Practicing data science: A collection of case studies

Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.

There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with real-time or close-to-real-time execution requirements and with acceptably slower performances; showing the results in shiny reports or hiding the nitty and gritty behind a REST service; and, last but not least, with large budgets or no budget at all. Rosaria Silipo shares a collection of past data science projects. While the structure is often similar—data collection, data transformation, model training, deployment—each required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions. You’ll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join in to learn what’s possible in data science.