Overview
and
Objectives

What is Data Science – Keith McCormick

  • What is the Organisational Value of Data Science?
    • Two High-Value Use Cases
    • Deriving Value from Analytics
    • Analytic Stages and ROI
    • The Relationship Between Data Science and High ROI Analytics
    • Top Three Sources of High ROI
  • How is Data Science Different from Data Analytics?
    • A Short History of Analytics
    • Three Types of Analytics
      • Descriptive Analytics
      • Predictive Analytics
      • Discovery
    • Data Science Analytic Methods, the Same but Different
      • Statistics
      • Data Mining
      • Machine Learning
    • Comparison and Cautions of Data Science Analytics vs. Regular Analytics
  • What are the Skills Needed for Data Science?
  • What Does a Data Scientist Do All Day?
    • Data Scientist Fundamental Skills
    • Characteristics of Data Scientists

Overview
and
Objectives

Data Science Core Concepts

  • Orientation to Big Data
    • The Official Definition
    • The Unofficial Definition
    • Some Executives’ Definitions
    • The “Real” Definition
    • A Strategic Definition
    • My Working Definition
  • Trends within the analytically competitive organisation
  • The advent of Data Science
    • The Arena: From business unit-based to IT department-based
    • The Professionals: From analyst to data scientist
    • The Analyses: From descriptive analyses / business intelligence to predictive analyses / data mining / machine learning
  • What is predictive analytics’ role in Big Data?
    • Big data needs advanced analytics …but does analytics need big data?
    • You will never have a perfect model
    • Market perceptions of big data
  • ROI of data science, big data and associated analytics
    • Retail use case
    • Guerrilla marketing use case
    • Medical or government use case
  • The future of data science, big data and advanced analytics

Overview
and
Objectives

How to think like a Data Scientist

  • Stats 101 in ten minutes
  • A / B testing and experiments
  • BI vs predictive analytics
  • IT’s role in predictive analytics
  • Statistics and machine learning: complementary or competitive?
  • Primary project types
    • Predicting a value given specific conditions
    • Identifying a category given specific conditions
    • Predicting the next step in a sequence
    • Identifying groups
  • Common analytic algorithms
    • Regression
    • Decision Trees
    • Neural Networks
    • Genetic Algorithms
    • Ensemble Modeling
  • Popular tools to manage large-scale analytics complexity
    • R and Python
    • Hadoop, MapReduce and Spark
    • Data Mining “workbenches”
  • Performing a data reconnaissance
  • Building the analytic sandbox
  • Preparing train / test / validation data
  • Defining data sufficiency and scope

Overview
and
Objectives

The CAO’s Roadmap

  • The Modeling Practice Framework™
  • The elements of an organisational analytics assessment
  • Project Definition: The blueprint for prescriptive analytics
  • The critical combination: predictive insights & strategy
  • Establishing a supportive culture for goal-driven analytics
  • Defining performance metrics to evaluate the decision process
  • What is the behavior that impacts performance?
  • Do resources support stated objectives?
  • Leverage what you already have
  • Developing and approving the Modeling Plan
  • Selecting the most strategic option
  • Planning for deployment
    • What will the operational environment be?
    • Who or what is the end consumer?
    • How do results need to be purposed or presented?
  • Measuring finalist models against established benchmarks
  • Preparing a final Rollout Plan
  • Monitoring model performance for residual benefit

Overview
and
Objectives

Building the goal-centered analytics operation

  • Attracting and hiring the right analytic talent
  • The roles and functions of the fully-formed analytic project team
  • Specialisation in analytic project teams
  • Analytic opportunity identification, qualification and prioritisation
  • Organisational resistance and developing a culture for change
  • Project failure is not the worst outcome
  • Staging the organisational mind shift to data-driven decisioning
  • Motivating adoption by domain experts, end users and leadership
  • Recording ongoing organisational changes
  • Monitoring and advancing organisational analytic performance
  • “Democratising” analytics: Advantages and risks of “self-service”
    • Tableau
    • Watson Analytics
    • Establishing performance dashboards
  • Standing up an agile analytic modeling factory
  • Knowledge retention and skill reinforcement
  • The Future of Data Science and Advanced Analytics
  • From Rhetoric to Reality
  • Biggest Driver of Analytic Innovation
    • Continually Improving Productivity and Profitability
    • Predicting Problems Before They Happen Becomes the New Norm
    • Changing Ever More Operational Models
  • What’s Next in Data Science?
  • Defining Your Organisation’s Data Science Reality