Tag: Data Science
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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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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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What a Data Analyst Really Does, vs Data Scientist and Data Engineer (Data Analyst Series, Part 1)
A plain, beginner-friendly breakdown of what a data analyst actually does day to day, how the job differs from data scientist and data engineer, and what the three roles pay.
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Major Advancements in VMware Cloud Foundation 9 for AI Workloads
The explosion of AI use cases from deep learning to computer vision has completely transformed how infrastructure is designed and managed. With the release of VMware Cloud Foundation (VCF) 9, VMware is stepping up to meet the demands of modern AI workloads with robust, enterprise-ready capabilities that simplify deployment, optimize GPU usage, and enhance integration…
Architect’s Toolkit
PJ’s Tools
- Infra 360 Hub – All Series
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VMware Cloud Foundation
- VCF Documentation
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Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
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- Hugging Face Models
Architecture & Design
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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