Business Analytics for Executives

Business Analytics for Executives

Aim of Course:


"For predictive analytics to work, two different species must cooperate in harmony: the business leader and the quant." (J. Deal and G. Pilcher in Mining Your Own Business)


This course will equip the business leader with the tools needed for that harmonious cooperation.  It features enough hands-on activity to provide the degree of comfort and facility needed to collaborate effectively with technical staff, but its goal is not to turn business generalists into coders.

After taking this course, you will have an understanding of key business analytics methods, including predictive analytics, time series forecasting, customer segmentation and more, at a level of detail sufficient to be able to:

1) Communicate with top and other senior management knowledgeably about the expected ROI from an analytics effort (and the constraints and uncertainties involved), and

2) Collaborate with IT and technical people about the specification and feasibility of an analytics project, so that business problems are effectively framed as data science problems.

This course is for

  • Directors, VP's
  • Senior Managers
  • Functional Business Managers and Heads
  • Those with oversight of data science or analytics teams

The format of the course is a series of hour-long webinars laying out the analytical methods, each accompanied by self-study exercises, followed by two webinars focusing on case studies in which participants will discuss the potential role for different analytical methods.

Course Program:


Session 1:  Introduction to Predictive Analytics 

  • Motivating example - Target predicts pregnancy
    • Application of KNN
  • The supervised learning paradigm
    • Detailed example - predicting house prices
  • Use of XLMiner (Excel add-in from Frontline)
  • Self-study exercises (1-hour)

Session 2:  Key Concepts in Statistics  

  • Randomness and chance
  • Probability
  • A-B tests
  • Hypothesis tests and p-values
    • Resampling
  • The normal distribution, z-scores
  • Outliers
  • Regression
  • Self-study exercises (1-hour)

Session 3:   The data  

  • Big data
    • When big is better (e.g. Google search)
    • When big is not necessarily better
      • Sampling
  • Forms of data
    • The spreadsheet model
    • Lists
    • Time series
    • Unstructured text
    • Images
    • Voice
  • Variables
    • Continuous, categorical
    • Predictor, outcome
  • Sources of data
    • Collected for studies
      • Study designs
    • Internal database - SQL
    • Other internal - NoSQL
    • Public - API
    • 80/20 rule about data prep
    • Surveys

Session 4:  Exploration and Visualization

  • Explore the data
    • Summary stats
    • Correlations
    • Visualization
  • Understanding and quantifying random variaion
    • Measurement and sampling error
    • Signfiicance testing
  • Self-study exercises (1-hour)

Session 5 - Machine and Statistical Learning Methods 

  • Goals
    • Describe
    • Explain
    • Predict/classify
    • Prescribe
  • Machine learning methods
    • Decision Trees
    • Neural nets
    • KNN
    • Bayesian classifier
  • Statistical learning methods
    • Multiple linear regression
    • Logistic regression
    • Discriminant analysis
  • Ensembles (wisdom of the crowd)
  • Self-study exercises (1-hour)


Session 6:  Unsupervised learning

  • Dimension reduction
    • Principal Components
  • Clustering
  • Recommender systems
  • Collaborative filters
  • Association rules
  • Spatial analysis

Session 7:  AI and Deep Learning 

  • Unsupervised learning
    • Recommender systems
    • Clustering (segmentation)
    • Dimension reduction
  • Deep learning
    • Neural nets on steroids
    • Unsupervised derivation of features
    • Applications
      • Image recognition
      • Voice recognition
      • Standard ML applications


Session 8:  Text & Network Analytics, Security 

  • Text Mining
    • As extension of prediction
    • Concept derivation
  • Natural Language Processing
  • Network (graph) analysis
    • Social network data
  • Security, ethical issues
  • Trading privacy for free services
  • Analytics as anti-fraud weapon
  • Gaming the system (ads, group-think)

Session 9 - Cases

  • Predicting cancelled rides (rideshare)
  • Predicting which marketing message should go to which individual consumers
  • Identifying segments of customers for bath soap
  • Evaluating & improving ROI on digital marketing

Session 10 - Cases (cont.)




Business Analytics for Executives<br>Anything

Who Should Take This Course:
  • Directors, VP's
  • Senior Managers
  • Functional Business Managers and Heads
  • Those with oversight of data science or analytics teams

The course consists of

  1. Presentations of various specific analytic methods (webinars)
  2. Illustrations for each method
  3. Selected hands-on exercises using an Excel add-in
  4. Webinar discussion of business cases 



Course Text:
All materials will be provided
XLMiner and Tableau


To be scheduled.

Business Analytics for Executives<br>Anything


To be scheduled.

Course Fee: $

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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