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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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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.

1
  • Preface

  • What’s New in the Eleventh Edition?
    Plan of the Course
    Resources, Links, and the Teradata University Network Connection

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

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

4
  • Nature of Data, Statistical Modeling, and Visualization

  • Opening Vignette: SiriusXM Attracts and Engages ...on of Radio Consumers with DataDriven 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

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

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

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

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

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

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

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

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

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

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

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

Audience

Data analysts and others interested in understanding analytic tools and concepts will benefit.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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