Start Here
New to the site? These four articles cover the foundational tools and concepts.
- Getting Started with Data Collection
Understand data sources, formats, and the basic workflow for collecting and organizing data before you write a single line of code.
- Excel to SQL: Low Hanging Fruit for Making the Switch
A practical roadmap for Excel power users ready to adopt SQL. These are the highest-value, lowest-effort topics to learn first.
- Organizing Data with SQL
Use SQL to filter, sort, join, and aggregate your data. A practical reference covering the queries you will actually use day to day.
- Python & Pandas for Data Wrangling
Load messy data into a DataFrame and use pandas to clean, reshape, and prepare it for analysis or storage.
Browse by Topic
How to gather raw data from APIs, web scraping, streaming sources, and open data repositories.
Preparation 7 articlesCleaning, reshaping, and validating data so it is ready for analysis or storage.
Pipelines 10 articlesBuilding, scheduling, and maintaining automated workflows that move data reliably from source to destination.
Analysis 15 articlesExtracting insight from clean data — from SQL aggregation and visualization to KPI design and ML experiments.
Culture & Communication 21 articlesThe human side of analytics: communicating findings, building trust in your numbers, and driving real decisions.
Career 3 articlesNavigating the professional side of data work — roles, growth paths, and how to position yourself for impact.
Latest Articles
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Physics Has Models. Machine Learning Has Black Boxes.
A physics model can be worked out from first principles — derived, interrogated, and defended line by line. A machine learning model can only be trusted. The difference is not academic. It is the difference between analytics you can stand behind and analytics that runs on "trust me."
🌳 High Hanging Fruit -
How to Start Maintaining an Ontology as a Non-Technical Business Owner
An ontology is just the written-down meaning of the words your business runs on — what a customer is, when a sale counts, what "active" means. You do not need to code to own it. You need to decide, write it down, and keep it current. Here is how to start.
🍎 Low Hanging Fruit -
How to Be a Data Champion
A data champion does not wait for perfect requirements, hoard certainty, or ship in the dark. They pull direction out of the business, buy room to experiment, make the case for telemetry, and sequence the whole thing crawl, walk, run.
🌳 High Hanging Fruit -
Building an Agent Harness for Data Engineering
A harness is what turns an LLM from a chat window into something that can safely operate on your pipelines — scoped tools, approval gates, sandboxing, and an audit log.
🌳 High Hanging Fruit -
Operational Telemetry, Explained for the Person Reading the Dashboard
Why that little dashboard you check for two seconds a day exists — and how it lets one person keep tabs on far more systems than they could ever check by hand.
🍎 Low Hanging Fruit -
Think With Data. Rest to Create.
Analytical rigor gets you to the right questions. Rest is what gets you to the right answers. The data professional who never steps back is not working harder — they are thinking shallower.
🍎 Low Hanging Fruit -
How to Build a Data Pipeline
Every data pipeline comes down to three decisions: where the work runs, what triggers it, and where the data lands. Get compute, a scheduler, and storage right, and you can reframe the whole thing as a textbook ETL.
🍎 Low Hanging Fruit -
Ontology vs Semantic Layer: What Each One Actually Is
An ontology defines what your business means. A semantic layer enforces those definitions inside your data stack. They are not synonyms, and confusing them produces dashboards that look authoritative but disagree with each other.
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Analytics Paints the Picture. It Does Not Prove the Story.
The job of analytics is to render reality clearly enough that the next move is obvious. Not to confirm the hunch in the room. Hyper-focusing from the start hides the elephant — and the elephant is usually the finding.
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Start With Data Stakeholders Already Trust
Building analytics on well-known public datasets earns stakeholder credibility before you write a single custom pipeline. Once the framework proves itself on familiar ground, automation and analytics compound the value without the credibility risk.
🍎 Low Hanging Fruit
Project Writeups
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Building a Stock Prediction Pipeline: What We Did and What We Learned
A project retrospective on combining Reddit sentiment, Google Trends, and NYSE price data to predict short-term stock moves with an SVM classifier.
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How BD Used AWS to Stop Guessing When Medical Devices Would Fail
A look at Becton Dickinson's MPAR system — a real-world example of turning device telemetry into proactive maintenance intelligence using AppFlow, Lambda, Athena, and QuickSight.
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What the Posts section is, how it differs from Articles, and what to expect here.