Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A practical guide to data science in retail, covering analytics and AI use cases such as forecasting, pricing, personalization, merchandising, fraud prevention, and omnichannel operations.
A practical comparison of Python NLP libraries, focused on when to use NLTK, spaCy, scikit-learn, Gensim, Polyglot, and Transformers.
A practical overview of data science in insurance, including ten high-value use cases across underwriting, fraud detection, claims automation, retention, and operations.
A refreshed comparison of Python, R, and Scala for data science, including how the languages differ and which library ecosystems still matter most.
A refreshed 2026 view of twenty Python libraries that matter most across data wrangling, statistics, machine learning, NLP, experimentation, and production work.
A refreshed 2026 view of the R packages that matter most for wrangling, visualization, modeling, reproducible pipelines, and delivery.
A practical overview of how financial firms use data science for risk, fraud, forecasting, personalization, and operational intelligence.
A modern comparison of Hadoop 3, Hadoop 2, and Apache Spark, including what changed in Hadoop 3 and how to choose the right platform in 2026.
A practical comparison of chatbot APIs and platforms, covering orchestration, retrieval, NLU, integrations, governance, and modern assistant architecture.
A practical guide to data science in healthcare, including seven high-value applications across imaging, clinical risk, operations, patient engagement, and drug discovery.
A practical guide to data science in banking, covering analytics and AI use cases such as fraud detection, credit risk, AML, churn prediction, customer intelligence, and operations.
A 2026-safe look at what deep learning can and cannot do for Bitcoin forecasting, with a more realistic framing for model design and evaluation.