Data Private

Data-Driven Decision Making (DATA250)

2 days
Data

Use a structured analytics process, statistics, and regression to build evidence, test its integrity, and present defensible recommendations to leaders.

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Course Overview

This course is designed for leaders and analysts who must replace intuition with empirical evidence when the stakes are highest. Participants follow the analytics process model from framing a well-defined problem and acquiring data through statistical analysis and predictive modeling, and on to interpreting analytical outputs. They learn to apply critical thinking, challenging analytical outputs, weighing alternatives and risk, and presenting defensible recommendations for strategic and operational decisions.

Course Benefits

  • Use a structured decision workflow and analytics process model to turn a management concern into a well-defined problem and analysis plan.
  • Acquire, structure, and query relevant internal and external data for use in an analysis.
  • Apply statistical concepts and regression techniques to analyze business data, create defensible predictions, and apply critical thinking to judge what the evidence does and does not support.
  • Assess analytical outputs for data integrity, bias, and ethical risk, applying the scrutiny expected in federal data environments.
  • Transform complex findings into clear visualizations and compelling data stories that recommend a course of action to stakeholders.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Course Outline

  1. Day 1: Framing the Decision and Analyzing the Evidence
    1. Data-Driven Decision Making and the Analytics Process Model: Moving beyond intuition; framing a well-defined problem and analysis plan; integrating evidence into a decision.
    2. Collecting and Querying Data from Diverse Sources: Internal and external data sources; data management and querying; acquiring data into the analysis environment.
    3. Statistical Foundations for Decision-Makers: Distributions and variability; hypothesis testing and p-values; confidence intervals; what evidence does and does not support.
    4. Regression and Modeling for Prediction: Scatterplots, correlation, and relationship strength; simple linear regression in Excel; predictions; correlation versus causation.
  2. Day 2: Integrity, Visualization, and the Decision Briefing
    1. Data Integrity, Bias, and Ethical Responsibility: Bias and poor data quality; red flags in analysis; federal data environments; scrutinizing work before trusting it.
    2. Visualization for Decision-Makers: Matching chart types to message; visualization best practices; removing chart junk; common visualization mistakes that damage credibility.
    3. Data Stories and Audience Adaptation: Context-Insight-Action structure; audience archetypes and their needs; the data story arc; moving stakeholders from numbers to action.
    4. Capstone: The Decision Briefing: The complete decision process; integrating problem, analysis plan, data, and results; evaluating alternatives; recommending a decision.

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Data class:

Data Analysis Fundamentals or equivalent experience framing analytical questions and summarizing data.

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