My articles

I like different aspects of data science, including people, practices, technology. My perspectives come from reflections on the experience on the field.

Data science

General:

• Evaluating GenAI solutions: science or art?

• Navigating Challenges and Harnessing Opportunities in Generative AI Implementation

• Generative AI: A Key Player in Manufacturing Transformations

• ChatGPT for data science prototyping

• Axioms: data science and cognitive biases

• What is data science?

• Why would we trust a machine learning product?, Can a machine learning product be trusted?

• What is the role of a data scientist (article on blog)

Delivery:

• Some key aspects to deliver a data science project (Post on Microsoft blog)

• Road to agile data science in 12 steps

• Agile delivery of data science projects

• Data-driven approach: how do you define and communicate it?

• Analytical discovery

Use cases:

• Reduce carbon emissions

• Optimize responses to pandemics

Scalable algorithm design

• Scaling a recommender system across large data volumes

• Validating a model in R Services using the k-fold (article on blog)

• An example of MapReduce with rmr2

• A possibility of using R and Hadoop together

• A Big Data introduction

• Academic thesis about clustering with R and Hadoop

Tools for Big Data and Analytics

• Demystifying decision forests (article on blog)

• Tutorial about R and Hadoop, co-presented at the UseR! conference in Aalborg

• Analyzing data in SQL Server 16, combining R and SQL

• Using miniCRAN in Azure ML

• R framework with Object-Oriented Programming

• R AND OOP – defining new classes

My books

• R Machine Learning Essentials

• Announcing the Publication of R Machine Learning Essentials