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Foundation of Data Analytics

(FDN-DA.AE2)
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Skills You’ll Get

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1

The Value of Data

  • Opening Case
  • Introduction
  • Managers and Decision Making
  • The Business Analytics Process
  • Business Analytics Tools
  • Business Analytics Models: Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics
  • AI in Business Analytics
  • Responsible AI and Ethics
  • Summary
  • Discussion Questions
  • Closing Case 1
  • Closing Case 2
2

Working with Data

  • Some Sample Data
  • Moving Quickly with the Control Button
  • Copying Formulas and Data Quickly
  • Formatting Cells
  • Paste Special Values
  • Inserting Charts
  • Locating the Find and Replace Menus
  • Formulas for Locating and Pulling Values
  • Using XLOOKUP to Merge Data
  • Filtering and Sorting
  • Using PivotTables
  • Power Query for Data Cleaning
  • Power Pivot and Data Model
  • Dynamic Array Functions
  • Excel + AI (Copilot & Formula Generation)
  • Creating Dashboards with Slicers
  • Using Array Formulas
  • Solving Stuff with Solver
  • OpenSolver: I Wish We Didn't Need This, but We Do
3

Data Typologies and Governance

  • Opening Case
  • Introduction
  • Managing Data
  • The Database Approach
  • Big Data
  • Data Warehouses and Data Marts
  • Knowledge Management
  • Data Governance and Responsible AI
  • IT's About Business: Data Privacy in AI Systems
  • Summary
  • Discussion Questions
  • Problem-Solving Activities
  • Closing Case 1
4

Business Statistics

  • Introduction to Probability
  • Structure of Probability
  • Marginal, Union, Joint, and Conditional Probabilities
  • Addition Laws
  • Multiplication Laws
  • Conditional Probability
  • Revision of Probabilities: Bayes' Rule
  • Introduction to Hypothesis Testing
  • Testing Hypotheses About a Population Mean Using the z Statistic (σ Known)
  • Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown)
  • Testing Hypotheses About a Proportion
  • Testing Hypotheses About a Variance
  • Solving for Type II Errors
  • From Statistics to Machine Learning
  • Summary
  • Formulas
  • Supplementary Problems
  • Analyzing the Databases
  • Case - Colgate-Palmolive Makes a “Total” Effort
5

Optimization and Forecasting

  • Why Should Data Scientists Know Optimization?
  • Starting with a Simple Trade-Off
  • Data-Driven Blending Models for Product Consistency
  • Modeling Risk
  • Predictive Analytics using AI models
  • It is important to understand that
  • Predicting customer Needs at RetailMart Using Linear Regression
  • Predicting Pregnant Customers at RetailMart Using Logistic Regression
  • For More Information
  • Correlation
  • Introduction to Simple Regression Analysis
  • Determining the Equation of the Regression Line
  • Residual Analysis
  • Standard Error of the Estimate
  • Coefficient of Determination
  • Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model
  • Estimation
  • Using Regression to Develop a Forecasting Trend Line
  • Interpreting the Output
  • Machine Learning for Forecasting
  • Summary
  • Formulas
  • Supplementary Problems
  • Analyzing the Databases
  • Case - Caterpillar, Inc.
6

Programming and AI Tools for Analytics

  • Getting Up and Running with Python and R
  • Doing Some Actual Data Science
7

Data Visualization

  • Why Do We Visualize Data?
  • How Do We Visualize Data?
  • Color
  • Common Chart Types
  • When Our Visual Processing System Betrays Us
  • Every Decision Is a Compromise
  • Interactive Dashboards (Power BI / Tableau)
  • AI-Generated Insights and Storytelling
  • Ethical Visualization in the AI Era
  • Summary

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Foundation of Data Analytics

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