About Alex Castrounis

Alex Castrounis is the founder and CEO of Why of AI and the author of AI for People and Business. He is also an adjunct for Northwestern University’s Kellogg / McCormick MBAi program.

Alex Castrounis Posts (8)

  • Artificial Intelligence Books to Read in 2020 - 21 Jan 2020
    Here are some AI-related books that I’ve read and recommend for you to add to your 2020 reading list!
  • Big Data Architecture: A Complete and Detailed Overview - 19 Sep 2017
    Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
  • Python vs R for Artificial Intelligence, Machine Learning, and Data Science - 11 Sep 2017
    This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
  • Why Artificial Intelligence and Machine Learning? - 30 Jun 2017
    With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify ​how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
  • Gold Blog, Mar 2017What Is Data Science, and What Does a Data Scientist Do? - 23 Mar 2017
    This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.
  • Silver BlogData Science and Big Data, Explained - 14 Nov 2016
    This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.
  • Silver BlogMachine Learning: A Complete and Detailed Overview - 28 Oct 2016
    This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond).
  • Silver BlogArtificial Intelligence, Deep Learning, and Neural Networks, Explained - 14 Oct 2016
    This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.