Tag: Data Science
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Python for Data Work, Past Automation Scripting (Infra to Data Science Series, Part 5)
Your automation scripts already move data. This part turns that habit into data code: load a month of cluster metrics with pandas, catch the dtype trap that silently breaks a metric column, and see why iterrows is slower than the plain loop you already write.
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Data Science Transition Roadmap and Timeline for Infra Engineers (Infra to Data Science Series, Part 4)
How long the move from infrastructure to data science really takes, broken into four phases you can hold against a calendar, with the sideways move that beats quitting for a bootcamp.
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Operations Mindset vs Data Science Mindset for Infra Engineers (Infra to Data Science Series, Part 3)
An operations mindset chases zero failure; a data science mindset budgets error and ships something useful before it is perfect. Here is how an infrastructure engineer switches between the two without losing the discipline that makes them worth hiring.
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Transferable Skills for Infra Engineers Moving to Data Science (Infra to Data Science Series, Part 2)
An operator moving into data science already holds more of the job than the posting admits. How to audit your skills against a real data science job description, which transfer, which reshape, and the two gaps to close first.
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What a Data Scientist Actually Does, Compared to Infra, SRE and VMware Work (Infra to Data Science Series, Part 1)
If you run infrastructure, keep an SRE rotation or administer VMware, you are not starting data science from zero. What a data scientist actually does, where your skills already transfer, and where to start.
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Data Science Architect Career Path, and What to Learn Next (Data Science Series, Part 30)
Stack Overflow folded data scientist into an AI/ML engineer group in 2025, and the reported pay gap was about 44,500 dollars. Here is the architect path out of senior data scientist: four ladders, the real skills gap, and a twelve month plan.
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Monitoring Machine Learning Models in Production: Drift, Decay and Silent Failure (Data Science Series, Part 25)
A model that breaks loudly gets fixed on Tuesday. A model that decays quietly gets fixed after a quarter of wasted budget. Here is how I monitor drift, estimate performance before labels arrive, and set thresholds nobody mutes.
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MLflow Experiment Tracking, Model Registry and Versioning (Data Science Series, Part 24)
A model you cannot reproduce is a rumour. Here is how I set up MLflow tracking, a model registry and aliases on the churn project, including the errors you will hit on a local file store.
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Recommender Systems in Python: Collaborative Filtering and Cold Start (Data Science Series, Part 20)
Collaborative filtering in Python with implicit, evaluated honestly by leave one out ranking. A popularity baseline beat matrix factorisation, and a new user returned five items scored exactly zero.
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Time Series Forecasting in Python, and Why Your Train Test Split Is Wrong (Data Science Series, Part 19)
A shuffled cross validation split on time ordered data will hand you a score you cannot ship. Here is what an honest forecast split looks like, how to backtest with a moving origin, and how far ahead a model is actually good for.
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Hyperparameter Tuning in Python: Halving Search, Optuna and Honest Model Selection (Data Science Series, Part 15)
A random search, a halving search and an Optuna study run against the same churn model, with wall times, the honest test score, and the nested cross validation number that showed how much of the gain was imaginary.
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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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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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