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MBA610: Advanced Business Intelligence and Analytics
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COURSE INTRODUCTION
START HERE → Orientation
MBA610 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
Study Guide: Unit 1
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
Study Guide: Unit 2
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
Study Guide: Unit 3
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
Study Guide: Unit 4
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
Study Guide: Unit 5
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
Study Guide: Unit 6
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
Study Guide: Unit 7
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
Study Guide: Unit 8
Study Guide: Unit 8
Unit 8 Assessment
Unit 8 Assessment
Study Guide
MBA610 Study Guide
Course Feedback Survey
Course Feedback Survey
Practice Final Exam
MBA610: Practice Final Exam
Final Exam
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Course info
MBA610: Advanced Business Intelligence and Analytics
Time: 83 hours
Explore how BI supports managerial decision-making, from data- and text-mining to warehousing and conducting analytics, and learn how to effectively report what you learn from data by creating visualizations to communicate your analysis.
Skill Level
:
Beginner
