Becoming a Good Data Science Customer

Course details

The world of data science has created radical shifts in the way we work, play, shop, socialize, and learn. And as it continues to grow in importance across many different industries, business leaders need to know how to communicate effectively with data science teams to maximize their return on investment. In this course, Columbia University professor Howard Friedman gives you an overview of the most critical questions and tools to probe data scientists about key topics related to data collection, data storage, data analysis, hardware and software, data modeling, ethics, and more. Along the way, explore the basic technical lingo, recognize the types of talent on the team, and learn how to pose well-formed questions to data scientists to gather better insights, create opportunities, and generate value as well as challenge key assumptions. By the end of this course, you’ll be equipped with the right questions and tools to start making more profitable data-driven business decisions.

Concepts

Introduction

Good data science customers ask critical questions
What you should know for this course

Tools of the Trade

Stages of data workflow
Data storage options
Data sources
Ensuring data quality
Coding languages and repositories
Data products
Data workflow exercise

Descriptive Statistics Foundations

Data limitations
Summary statistics
Correlations
Testing hypothesis - Effect sizes and p-values
Common mistakes

Making Good Decisions with Data

Causality
Benefits of randomization
What if you can't randomize
Spotting biases
Matching description to bias exercise

Customer Segmentation

What projects involve unsupervised machine learning
Reducing dimensions
Clustering algorithms

Predictive Modeling

What projects involve predictive modeling
Feature selection
Model training and testing (data partitioning)
Model tuning
Measuring model performance
Confusion matrix
Customer simulation exercise

Other Modeling Methods

Natural language processing (NLP)
Geospatial analysis
Computer vision
Network analysis
Large language models (LLM)

Ethics

Data science code of ethics
Bias and fairness
Data drift
Privacy and security

Conclusion

Next steps