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    Course Introduction
    Course Syllabus
    Unit 1: Business Intelligence Overview
    1.1: What is Business Intelligence?
    Business Intelligence
    Introduction to Business Intelligence
    1.1.1: What Business Intelligence is Not
    Frontiers of Business Intelligence and Analytics
    Business Intelligence Dashboards
    1.1.2: Business Intelligence vs. Competitive Intelligence
    What is Competitive Intelligence?
    1.1.3: From Systems Engineering to Business Engineering
    Information Architecture Analysis
    Systems Engineering
    Business Engineering
    1.2.1: Contemporary Applications
    Business Intelligence in ERP
    Improving Outcomes with Business Intelligence
    How Businesses Use Information
    1.2.2: BI Approaches for Each Lifecycle Stage
    The Business Cycle
    Big Data Analytics in Supply Chain Management
    1.2.3: BI for Prediction
    Goal-Oriented BI
    Big Data Analytics
    BI System Effectiveness
    Data Mining Analytics for BI and Decision Support
    1.3: The Future of BI
    Future Trends in Information Systems
    Internet Trends
    Trends in Information Technology
    Technology Trends in the COVID-19 Pandemic
    The Future of BI
    1.3.1: Adapting Business Models to Globalization and Technology
    Global Business Strategies for Responding to Cultural Differences
    Internationalization and the Need of Business Model Innovation
    1.3.2: Maintaining the Firm-Centric Approach
    Designing BI Solutions in the Era of Big Data
    1.3.3: Incorporating Data from the Internet of Things (IoT)
    The Internet of Things
    The Cognitive Internet of Things and Big Data
    Data Science in Heavy Industry and the Internet of Things
    Causality and Variables
    The Internet of Things is Revolutionary
    Unit 1 Discussion
    Unit 1 Study Resources
    Unit 1 Review Video
    Study Guide: Unit 1
    Unit 1 Assessment
    Unit 1 Assessment
    Unit 2: BI as Business Support
    2.1: Defining the Problem
    Choice and Happiness
    2.1.1: Framing Internal Client Discussions
    Overview of Managerial Decision-Making
    2.1.2: Drafting the Terms of Reference (TOR)
    Defining the Scope of your Project
    Developing Terms of Reference
    2.1.3: Negotiating the Project Scope
    Scope Planning
    Negotiation
    2.2: The Art and Science of Decision-Making
    Decision-Making in Management
    Decision-Making Processes in the Workplace
    2.2.1: Thinking about Thinking
    Experience vs. Memory
    Evidence Logs and Metacognitive Logs
    2.2.2: Use Analysis, or "Go with Your Gut"?
    Problem Solving, Thinking, and Intelligence
    Using a Heuristics Checklist
    2.2.3: Decision-Making Approaches
    Decision-Making Tools
    2.2.4: Structuring Decision-Making Effectively
    RAPID Decision-Making
    2.3: Using Data to Make Decisions
    Business Intelligence Dashboards
    2.3.1: Everyday Data
    2.3.2: Why Expert Judgement is No Better than Yours
    Why You Think You're Right Even if You're Wrong
    2.3.3: How Forecasting can Help Decision-Making
    System Interventions
    Short-Term Decision-Making
    Unit 2 Discussion
    Unit 2 Study Resources
    Unit 2 Review Video
    Study Guide: Unit 2
    Unit 2 Assessment
    Unit 2 Assessment
    Unit 3: Data Mining and Text Mining
    3.1: Understanding Big Data
    Using Text Mining Techniques to Identify Research Trends
    Text Mining Techniques, Applications, and Issues
    Data Mining Applications and Trends in Data Mining
    A Review of Data Mining Techniques and Trends
    3.1.1: What is Big Data?
    Big Data
    What is Big Data?
    An Introduction to Big Data
    3.1.2: Where Does Big Data Live?
    How Much Data Do You Produce?
    Big Data in Healthcare
    3.2: Data and Text Mining
    Data Mining from C4DLab
    Getting Started in Text Mining
    3.2.1: Data Mining Techniques
    Practical Real-world Data Mining
    Text and Data Mining (TDM) Explained
    Information, People, and Technology
    3.2.2: Text Mining and the Complications of Language
    Introduction to Text Mining
    Text Mining
    3.3: Evaluating Source Data
    Data as Sources
    Types of Data Sources
    Evaluating Sources
    3.3.1: Identifying Data Sources
    Data Lineage
    3.3.2: Source Evaluation Trust Matrix
    A Trust Evaluation Model for Wireless Sensor Networks
    A Trust Evaluation Model for Cloud Computing
    3.4: Data Optimization
    Data Science and AI-Based Optimization in Scientific Programming
    3.4.1: Preparing Data
    Data Mining Techniques in Analyzing Process Data
    Data Preparation by Developers
    Knowledge Discovery in Data-Mining
    3.4.2: Standardization
    Big Data Analytics for Disparate Data
    Types of Statistical Studies and Producing Data
    3.4.3: Combining Data from Different Sources
    Capturing Value from Big Data
    Types of Statistical Studies and Producing Data
    Unit 3 Discussion
    Unit 3 Study Resources
    Unit 3 Review Video
    Study Guide: Unit 3
    Unit 3 Assessment
    Unit 3 Assessment
    Unit 4: Data Warehousing and Integration
    4.1: The History of Data Storage
    Writing
    A Brief History of Big Data
    Data Sharing and the Future of Science
    Data Warehousing and Data Mining
    4.1.1: Early Days
    Evolution of Data Storage
    Before the Advent of Database Systems
    4.1.2: The Evolution of Data Storage
    Modeling and Management of Big Data in Databases
    A Literature Survey on Big Data
    Data Storage
    4.2.1: How Data Warehousing Works
    Big Data Management
    Data Warehouse Strategies
    Data Warehousing and Data Mining
    4.2.2: Common Methods and Tools
    Methodologies for Data Warehousing
    Building an Effective Data Warehouse
    4.3.1: Local Data vs. Cloud Storage
    Hardware Development Trends
    Big Data and Business Analytics Trends
    Business Analytics Toolkit
    4.3.2: Beyond the Cloud
    Opportunities and Challenges for Data, Models, Computation, and Workflows
    The Era of Cloud Computing
    Unit 4 Discussion
    Unit 4 Study Resources
    Unit 4 Review Video
    Study Guide: Unit 4
    Unit 4 Assessment
    Unit 4 Assessment
    Unit 5: Data Analytics
    5.1: Overview of Data Analysis
    Data Analysis: A Basic Definition
    Introduction to Statistical Analysis
    Data Analysis
    5.2: Analytic Techniques
    Uncertainty in Big Data Analytics
    5.2.1: Decision trees
    Decision Trees: A Brief Introduction
    Using a Decision Tree
    Classifying Data with Decision Trees
    5.2.2: Structured Analysis of Competing Hypotheses (SACH)
    Structured Analytic Tools
    Analysis of Competing Hypotheses
    Dare to Disagree
    5.2.3: Predictive Modeling
    How Can Predictive Modeling Change the World?
    Big Data is Better
    Prediction and Inference in Data Science
    Better Predictive Modeling with Data Preparation
    5.2.4: Other Popular Methods
    The Structured Analytic Techniques "Toolbox"
    Examples of Other Approaches
    5.3: Real-World Problem-Solving
    Real World Problem-Solving
    Models and Applications
    5.3.1: Scenarios
    Role-Playing in Intelligence Analysis
    Reintroduction to Business Intelligence
    5.3.2: Simulations
    Simulation Approach to Decision-Making
    Unit 5 Discussion
    Unit 5 Study Resources
    Unit 5 Review Video
    Study Guide: Unit 5
    Unit 5 Assessment
    Unit 5 Assessment
    Unit 6: Data Reporting and Visualization
    6.1: Effective Data Visualization
    Interactive Visualizations of Big Data
    6.1.1: A Picture Really Is Worth a Thousand Words!
    The Beauty of Data Visualization
    Visualizing Big Data with Augmented and Virtual Reality
    6.1.2: Interpreting and Evaluating Visualizations
    Building Effective Data Visualizations for Business Intelligence
    A Survey of Visualizing Business Data
    Data Visualization
    Why is Data Visualization Important?
    6.1.3: Excel and Other Visualization Products
    Preparing Data
    Overview of Data Visualization Tools
    Tools for Data Visualization
    How to Use Data Visualization Tools
    Designing a Data Visualization
    6.2.1: On-Line Analytical Processing (OLAP)
    Integrated Decision-Making Based on OLAP
    Introduction to Online Analytical Processing
    6.2.2: How Can Machines and Humans Work Together?
    GPT-3
    GPT-3 in Layman's Terms
    Will GPT-3 Take Over Jobs?
    6.2.3: What Happens to Humans When Machines Learn Faster?
    "Data Cubes" for Large-Scale Psychometric Data
    Robotics, Artificial Intelligence, and the Workplace of the Future
    Machine-based Collective Intelligence and the Human Experience
    Artificial Intelligence and the Future of Humans
    What the Educated Citizen Needs to Know about Data Science
    6.3.1: How to Write a Report that Influences Managerial Decisions
    Management Information Systems and Decision Support Capabilities
    Major Characteristics of the Manager's Job
    Requirements of Successful Managers
    6.3.2: The Art of the One-Page Memo
    Work Memos
    Memo Purpose and Format
    Memorandums and Business Letters
    6.3.3: BLUF (Bottom-Line Up Front) instead of BLAB (Bottom Line At Bottom)
    BLUF
    6.3.4: Evaluating and Expressing Confidence Levels
    Calculating Confidence Intervals
    What is a Confidence Interval?
    Introduction to Confidence Intervals
    Unit 6 Discussion
    Unit 6 Study Resources
    Unit 6 Review Video
    Study Guide: Unit 6
    Unit 6 Assessment
    Unit 6 Assessment
    Unit 7: Data Analysis Dashboards
    7.1: Elements of a Dashboard
    Universal Dashboards
    7.1.1: Form Over Function?
    Form Follows Function
    7.1.2: Gauging User Experience
    Usability Evaluation Basics
    7.2: Using a Dashboard
    Performance Dashboard Design
    Business Intelligence Dashboards
    The UNHCR Dashboard
    7.2.1: Dashboards for Monitoring
    Effective Infrastructure Monitoring
    7.2.2: Dashboards for Predicting
    Marketing Prediction Example
    7.3: Common Designs, Uses, and Limitataions
    Open-Source Dashboard Tools
    Refreshing Your Nonprofit Dashboard
    Dashboard Design Limitations
    7.4.1: Determining Appropriate Performance Indicators
    Mobile Business Intelligence Acceptance Model
    Using Excel to Track KPIs
    7.4.2: Selecting a KPI Template
    5 KPIs Every Business Must Consider
    Pitfalls of KPIs
    Unit 7 Discussion
    Unit 7 Study Resources
    Unit 7 Review Video
    Study Guide: Unit 7
    Unit 7 Assessment
    Unit 7 Assessment
    Unit 8: Project Management
    8.1: Reviewing Requirements
    Requirements Management
    Project Implementation
    Project Management
    8.1.1: Did We Answer the Question?
    Documenting and Managing Requirements
    8.1.2: Was the Question the Right One?
    Why are Questions so Difficult?
    8.2: Report Formatting and Accountability
    Progress Reports
    Memo Example: Presidential Daily Briefing
    8.2.1: Using Style to Enhance Credibility
    Communicating with Precision
    Rules for Effective Intelligence Writing
    Looking at the Fine Print
    Analytic Confidence
    8.2.2: Know Your Decision-Maker
    What to Know Before You Discuss with a Decision-Maker
    Evidence-Based Decision Making
    8.3.1: Small Group Dynamics
    Why Do Teams Succeed?
    Group Development
    Group Size and Structure
    8.3.2: Managing Teams
    Things to Consider when Managing Teams
    Building Successful Teams
    8.4.1: Risks and Rewards
    Lessons from BI in Public Institutions
    8.4.2 :Going with Your Gut
    Lessons on Decisionmaking from a Champion Poker Player
    Understanding the World Around You
    8.4.3: Mentorship and Growth
    Learning Theories
    Personal and Organizational Growth
    8.4.4: Identifying and Managing Risks
    Risk Management Planning
    Identifying Opportunities
    Unit 8 Discussion
    Unit 8 Study Resources
    Unit 8 Review Video
    Study Guide: Unit 8
    Unit 8 Assessment
    Unit 8 Assessment
    Study Guide
    BUS610 Study Guide
    Course Feedback Survey
    Course Feedback Survey
    Certificate Final Exam
    BUS610: Certificate Final Exam
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  1. BUS610: Advanced Business Intelligence and Analytics
  2. Unit 8: Project Management
  3. 8.4: BI Management Approaches
  4. 8.4.2 :Going with Your Gut
  5. Lessons on Decisionmaking from a Champion Poker Player

Lessons on Decisionmaking from a Champion Poker Player

Completion requirements

This brief talk by a champion poker player discusses the importance of analytic reasoning and information in making the best decisions and why we need to leave our intuition at home to rest up for small determinations with little consequence when we have big decisions that require data and analysis. What are the three lessons? When have you followed these lessons? Did they work well or not?


Source: Liv Boeree, https://www.ted.com/talks/liv_boeree_3_lessons_on_decision_making_from_a_poker_champion
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Last modified: Thursday, March 16, 2023, 6:43 PM
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Saylor Academy © 2010-2025 except as otherwise noted. Excluding course final exams, content authored by Saylor Academy is available under a Creative Commons Attribution 3.0 Unported license. Third-party materials are the copyright of their respective owners and shared under various licenses. See detailed licensing information. Saylor Academy®, Saylor.org®, and Harnessing Technology to Make Education Free® are trade names of the Constitution Foundation, a 501(c)(3) organization through which our educational activities are conducted.