Corporate for "I forgot what this is about but I need to make noise before someone notices".
An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.
Technologically frozen in 1995. Still thinks "the cloud" is for rain and refuses to click anything newer than Solitaire.
Where your data goes to sleep.
The moment of truth when your model actually makes predictions—hopefully not embarrassingly bad ones.
Because mistakes were made.
A gradient boosting algorithm that wins Kaggle competitions—because sometimes brute force just works.
Because JSON wasn’t painful enough.
The fine art of deciding who gets in and who gets a "403 Forbidden."
Grouping users to prove that trends aren’t just luck.
Because “whatever naming convention feels right” is not a strategy.
Because just because you can collect data doesn’t mean you should.
The Costco of structured data.
Deploying apps without touching infrastructure (until something breaks).
Because sometimes, you actually want long-winded responses.
Stripping away identities because privacy lawsuits are expensive.
When your company trends on Twitter for all the wrong reasons.
The theoretical version of your data that reality refuses to match.
A structured way to describe data relationships (or overcomplicate things).
The awkward middle child of structured and unstructured data.
Predicting continuous values, like sales figures or how many coffees you'll need to survive Monday.
A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.
Grouping similar things together—useful for customer segmentation, but also how your closet naturally organizes itself into chaos.
“Let’s keep slicing the data until we find something that supports our assumption.”
Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.
Google’s way of making your SQL queries cost a small fortune.
“Yes, our data platform supports SQL. That’s not a selling point.”
The reason your reports make no sense.
Proof that a company probably takes security seriously.
When economics meets statistics and things get extra nerdy.
The law that keeps finance teams on their toes.
A 57-slide PowerPoint where 3 slides actually contain useful charts.
Just because two things happen together doesn’t mean one caused the other. Like, eating more cheese doesn’t actually make you better at math.
Creates JIRA tickets to track their JIRA tickets while drowning in chaos.
Rules about data that everyone agrees on but nobody follows.
Because raw data is just too ugly.
Because well-managed data is the difference between insights and chaos.
Because “I think this field means…” shouldn’t be part of data analysis.
Splitting your database into smaller disasters.
Moving data from one mess to another.
Hacking yourself before someone else does.
Checking your data before it embarrasses you.
When your data is so bloated no one knows what to do with it, but it sounds impressive.
Fixing data mistakes before they embarrass you.
The science of figuring out whether A actually causes B, or if it’s just a coincidence (like ice cream sales and shark attacks).
The algorithm that helps machine learning models learn—think of it as slowly rolling downhill to the right answer.
Following data laws just enough to avoid fines.
The constant struggle to keep data clean, secure, and useful.
Helping engineers understand how data flows, transforms, and actually works.
Convincing everyone that my version of the dashboard is the truth.
Preparing for disasters that will still somehow surprise you.
A central place for data that everyone fights over.
Where your data has commitment issues.
Stalking customers, but make it “data-driven.”
“Throw some data models at the wall and see what sticks.”
“Will this dashboard break when more than 5 people refresh it at once?”
“Your data reports need to be better, but we won’t give you more resources.”
Metadata management to keep track of your ever-growing data jungle.
Keeping track of all the ways hackers can ruin your day.
Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.
The art of making sure analysts don’t work with garbage.
When you want fast answers and minimal thinking.
Code for “this could’ve been a Slack message.”
A free tool for tracking website traffic—until privacy laws step in.
When search meets machine learning and everyone gets confused.
Slapping AI on the same old nonsense.
When bad data leads to even worse decisions.
The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.
“We need better numbers, but we don’t want to change anything.”
Tweaking and creating data inputs so your model performs better—basically, data science alchemy.
The lazy friend of machine learning—it just looks at its closest neighbors and copies them.
Spews directives like "make it intuitive" with all the specificity of a drunk fortune cookie.
Cutting back on data storage costs until everything runs painfully slow.
Because not every department deserves full database access.
“We need to filter this data in every way possible until it agrees with us.”
Because someone needs to process transactions in real-time.
Because reading rows one at a time is for chumps.
Load first, transform later—modern data integration in action.
Double-checking data before it makes a fool of you.
Worships clean metadata and version control. Lives for data lineage and will fight you over naming conventions.
The underappreciated hero who turns messy data into charts and makes everyone else look good.
The thing everyone builds but nobody documents.
Rules everyone agrees on but nobody follows.
The fantasy of having the same data everywhere at the same time.
When everyone agrees on what to pretend to care about.
“Can you analyze all our data from the last 10 years for a report we’ll ignore?”
Translating raw data into real-world meaning so it’s actually useful.
Sorting stuff into categories, like whether an email is spam, a cat is a dog, or your AI is actually working.
Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.
Feeding your data pipeline a never-ending buffet.
Proof that "we'll fix it later" never actually means later.
Like conducting a symphony, but with way more screaming.
Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.
It’s not just a conference—it’s a group hug wrapped in YAML. No fluff, no gatekeeping—just real talk from data practitioners sharing their learnings and strategies.
Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.
Redefines success metrics faster than politicians backpedal after an election.
Protecting user info while secretly monetizing it.
What you just got assigned because you asked a question in the meeting.
Your code, but only when someone remembers it exists.
A table that tells you how often your model gets things right (or, more realistically, how often it screws up).
The illusion of structure in your chaotic data world.
The science of making sense of data—assuming it’s not lying to you.
Goes to every conference and is part of every newsletter. Needs an intervention.
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