Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support - OnDemand
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Course Outline
Audience
Prerequisites
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.
* 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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Course Schedule:
Online Schedule
In Class Schedule
On Demand