Over the weekend I was going through a collection of data science books. Many were published between 2015 and 2018. At first glance, they looked outdated.
Then I realized something.
The tools have changed.
The foundations have not.
Linear regression still explains relationships.
Experimental design still matters.
Exploratory Data Analysis (EDA) still uncovers patterns.
Decision trees still split data.
Hypothesis testing still helps us distinguish signal from noise.
The mathematics underneath modern AI hasn’t suddenly become obsolete because ChatGPT exists.
It reminded me of something I’ve started thinking about as I develop curriculum, write my textbook, and conduct dissertation research.
Knowledge has layers.
Layer 1: Foundations (decades)
Statistics, research methods, critical thinking, decision science, and analytics. These principles often remain relevant for generations.
Layer 2: Professional Tools (years)
Excel, Python, SQL, Tableau, Power BI, cloud platforms. These evolve, but their underlying concepts remain recognizable over time.
Layer 3: Emerging Technology (months)
Large language models, AI agents, prompt engineering, NotebookLM, MCP, and whatever comes next. These tools change rapidly, sometimes within a single semester.
This way of thinking extends beyond writing textbooks.
It influences curriculum design.
It shapes professional development.
It helps organizations decide where to invest in training.
And perhaps most importantly, it reminds us that while technology will continue to evolve at an incredible pace, the strongest professionals are the ones who build on a solid foundation.
Learn the newest tools.
But never stop investing in the fundamentals.
Robert Foreman
Doctoral Candidate – Educational Technology
Central Michigan University
Research Focus:
AI-Augmented Exploratory Learning (AAEL)
How Professionals Learn with AI
Website: NhanceData.com
Email: forem1r@cmich.edu
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