Practicing data science: A collection of case studies
There are many delineations 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-gritty behind a REST service; and—last but not least—with large budgets or no budget at all.
Rosaria Silipo discusses some of her past data science projects, showing what was possible and sharing the tricks used to solve their specific challenges. You’ll learn about demand prediction in energy, anomaly detection in the 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.
Rosaria Silipo is a principal data scientist at KNIME. She loved data before it was big and learning before it was deep. She’s spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Rosaria shares her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, and cybersecurity, and through her 50+ technical publications, including her recent ebook, Practicing Data Science: A Collection of Case Studies. Follow her on Twitter, LinkedIn, and the KNIME blog.
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