Hands-on Tutorials

From white noise to SARIMAX and beyond

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Predicting the future has forever been a universal challenge, from decisions like whether to plant crops now or next week, marry someone or remain single, sell a stock or hold, or go to college or play music full time. We will never be able to perfectly predict the future [1], but we can use tools from the statistical field of forecasting to better understand what lies ahead.

Forecasting involves time series data, or repeated measures over time. In data such as hourly temperature, daily electricity consumption, or annual global population estimates, we can look for patterns that collapse those hundreds…


Making Sense of Big Data

Scan a novel, calculate pi, and run regression on 50 million rows

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Cloud services firm Domo estimates that for every minute in 2020, WhatsApp users sent 41.7 million messages, Netflix streamed 404,000 hours of video, $240,000 changed hands on Venmo, and 69,000 people applied for jobs on LinkedIn. In that firehose of data are patterns those companies use to understand the present, predict the future, and ultimately stay alive in a hyper-competitive market.

But how is it possible to extract insights from datasets so large they freeze your laptop when you try to load them into pandas? When a dataset has more rows than dollars the median U.S. household will earn in…


The accuracy is in the aggregate

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Animal groups are greater than the sum of their parts. The individual termite wanders cluelessly while the colony builds a sturdy and well-ventilated mound. The lone stork loses its way while the flock successfully migrates. Across the spectrum of cognitive complexity, we regularly see the emergence of behaviors at the group level that the members alone aren’t capable of. How is this possible?

I spent my Ph.D. puzzling over how golden shiner fish − a generally hopeless and not very intelligent creature − form schools capable of elegantly evading predators. …


Tips and Tricks

The code around your code

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The programming concepts in the “Analytics Essentials for Data Science” post covered how to work with data once it’s sitting in front of you. These concepts are sufficient if your workflow looks something like downloading a CSV from Google Drive onto your laptop, analyzing the data, then attaching a PDF to a report.

Yet, what happens when you start a project that requires combining data from hundreds of CSVs? Clicking and dragging can only get you so far − even if you have the patience, your manager likely doesn’t! …


The foundations for making data make sense

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While Excel wizardry might cut it for many analytics tasks, data science work relies heavily on the nuance, reproducibility, and scalability of programming. From statistical tests only available in specialized R and Python libraries, to being able to show step-by-step how a model is formulated and generates predictions, to being able to go from processing one dataset to 1,000 with a few keystrokes, fluency in programming is essential for being an effective data scientist.

We’ll therefore focus on programming skills that are key to effectively manipulating and analyzing data. …


Getting Started

The foundations for analytical thinking

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Whether you’re spending all day in spreadsheets or TensorFlow, being an effective data scientist requires a solid understanding of statistics fundamentals. It’s hard not to write a textbook about all the stats that can be useful, but in this post I’ve narrowed down a set of foundational skills that will get you started for your role, no matter where you go. Let’s get started!

Hold on. Do I actually need to learn stats?

In the era of big data and machine learning, it’s tempting to shrug off learning any stats. When the average laptop is 2 million times more powerful than the computer that got us to the moon…


The pros, cons, and questions to ask before taking the plunge

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Data science in startups is notorious for being a memorable ride. From work that pivots on a dime from spreadsheets to customer interviews to CI/CD pipelines, to being handed more responsibility than you likely know what to do with, you’re guaranteed to learn nonstop in this role.

But what about being the first data scientist at a startup, the tip of the spear? Or what if you’re the second to join, the first extra hands for those more ambitious projects?

To answer these questions, I spoke with my great friend and colleague, Minkyung Kang (MK), who founded the data science…


Decorators to the rescue

Typing on old keyboard
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When it comes to writing complex pipelines running in production, it’s critical to have a clear understanding of what each function does and how its outputs affect downstream functions. But despite our best efforts to write modular, well-tested functions, bugs love hiding in the handoffs between functions — and they can be hard to catch even with end-to-end tests.

This article will cover a Python decorator for input validation that we can use to “lock” the inputs to our functions and immediately notice when there’s an unexpected mismatch.

Example Pipeline

Consider a simple pipeline where we query an API, clean the data…


How to find a job you won’t leave in six months

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The data science hype is real. Glassdoor labeled data scientist as the best job in America four years in a row, nudged out of the top spot only last year. Data science is transforming medicine, healthcare, finance, business, nonprofits, and government. MIT is spending a billion dollars on a college dedicated solely to AI.

Matt Sosna

MLE @ Facebook. Princeton PhD. Dad. Write about data science, machine learning, statistics, Python. Addicted to learning how things work. Views my own.

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