processes and Computerized Decision Support Framework
- Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science
- Analytics Overview
- Analytics Examples in Selected Domains
- Artificial Intelligence Overview
- Convergence of Analytics and AI
- Overview of the Analytics Ecosystem
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 3:
Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications
- Opening Vignette: INRIX Solves Transportation Problems
- Introduction to Artificial Intelligence
- Human and Computer Intelligence
- Major AI Technologies and Some Derivatives
- AI Support for Decision Making
- AI Applications in Accounting
- AI Applications in Financial Services
- AI in Human Resource Management (HRM)
- AI in Marketing, Advertising, and CRM
- AI Applications in Production-Operation Management (POM)
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 4:
Nature of Data, Statistical Modeling, and Visualization
- Opening Vignette: SiriusXM Attracts and Engages …on of Radio Consumers with Data-Driven Marketing
- Nature of Data
- Simple Taxonomy of Data
- Art and Science of Data Preprocessing
- Statistical Modeling for Business Analytics
- Regression Modeling for Inferential Statistics
- Business Reporting
- Data Visualization
- Different Types of Charts and Graphs
- Emergence of Visual Analytics
- Information Dashboards
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 5:
Data Mining Process, Methods, and Algorithms
- Opening Vignette: Miami-Dade Police Department I… Predictive Analytics to Foresee and Fight Crime
- Data Mining Concepts
- Data Mining Applications
- Data Mining Process
- Data Mining Methods
- Data Mining Software Tools
- Data Mining Privacy Issues, Myths, and Blunders
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 6:
Machine-Learning Techniques for Predictive Analytics
- Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures
- Basic Concepts of Neural Networks
- Neural Network Architectures
- Support Vector Machines
- Process-Based Approach to the Use of SVM
- Nearest Neighbor Method for Prediction
- Naïve Bayes Method for Classification
- Bayesian Networks
- Ensemble Modeling
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 7:
Deep Learning and Cognitive Computing
- Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
- Introduction to Deep Learning
- Basics of “Shallow” Neural Networks
- Process of Developing Neural Network–Based Systems
- Illuminating the Black Box of ANN
- Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Networks and Long Short-Term Memory Networks
- Computer Frameworks for Implementation of Deep Learning
- Cognitive Computing
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 8:
Text Mining, Sentiment Analysis, and Social Analytics
- Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales
- Text Analytics and Text Mining Overview
- Natural Language Processing (NLP)
- Text Mining Applications
- Text Mining Process
- Sentiment Analysis
- Web Mining Overview
- Search Engines
- Web Usage Mining (Web Analytics)
- Social Analytics
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 9:
Prescriptive Analytics: Optimization and Simulation
- Opening Vignette: School District of Philadelphi…ptimal Solution for Awarding Bus Route Contracts
- Model-Based Decision Making
- Structure of Mathematical Models for Decision Support
- Certainty, Uncertainty, and Risk
- Decision Modeling with Spreadsheets
- Mathematical Programming Optimization
- Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
- Decision Analysis with Decision Tables and Decision Trees
- Introduction to Simulation
- Visual Interactive Simulation
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 10:
Big Data, Cloud Computing, and Location Analytics: Concepts and Tools
- Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
- Definition of Big Data
- Fundamentals of Big Data Analytics
- Big Data Technologies
- Big Data and Data Warehousing
- In-Memory Analytics and Apache SparkTM
- Big Data and Stream Analytics
- Big Data Vendors and Platforms
- Cloud Computing and Business Analytics
- Location-Based Analytics for Organizations
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 11:
Robotics: Industrial and Consumer Applications
- Opening Vignette: Robots Provide Emotional Support to Patients and Children
- Overview of Robotics
- History of Robotics
- Illustrative Applications of Robotics
- Components of Robots
- Various Categories of Robots
- Autonomous Cars: Robots in Motion
- Impact of Robots on Current and Future Jobs
- Legal implications of Robots and Artificial Intelligence
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 12:
Group Decision Making, Collaborative Systems, and AI Support
- Opening Vignette: Hendrick Motorsports Excels with Collaborative Teams
- Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
- Supporting Group Work and Team Collaboration with Computerized Systems
- Electronic Support for Group Communication and Collaboration
- Direct Computerized Support for Group Decision Making
- Collective Intelligence and Collaborative Intelligence
- Crowdsourcing as a Method for Decision Support
- Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making
- Human–Machine Collaboration and Teams of Robots
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 13:
Knowledge Systems: Expert Systems, Recommenders,…, Virtual Personal Assistants, and Robo Advisors
- Opening Vignette: Sephora Excels with Chatbots
- Expert Systems and Recommenders
- Concepts, Drivers, and Benefits of Chatbots
- Enterprise Chatbots
- Virtual Personal Assistants
- Chatbots as Professional Advisors (Robo Advisors)
- Implementation Issues
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 14:
The Internet of Things as a Platform for Intelligent Applications
- Opening Vignette: CNH Industrial Uses the Internet of Things to Excel
- Essentials of IoT
- Major Benefits and Drivers of IoT
- How IoT Works
- Sensors and Their Role in IoT
- Selected IoT Applications
- Smart Homes and Appliances
- Smart Cities and Factories
- Autonomous (Self-Driving) Vehicles
- Implementing IoT and Managerial Considerations
- Lesson Highlights
- Questions for Discussion
- Exercises
- References
Lessons 15:
Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
- Opening Vignette: Why Did Uber Pay $245 Million to Waymo?
- Implementing Intelligent Systems: An Overview
- Legal, Privacy, and Ethical Issues
- Successful Deployment of Intelligent Systems
- Impacts of Intelligent Systems on Organizations
- Impacts on Jobs and Work
- Potential Dangers of Robots, AI, and Analytical Modeling
- Relevant Technology Trends
- Future of Intelligent Systems
- Lesson Highlights
- Questions for Discussion
- Exercises
- References