Explains four levels of customer targeting, from no segmentation to advanced recommendation systems, and their business applications.
A critique of traditional statistics education, arguing for a more data-driven, question-focused approach using modern tools.
Explores GPU-based data science workflows using MapD (now OmniSci) for high-performance analytics and machine learning without data transfer bottlenecks.
A team shares their diverse experiences attending and presenting at the SatRdays Cardiff conference for R programming language users.
A data scientist shares ideas for micro-projects for the Summer of Data Science, including manual annotation, package comparisons, and resource curation.
A data scientist shares tips on automating a monthly book giveaway using R, including package organization and image manipulation with the magick package.
A data scientist explains why he's attending the eRum 2018 R conference in Budapest instead of useR! this year, and provides details about the event.
A tutorial on using Daskernetes to create auto-scaling, personal Python clusters on Kubernetes for distributed computing tasks.
Explains the difference between predictive analytics and machine learning, clarifying their relationship and distinct roles.
A summary of a Python Frederick meetup featuring Christine Lee's presentation on data science tools and features available in Python.
Learn how to identify predictive analytics questions using patterns and keywords like 'which', 'will', and 'likely'.
Learn to identify predictive analytics questions using patterns and keywords like 'which', 'will', and 'likely'.
A curated list of resources and tutorials for using Docker with R to create reproducible research environments and containerized applications.
A recap of PyData Warsaw 2017, covering key talks, new package announcements, and analytics on the conference's international attendees.
Slides and resources from a talk on R programming and data science presented at the EARL Boston conference.
A curated collection of resources and tools for learning and using Regular Expressions (RegEx) in the R programming language.
A two-day workshop on survival analysis, covering data exploration, regression modeling, and practical sessions for time-to-event data.
A practical guide outlining essential tools, skills, and practice methods for beginners to start a career in data science.
Explores using machine learning algorithms to predict outcomes in the NCAA March Madness basketball tournament, analyzing data and modeling techniques.
Final part of a series on building a product classification API, covering the creation of a custom Python class and web app for categorizing product titles.
