Guides
Practical guides for data and AI roles, tools, projects, and workflows.
25 pages
AI Tools Workflow Guide
How data professionals integrate AI tools into daily work, keep reviews in place, and add evaluation and privacy habits around repeated tasks.
Competitions Beyond Kaggle
How to use non-Kaggle competitions as portfolio evidence through reproducible code, evaluation notes, research challenges, and honest limits.
Data Analysis Guide
Practical data analysis guide covering SQL, metrics, dashboards, experiments, stakeholder communication, role boundaries, and portfolio evidence.
Data Engineering Certification
Decide whether a data engineering certificate is worth it, and turn certificate study into portfolio proof that employers can review.
Data Observability Guide
How data engineering teams use freshness, volume, schema, lineage, ownership, and runbooks to reduce data downtime.
Data Product Manager
A data product manager role reference for users, ownership, discovery, adoption, and adjacent product-role boundaries.
Data Roles Guide
Guide to common data roles, how responsibilities differ, how to choose a target role, and what portfolio evidence each role needs.
Data Science for Managers
How managers can hire, scope, support, and evaluate data science work.
Data Science Project Guide
How data science project management frames, scopes, measures, ships, and hands off analytics and ML work with stakeholders and adoption owners.
Data Science Recruiter
How data science recruiters screen candidates, define role fit, work with headhunters, and help both sides avoid mismatched roles.
Data Scientist Interview Prep
Prepare for data scientist interviews with role targeting, CV evidence, recruiter screens, technical rounds, case studies, and offer questions.
DataOps Tools Guide
A guide to DataOps tool categories for version control, CI/CD, orchestration, testing, observability, lineage, deployment, and recovery.
Freelance Data Consulting
An operating playbook for data freelancers: client buying fit, pricing risk, scope control, delivery, agencies, and reusable assets.
How to Hire Data Engineers
Guidance for managers and founders on when to hire data engineers, which profile to hire first, how to define the role, and what to test.
LLM System Design Interview
Prepare for LLM system design interviews with production patterns for RAG, agents, evaluation, safety, latency, cost, and operations.
LLM Tools for Real Products
Choose LLM tools for real products across model APIs, open-source models, RAG, evaluation, agents, observability, cost, and review.
Machine Learning for Business
How businesses choose ML use cases, compare baselines, test small-budget options, define business models, and plan adoption and ownership.
Machine Learning for Startups
A practical startup guide to ML-specific problem selection, MVPs, data/product fit, lean MLOps, hiring, monitoring, and knowing when not to use ML.
ML for Software Engineers
A roadmap for software engineers moving into ML: transferable skills, missing data habits, project sequence, production awareness, and interviews.
ML System Design Interview
Prepare for ML system design interviews with answer structure, prompts, metrics, data strategy, serving, monitoring, fallbacks, and portfolio practice.
MLOps Architecture
MLOps architecture as a component map for data, training, registries, CI/CD, serving, monitoring, and system interfaces.
Product Analyst Role
Guide to product analyst responsibilities, skills, event tracking, product analytics, and role boundaries.
Python Stock Analysis
How Python stock analysis connects market data, features, backtesting, validation, risk controls, and algorithmic trading deployment.
Solopreneur Data Scientist
A guide to solo data and AI work: offers, income streams, risks, and when solopreneurship differs from freelancing.
Volunteer Data Projects
How volunteer, nonprofit, and open-source data work becomes reviewed portfolio evidence for data engineering roles.