Category: Tech Notes
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Linear Regression in Python From the Inside Out (Data Science Series, Part 9)
Least squares is three lines of numpy once you see the geometry. This part fits, checks and defends a linear model, then shows the collinearity failure that produces nonsense coefficients without ever raising an error.
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Statistical Inference for Machine Learning: Sampling, Confidence Intervals and What a p Value Is Not (Data Science Series, Part 8)
Every metric you report is one draw from a distribution. Here is how to put a confidence interval on it, when to bootstrap, and the three readings of a p value that quietly wreck model selection.
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Probability and Distributions a Modeller Actually Needs (Data Science Series, Part 7)
Probability is what separates a model that ranks customers from a model you can attach money to. Here is the working subset a modeller needs, with a churn worked example showing why a well ranked model can still lose cash.
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Feature Engineering in Python: Where Model Accuracy Actually Comes From (Data Science Series, Part 6)
Encoding, ratio features and cross fitted target encoding on the churn table, with a measured leakage demo that turns a column of random noise into an AUC of 0.800.
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Getting Data Into Python: APIs, Files, SQL Pulls and Formats That Bite (Data Science Series, Part 5)
Most model errors enter at the moment data is read. Here is how I pull from paginated APIs, run SQL into pandas safely, and pick a file format, with measured numbers from the churn project.
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NumPy and pandas Past the Basics: Vectorisation, Merges, Reshaping and Memory (Data Science Series, Part 4)
Most pandas mistakes do not raise an error. They cost you memory, hours of runtime, and rows you did not know you gained. Here is what changed my day to day handling of the churn dataset.
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Python Setup for Data Science That Will Not Break: Virtual Environments, Notebooks and Reproducibility (Data Science Series, Part 3)
A working Python setup for data science that survives a laptop change, a colleague, and a rerun six months later. Virtual environments, tool choice, notebook discipline, pinned versions and seeds, with real errors and the fixes.
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The Data Science Lifecycle End to End, From Business Question to Retired Model (Data Science Series, Part 2)
Modelling was 11 percent of my last churn project. Here is the eight stage lifecycle that accounts for the other 89 percent, including the retirement stage almost nobody plans for.
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What a Data Scientist Actually Does, vs Analyst, ML Engineer and Researcher (Data Science Series, Part 1)
Four job titles get used interchangeably and they are not the same job. Here is what a data scientist actually does all day, how the role differs from analyst, ML engineer and research scientist, and what the labour numbers say about the path.
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FinOps Tooling, Maturity and the Roadmap That Follows (Cloud FinOps Series, Part 20)
Three organisations bought the same cost platform and got three different outcomes. Here is how to choose FinOps tooling, read the maturity model honestly, and build a twelve month roadmap that ends in decisions rather than dashboards.
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How to Build a FinOps Team and an Operating Model That Holds (Cloud FinOps Series, Part 19)
Tooling does not decide whether a FinOps practice survives. Reporting lines, decision rights and cadence do. A practitioner guide to FinOps team roles, centralised versus hub and spoke structures, sizing, and where to report.
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FinOps Automation With Guardrails, Policy and Infrastructure as Code (Cloud FinOps Series, Part 18)
Alerts tell you money already left. Policy stops it leaving. A practitioner guide to cost guardrails on AWS, Azure and GCP, plus cost checks in the pull request, and where automation goes wrong.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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