Online Short-Course | In collaboration with Babson College

Analytics for Decision Making

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.


No Need to Travel

01 March 2019

R1 330,00

4 Weeks

4-6 hrs effort per week

  • Variability in the real world and implications for decision making
  • Data types and data quality with appropriate visualizations
  • Apply data analysis to managerial decisions, especially in start-ups
  • Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Week 1: Distributions, Estimation & Hypothesis Testing

  • Week 1 Overview
  • Data Types
  • Data Distribution
  • Survey and Sampling Overview
  • Estimation for Proportions
  • Point Estimates and Confidence Intervals
  • Estimation for Means
  • Confidence Interval for Population Mean
  • Hypothesis Testing Overview
  • Week 2: Avoiding Fallacies in Quantitative Reasoning

  • Week 2 Overview
  • Action and Consequences
  • Constructing a Story
  • Steering a Story: Words Matter
  • Steering a Story: Priors Matter
  • Week 1: Distributions, Estimation & Hypothesis Testing

  • How a clear definition of done drives acceptance by all key stakeholders.
  • Measuring performance and benefits of working solutions during project delivery.
  • Iteratively testing to gain authentic feedback on solution requirements and stability.
  • Regular retrospectives that drive continuous improvement into the team.
  • Winning and Losing
  • Week 3: Predictive Analytics

  • Week 3 Overview
  • Prediction: Then and Now
  • Regression
  • Classification
  • Overfitting
  • Week 4: Customer Analytics

  • Week 4 Overview
  • Analytics for Startups
  • Customer Retention and Cohort Analysis
  • Customer Segmentation and Clustering
  • A/B Testing

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