Webinar: Why logical layers matter, and how to use them -Watch now

Data Science at Companies

Why does Airbnb have a data scientist on every team? What did it take to build out Thumbtack's data infrastructure? How do Twitch data scientists convince execs to embrace data-informed decision making? Get behind-the-scenes perspectives on how data teams at actual companies tackle questions of building infrastructure, scaling analytics, attaining buy-in, and structuring teams.

Do Data-Driven Companies Actually Win?

Some gut-based judgements on the effectiveness of data.-benn.substack

Eight Archetypes of Data Citizens

The question for data teams is: what sort of attitudes do you want to cultivate?-Data People Etc.

Are We Human, or Are We Vendor?

We can talk about the tools, we can talk about the work, or we can take the third option.-benn.substack

Building More Effective Data Teams Using the JTBD Framework

The way your team drives impact to the business is through improved decision-making- both for your company and your customers. But it can be hard to take that mission and put it into practice.-Locally Optimistic

The Modern Data Stack Guide for 2022

We’re not here to throw more lingo in this ever-growing pot of modern data stack gumbo...but we do want to help you understand what the modern data stack is, how it works, and its benefits for companies of different sizes.-Mode Blog

Why Do People Want to Be Analytics Engineers?

The job nobody wanted is now the job we can’t get enough of.-benn.substack


It’s not the App Store. It’s the iPhone.-benn.substack

Awesome Data Leadership

A curated list of posts, videos, and articles on leading a data team should be useful for leadership at the middle-management, Director/VP, or C-suite level, for organizations both big and small.-Roni Kobrosly

Stakeholders: The Most Important Relationship for Analysts

Usually there are one or two really important stakeholders in an analyst’s work. You know the ones. Here are some do’s and don’ts for fostering a great relationship.-Inside Data by Mikkel Dengsøe

Microsoft, Google, and the Original Purple People

Google has the products, technology, and capital to dominate the data industry. Why aren’t they?-benn.substack

All Your Database Are Belong to Us

The machines are coming for our jobs, and…they might actually help? Plus, free advice for Steve Kerr and Ime Udoka.-benn.substack

Using Data to Navigate: Data Leaders On Leading Companies Through Economic Uncertainty

In times of uncertainty, we search for answers—and data holds a lot of answers.-Mode Blog

The Technical Pay Gap

The culture we build is the culture we buy.-benn.substack

Locally Optimal

How much value is really created by analyzing data?-Casual Engineering

The Best Decision Is One

As data folk, we’re not here to help organizations make better decisions; we’re here to create better results.-benn.substack

The Case for Consolidation

The data world should pay more attention to Microsoft.-benn.substack

Hiring Data Scientists With Intention

Focus on these three areas to more inclusively hire data scientists.-The Data Leader’s Survival Guide

Has SQL Gone Too Far?

The case for better business models.-benn.substack

Data Salaries at FAANG Companies in 2022

Salary is a sensitive topic. This post shines some light on data compensation using—you guessed it—data.-Mikkel Dengsøe

The End of Big Data

Databricks, Snowflake, and the end of an overhyped era.-benn.substack

Thinking About Evolving Your Data Stack

When you’re trying to decide which data tools to use, start not with the data sources, but with the business outcomes you want to achieve.-The Data Leader’s Survival Guide

Startups Shouldn’t Care About Revenue

And data teams should make sure they don’t.-benn.substack

Work Like an Analyst

The divisions of labor between analysts and everyone else are fading.-benn.substack

Data Team Branding

“You may not think about it consciously every day, but your hiring practices, promotion cycles, and ultimately your team’s success are all influenced by how you ideally want the rest of the company to perceive your team.”-The Data Leader’s Survival Guide

Rent the Runway’s First Fix-It Week

While their engineering counterparts set aside more time in every sprint to tackle tech debt, Rent the Runway’s data team opted to wrangle their issues in one fell swoop.-Dress Code

The Economics of Data Businesses

How data businesses start, and how they keep going, and growing, and growing.-Pivotal

Service Pressure

Data teams aren't service organizations, but we can learn a lot from those that are.-benn.substack

Planning Quantitative Work

When someone says “I need this analysis by next Friday,” what do you do?-Counting Stuff

Delegation is a Superpower

“Delegation is necessary soil for growth – for your own growth, for those around you, and for your organization. To grow into a new phase, you need to evolve your established responsibilities and make space for new ones.”-LeadDev


We talk a lot about what data can borrow from software engineering. But on the biggest issue of all, the technology industry has a lot to learn from the analytics community.-benn.substack

Estimating the Geographic Area of a Real Estate Agent

This in-depth post shows how Compass uses Kernal Density Estimation to calculate where real estate agents work, so that they can locate the right agent for their prospective home buyers.-AI@Compass

Data is for Dashboards

Defining metrics and creating dashboards isn’t banal busywork that gets in the way of “the more rewarding aspects” of analysts’ jobs. It’s one of the most important things that we do.-benn.substack

Why So Many Data Scientists Quit Good Jobs at Great Companies

A look at why the "sexiest job of the 21st century" has lost its appeal.-Start It Up

Does Data Make Us Cowards?

There’s a thin line between being analytical and being afraid.-benn.substack

The Missing Analytics Executive

We should redefine the role of the chief data officer. For our companies, for our careers, and for ourselves.-benn.substack

A Method for Measuring Analytical Work

Our only job should be to make people more decisive.-benn.substack

What Data Folk Were Saying About Zillow

In early November, Zillow announced it was winding down its iBuying business because of massive losses. This prompted many discussions on Twitter about the data science team’s role in iBuying’s failure and what machine learning is good and not good for in the industry.-Counting Stuff

Data and the Almighty Dollar

The data ecosystem is booming. The data economy has some things to figure out.-benn.substack

Universal Holdout Groups at Disney Streaming

“The idea of a universal holdout group is to hold back a randomly-sampled small percentage of users from all product changes for a period of time. This allows us to then compare metrics between the users who receive the production experience and those users who are held back from any product changes.”-Disney Streaming

Our Take On Prescriptive Analytics and How We Used it in 2020

Being able to quickly leverage data via prescriptive analytics gives businesses the advantage needed to tackle any unprecedented uncertainty, whether it’s a pandemic, a sudden decline in membership, or a surprise uptick in product demand.-Mode Blog

The Future of Operational Analytics

A better way to make daily decisions is already here—just not at work.-benn.substack

BI is Dead

How an integration between Looker and Tableau fundamentally alters the data landscape.-benn.substack

Chasing Ghosts

The data community talks a lot about tools, but what about the work of analysis itself? Because most corporate analyses are proprietary, it’s hard to learn from others’ work and improve.-benn.substack

The Intergalactic Data Stack

Falling further into the BI black hole—and charting a course out.-benn.substack

How I Got a Job at DeepMind as a Research Engineer (without a Machine Learning Degree!)

Lots of good insight in here for structuring your own ML learning curriculum, how the DeepMind hiring pipeline is structured, and how to get referrals.-Aleksa Gordić

Who Is “The Community?”

Even though the analytics community is generally thought of more welcoming and inclusive than other tech circles, Black folks remain woefully underrepresented. And we need to start talking about it.-benn.substack

Is BI Dead?

By defining BI as just self-serve, we shortcut what it could be, no matter how good that self-serve is. A better, more universal BI tool would combine both ad hoc and self-serve workflows, making it easy to hop between different modes of consumption.-benn.substack

Why Data Scientists Shouldn’t Need to Know Kubernetes

Where does the “full-stack” data scientist expectation come from? And how might data scientists own the stack, with the help of a good infrastructure abstraction tool?-Chip Huyen

Quasi-mystical Arts of Data & the Modern Data Experience

If we should think of data as a product, then we should think of data as a software product.-The Analytics Engineering Roundup

The Data OS

In 2021, Y Combinator funded 100 data companies. While some folks say there are “too many data tools,” instead of calling for a culling of said tools, we should try to figure out how to better manage the tools we have, and the shiny future ones we might want.-benn.substack

The Modern Data Experience

The modern data stack isn't enough. We have to create a modern data experience.-benn.substack

The Unspoken Gerrymandering of the Modern Data Stack

The modern data stack doesn’t need a BI bucket and a data science bucket; it needs a unified consumption layer. To do our job well, we have to overcome the technical division, not be defined by it.-benn.substack

The Third Rail

Analysts are like honeydew, victims of the soft bigotry of low expectations.-benn.substack

How To Build An In-House Data Team

Which comes first—the data engineer or the data analyst? And what’s the right team structure?-Forbes

Building a Data Team at a Mid-stage Startup: A Short Story

Gather round, folks, and hear a tale of fragmented data and unclear expectations, of organizational challenges and data-demotivated culture.-Erik Bernhardsson

Self-serve Is a Feeling

As data teams, we chase the self-serve experience that we think we’re supposed to build. We should be more critical of that, and chase the self-serve experience that makes us and our customers feel most at home.-benn.substack

Analytics Is at a Crossroads

The world is full of great analysts. Will we have the courage to go looking for them?-benn.substack

Data's Big Whiff

How to escape our dashboard rat race, learn from data, and love the job again.-benn.substack

What the Heck is a Data Mesh?!

“What we need... is for organizations to treat data with the same care that they treat any other public facing API.”-Chris Riccomini

When Are Templates Going to Happen?

The core assumption behind templates is that everyone's data is mostly the same, and everyone's business problems are mostly the same. That's only half true.-benn.substack

DataHub: Popular Metadata Architectures Explained

It doesn’t matter how cost-efficient or scaleable your data warehouse is if your stakeholders are wasting time trying to find the right dataset to perform analysis.-LinkedIn Engineering

The Self-serve Shibboleth

“The problem self-serve is meant to solve—that there are more questions than analysts, and we don’t have time to answer them all—is still a problem, even if self-serve is a lousy solution.”-benn.substack

Analytics Engineering Everywhere

While we were being dazzled by data scientists, another role started to emerge, one that has a lot of impact for smaller organizations where data science often falls flat.-Jason Ganz

How Product Leaders Can Make Better Decisions With Iterative Analytics

This post walks through the process of answering a question every product leader has had: “Should the product team spend time fixing bugs or building new features?”-Mind the Product

Analytics Is a Mess

And a mess isn’t a bad thing. Data isn’t objective, and analysis isn’t structured. It’s creative and disorganized. So don’t stifle it; just have a plan for tidying up later.-benn.substack

Run Your Data Team Like A Product Team

“When a data team emerges in a company as an afterthought, they often end up being built like service-based departments with a “submit a ticket with a question, get a very specific answer” mindset. Data folks who are bound to this model rarely spend time being proactive. Without intentional space, they are unlikely to be anything more than ticket closers.”-Locally Optimistic

The Most Important Role You’re Not Hiring for Your Data Team: The Information Architect

When you think of information architecture, your first thought might be “websites.” But it’s just as important for designing dashboards and data viz.-Nightingale

Can College Predictive Models Survive the Pandemic?

“The last year has revealed just how important it is that we fully understand the ‘how’ and the ‘why’ of the predictions these tools make about ‘who’ is most likely to enroll or may need additional services to help them succeed at an institution.-EdSurge

Why Is Self-serve Still a Problem?

“Analysts and non-analysts use data in structurally different ways. By conceptualizing self-serve BI as a simplified means for doing an analyst’s job, we’re not only making the self-serve problem too hard—we’re solving the wrong thing altogether.”-benn.substack

Building Powerful Data Teams: On Investing in Junior Talent

“As a Data Director and manager of a 5-person data team, I'm obsessed with how we can more efficiently, and with a lot less shame, go through that cycle of learning from mistakes.-Brittany Bennett

Data Science at an Analytics SaaS Startup

Working at a SaaS company was a new challenge for Mode’s Director of Data Science. Here are five key lessons from his past roles in P2P commerce, traditional commerce, gaming, and ad tech that he puts applies to SaaS.-Towards Data Science

Four Steps To Make Your Organization More Data-Proficient In The Coming Decade

Everyone—data scientists, marketing managers, CEOs, all—will need to deepen their data skills.-Forbes

Growing and Running Your Data Science Team

“An effective data science team is like the goose that lays the golden eggs. Sometimes, we focus too much on the eggs (i.e., results)—how can we get more, faster? As a result, the goose (i.e., team) can get neglected.”-Eugene Yan

One Woman’s Work Using Data Science to Break Down Barriers – and Breaking Down Barriers to Data Science

This profile of Kim Eng Ky highlights examples of how data science can be used for improved social outcomes in both the private and public sector.-The Good AI

Data Meta-Metrics

How do we communicate confidences and doubts about data to a non-technical audience?-Haystacks

(At Least) 5 Ways Data Analysis Improves Product Development

Traditional BI tools are great at answering static questions, but not the big-picture questions that direct decisions on what big swings to take in your product roadmap.-Mode Blog

Designing Data Science Tools at Spotify

A product designer explains her process for creating usable, clean tools to help data scientists make sense of Spotify’s millions of users.-Spotify Design

Building Diverse Data Teams

These workshop slides present real strategies for creating inclusion and equity in your hiring process. One way to start? Throw out your referral program and only incentivize referrals for under-represented minorities.-Ayodele Odubela

State of Data Science and Machine Learning 2020

Cloud computing is on the rise. Scikit-learn is the most popular machine learning tool. And data scientists are still overwhelming young and male.-Kaggle

Election Night with Biden’s Data Guru

This is a fascinating look into the world of political data and analytics. Chief Analytics Officer Becca Siegel was a critical part of Biden’s campaign, raising early alarm bells about unrealistically friendly polling data and pushing for more appearances in Georgia during the final stretch.-Intelligencer

Lessons Data Teams Can Learn From the 2020 Election Polls

The question of whether we should produce political polls is akin to asking whether we should use be using imperfect data to inform business decisions.-Mode Blog

When Good Data Analyses Fail to Deliver the Results You Expect

A cautionary tale of a data analysis, a dashboard, and a huge waste of resources.-Narrator

What’s It Like to Be an Applied Scientist at Amazon?

What’s an average day (or non-average day) look like building machine learning systems to help customers?-Career Fair

An Intake Form for Data Requests

When a newly formed data team started getting data requests from all over the company via Slack, email, meetings, and more, they decided to centralize calls for help with a form. The info they receive sets them up for great kickoff conversations with stakeholders.-Haystacks

What if You Were an Evil Data Scientist?

Generally, data folks are well meaning, but what if they weren’t? This thought experiment is good reminder of how much power data scientists and analysts wield and the responsibility that comes with it.-Counting Stuff

The Hardware Lottery

Does this sound familiar? A research idea wins because it works well with the available software and hardware and not because the idea is better than other research directions. Then you, my friend, have been playing the hardware lottery.-Sara Hooker

Don’t Tell Your Data Team’s ROI Story

“The truth is that if you’re trying to quantify your impact by yourself, you have already lost. Instead, the best way to tell the ROI story is for other people to tell it.”-Hex

Unpopular Opinion - Data Scientists Should Be More End-to-End

While this is frowned upon (too generalist!), I've seen it lead to more context, faster iteration, greater innovation—more value, faster.-Eugene Yan

Are Dashboards Dead?

“If you’ve been using the modern analytics stack for multiple years now, you’re likely starting to feel some pain. It’s probably starting to feel a little bit like the ‘wild west’ to you.”-dbt Blog

Monitoring Data Quality at Scale with Statistical Modeling

Based on historical data patterns, Uber’s Data Quality Monitor automatically locates the most destructive anomalies and alerts data table owners to check the source, but without flagging so many errors that owners become overwhelmed.-Uber Engineering

How Do Data Scientists and Machine Learning Engineers Work With One Another?

This thread offers a look into the wide range of ways companies structure their machine learning and data science organizations.-r/datascience

What I Love about Scrum for Data Science

“Despite my initial concerns (and violent objections), Scrum grew on me. Now, I find it almost indispensable.”-Eugene Yan

Does Stress Impact Technical Interview Performance?

These findings show that traditional technical interviews assess performance anxiety, not software skills. That means companies are missing out on hiring talented employees just because those programmers aren’t good at writing on a whiteboard and explaining their work out loud while coding.-NC State University

Data Analyst 3.0: The Next Evolution of Data Workflows

With the rise of cloud-native data warehouses and advancements in scalable inference methods, we’re now at the cusp of a third phase that not only affords better, faster processing of data, but also lets operational data analysts impact business decisions like never before.-Sisu

How Five Data Leaders Are Leading Teams Through the Pandemic

Starting with the transition of working from home, leaders from Lyft, Intercom, Greenhouse, Sisu, and Patreon walked us through stories of pivots, rapid user growth, new metric adoption, and managing competing priorities.-Mode Blog

COVID-19 Cases Are Rising, So Why Are Deaths Flatlining?

Some possible explanations for the case-death gap? Deaths lag behind cases. The typical COVID-19 patient is getting younger. Expanded testing means finding more cases, milder cases, and earlier cases.-The Atlantic

N=1: My Experience with Motherhood in Tech

“I would have loved to hear about this journey from a person similar to me—a woman in tech who cares deeply about her career and community as well as her family. Influencers can be great, but sometimes you just want to know how a regular person told their boss they were pregnant, and maybe what stroller they picked and why.”-Haystacks

10 Tips for Research and a PhD

“This advice is not just limited to PhD students. If you are an independent researcher, want to start a PhD in the future or simply want to learn, then you will find most of this advice applicable.”-Sebastian Ruder

From F2F to Remote

A stats professor shares how she adapted her undergraduate regression course when the class switched to remote learning mid-semester.-Maria Tackett

How I Wound Up Being a Quantitative UX Researcher

Quantitative UX researchers and data analysts share a similar base skill set. The main difference is how closely UX researchers work with the product team.-Counting Stuff

Reassessing Customer Health Scores in a Fluctuating Market

Within only a week or two of closing our office, we had a fully explorable, dynamic health scoring model leveraged across key stakeholders of Mode.-Mode Blog

Proxy Metrics

The former VP of Product Management at Netflix walks through how to define a product metric to prove or disprove your hypotheses and measure progress.-Gibson Biddle

Building Data Science Infrastructure

Super-practical, step-by-step advice on setting up the very first data science infrastructure and team in an organization.-Data Science Salon

An Analyst’s Perspective on COVID-19’s Economic Impact on Businesses

In a previous life, Mode's co-founder and president worked as an economic analyst. Here's his assessment of how businesses can brace for strong economic headwinds ahead.-Mode Blog

Cultivating Algorithms: How We Grow Data Science at Stitch Fix

In this interaction visualization, Stitch Fix presents the organizational structure, roles, and processes that define their way of working, and how it is different from the way data science operates at other companies.-MultiThreaded

How Mode Went Completely Remote in 36 Hours, and 7 Tips We Learned Along the Way

The combination of unified activity monitoring, digital accountability, and virtual togetherness processes allowed this largely non-remote group to transition eerily seamlessly into a remote workforce.-Mode Blog

How We Improved Data Discovery for Data Scientists at Spotify

What Spotify’s insights team learned as they iterated on an internal tool for sharing data and knowledge with employees.-Spotify Labs

The New Business of AI (and How It’s Different From Traditional Software)

“Many AI companies (and investors) are betting that this relationship will extend beyond just technology–that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.”-Andreessen Horowitz

Lessons Learned Managing the Gitlab Data Team

Don't reinvent the wheel, plan for growth, and pick excellent tools.-Gitlab Blog

10x Data Scientist is Luckily Not "a Thing" Let's All Work to Keep It This Way

The 10x engineer is a toxic stereotype in the first place. It’s an especially unproductive way to think about data science.-Counting Stuff (https://counting.substack.com/)

Bayesian Product Ranking at Wayfair

With 25,702 shower curtains in their store, how does Wayfair pick the right three to recommend to a customer?-Wayfair Technology Blog

Doing Freelance Data Science Consulting in 2019

First tip: call yourself a consultant instead of a freelancer or contractor—it sounds fancier.-Ethan Rosenthal

A Dispassionate Examination of the Empirical Evidence Regarding Positional Punctuation in SQL

Let’s put the leading vs trailing comma debate to bed, once and for all. (But c’mon, trailing commas are obviously way better, right?)-Mode Blog

How to Solve a Business Problem Using Data

Reflect back on your grade school days... It turns out solving a business problem with data requires the same foundational skills as solving a word problem in grade school math class.-Little Miss Data

Data Project Checklist

Use this questionnaire to understand an organization’s context for developing data projects. It’s based on decades of projects across many industries, including agriculture, mining, banking, brewing, telecoms, and retail.-fast.ai

How to Demystify Skewed Data and Deliver Analysis

Try out these four data distribution charts to spot meaningful patterns, trends, or significant errors in your data.-Mode Blog

Want To Make Good Business Decisions? Learn Causality

This causality walkthrough uses an example that plagues many modern marketing teams: “Should we pay for branded search ads?”-MultiThreaded

dbplyr: A Path to More Inclusive Data Transformations at the ACLU

By using the Tidyverse’s dplyr to generate complex SQL code in their data warehouse, the ACLU analytics team is able to create reusable data workflows that everyone can benefit from—whether they’re using SQL, Python, R, or a BI tool.-ACLU Tech & Analytics

Tips for Data Science Interviews

A lot of the advice boils down to: put yourself in the shoes of the interviewer.-Becoming a Data Scientist

When Did Analytics Engineering Become a Thing? And Why?

Read about the market trends that gave rise to the newest role on modern data teams.-dbt Blog

How to Find Consulting Clients

If you’re thinking of striking out on your own as a data science consultant, your biggest fear is probably “How will I find work?”-Chris Achard

Choose Boring Technology

Adding technology to your company comes with a cost. How would you solve your immediate problem without adding anything new?-Dan McKinley

Coding Salaries in 2019: Updating the Stack Overflow Salary Calculator

It turns out DevOps engineers generally love their jobs and they make bank.-Stack Overflow

Modeling Conversion Rates and Saving Millions of Dollars Using Kaplan-Meier and Gamma Distributions

“Computing a conversion rate is often fairly straightforward and involves nothing more than dividing two numbers... [But] when there is a substantial delay until the conversion event, this analysis suddenly gets vastly more complex.”-better.engineering

Logs Were Our Lifeblood. Now They're Our Liability.

“If the first ten years of data science were all about collecting and analyzing everything, the second ten will be about how to be deliberate and selective about collected and analyzed data.”-Normcore Tech

How the Data Insights Team Helps Flatiron Build Useful Data Products

A look at how the first data hire at a healthcare startup scaled up to a group of Data Insights Engineers who are embedded with almost every product team.-Carlos Aguilar

Need Some Fashion Advice? Just Ask the Algorithm

Stitch Fix is using data to demystify one of the most elusive parts of fashion: how individual pieces of clothing relate to one another.-Wired

How Lyft Creates Hyper-Accurate Maps from Open-Source Maps and Real-Time Data

Lyft has fixed thousands of map errors in OpenStreetMap (OSM) in bustling urban areas.-Lyft Engineering

Selling Data Science

“Half of your job, regardless of what that job is, is being able to sell your work.-Normcore Tech

Devoted Health and Data Science with Chris Albon

Centralized data science team or matrixed? How do you choose whom to hire? How do you go from a Political Science Ph.D. to non-profit data science wizard?-Google Cloud Platform Podcast

The Care and Feeding of Data Scientists

Are you a data scientist manager who had the role thrust upon you? Feel like you don’t have the necessary toolset or role models or mentorship to do the job well? This e-book aims to fill that gap.-O’Reilly

The UX of Data

Generating data is easy. Making sure it’s reliable and widely accessible is hard. With a bit of human-centric thinking, teams can build shared understanding of why, what, and how to measure their work.-Lex Roman

Presto Infrastructure at Lyft

Learn about the query engine that supports thousands of dashboards, 1.5K weekly active users, and a couple million queries every month.-Lyft Engineering

Building Our Centralized Experimental Platform

Running an A/B test is easy. Screwing up an A/B test is even easier. Stitch Fix built their own tool to make tests consistent across teams and give visibility into every test being run in the company.-MultiThreaded

Models for Integrating Data Science Teams Within Organizations

Each model has its benefits and drawbacks. Take a gander and see what might best fit your needs.-Medium

What is Your “Data Science Team” Called in Your Organization?

A fun Twitter thread to peruse! There’s more variety in team names than you might expect.-Chris Riccomini

Models and Microservices Should Be Running on the Same Continuous Delivery Stack

The deployment needs for models and services are strikingly similar, so why are so few companies actually using a unified model and service deployment stack?-Chris Riccomini

Building Lyft’s Marketing Automation Platform

Lyft built an ML system that automatically finds the best potential users to target, allocates the right budget for each ad, and sets the right amount to bid on each platform to maximize that budget.-Lyft Engineering

You Probably Don't Need a Data Dictionary

Data dictionaries and data catalogs can end up being a large maintenance burden for little value and go out of date very quickly. Try these other approaches instead.-Locally Optimistic

Is it a good idea for a data scientist to take a position as a data engineer for a few years to build their software engineering skills?

Check out the replies in this thread if you're a data scientist curious about picking up data engineering skills.-Caitlin Hudon

The Agony and Ecstasy of Building with Data

This post on the pitfalls of A/B tests and data was written 6 years ago, but still holds up perfectly.-The Year of the Looking Glass

The Data Science Mindset: Six Principles to Build Healthy Data-Driven Organizations

Do you want to make your organization more data-driven, but you're not sure where to start? Whether you're a business or technical leader, this detailed framework provides a lifecycle to structure the development of your data science projects.-InfoQ

Real Scientists Make Their Own Data

“Your best chance to make a serious contribution as a business or academic researcher is to find, make, and combine novel data.”-Sean J. Taylor

4 Lessons to Keep Your Data Model Under Control

As data sources are added and complexity grows, the data model makes a big impact on the productivity and efficiency of anyone in the company that works with data.-Mode

A Culture of Partnership

“An analytics team that operates in a silo, despite being filled with super talented analysts, engineers, and data scientists will often end up spinning its wheels and failing to impact the bottom-line of the organization.”-Locally Optimistic

Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function

This division of labor by function is so ingrained, we're quick to organize our data science teams accordingly: one person to source the data, one to model it, one to implement, etc. But when your product is still evolving and the goal is to learn, a generalist approach is the way to go.-MultiThreaded

The AI Diet

“Only recently, with the ability to analyze large data sets using artificial intelligence, have we learned how simplistic and naïve the assumption of a universal diet is. It is both biologically and physiologically implausible... A good diet, it turns out, has to be individualized.”-The New York Times

Empathy and the Art of Analytical Persuasion

Even the most sophisticated analysis is no match for an apathetic audience. Over years of practice, we've developed a framework for impactful, empathetic presentation of data.-Mode

Git Your SQL Together (with a Query Library)

You will always need that query again. That's why you should set up a git repository for saving and sharing queries, and track any changes made over time.-Haystacks

How We Created a Visual Search Engine for Hayneedle

This post is an incredibly detailed and excellent reference for anyone considering building visual search for their product.-The Jet Tech Blog

The Problem With Hands-Off Analytics

As data scientists, we should be clear-eyed about our primary responsibility: to turn data into insight, and insight into action. Attempting to hand off these problems doesn't change that. Providing a tool to someone to get the data doesn't offload that responsibility.-Mode

The Analytics Engineer

In the shifting landscape of analytics, another role rises: someone whose job is to build tools and infrastructure to support the efforts of the analytics and data team as a whole.-Locally Optimistic

You're Only 30 Minutes From Answers

How do you ship value early when you have to build infrastructure first? Today, it takes hours—not months—to get an entire data infrastructure up and running thanks to off-the-shelf tools.-Mode

The ‘Moneyball’ Solution for Higher Education

4 out of 10 students who start college and don’t finish in at least six years. Georgia State's predictive analytics system monitors 800 different academic risk factors and identifies students who need support to graduate on time.-Politico

Overplanned Analytics Initiatives Are Doomed to Fail

The road to changing the fundamental decision-making culture of an organization always has twists and turns. So what separates the ones who navigate them successfully from those that don't?-Mode

Creating a Data Road Map

Since data often works cross functionally with other teams, it’s critical to consider other team’s priorities and objectives in developing your road map. Here’s a blueprint to get started.-Locally Optimistic

What Great Data Analysts Do — and Why Every Organization Needs Them

“Far from being a lesser version of the other data science breeds, good analysts are a prerequisite for effectiveness in your data endeavors.”-Harvard Business Review

Mo models, mo problems: tracking the quality of models in production

Caitlin Hudon asked “Data scientists: What’s your team’s approach to tracking the quality of models in production?” and got a load of great advice. Check out the original thread or the summary in this doc.-Caitlin Hudon

The Netflix Data War

This take on the “war” between the Netflix’s data team and content team examines what happens when the data says one thing, but other business factors need to be taken into consideration.-Simply Statistics

Bye bye Mongo, Hello Postgres

Why and how The Guardian switched off the Mongo DB cluster used to store their content after completing a migration to PostgreSQL on Amazon RDS.-The Guardian

Data Science vs Engineering: Tension Points

Want to get a good understanding of how data science turns into data products? This article is for you.-Domino

Predicting the Real-time Availability of 200 Million Grocery Items

Ever wished there was a way to know if your favorite Ben and Jerry’s ice cream flavor is currently available in a grocery store near you? Instacart’s machine learning team has built tools to figure that out.-Engineering at Instacart

Convoys: a Library for Modeling Time-lagged Conversion Rates

When it takes a while for someone to convert, predicting conversions gets tricky. You know who converted, but if someone did not convert, they might still convert in the future. Survival analysis to the rescue!-Better

Building Data Dictionaries

Cataloging all the data your company collects is a massive undertaking. Get started with these examples and best practices. (And remember, one step at a time!)-Haystacks

Don't Choose Dashboards Over Analysis

An analyst's real contribution doesn't come from delivering dashboards—it comes from solving problems. And often, the choice ends up being mutually exclusive. Learn how the best companies we know stay focused on analysis over dashboards.-Mode

Your Client Engagement Program Isn't Doing What You Think It Is.

Stitchfix deploys a “contextual bandit” framework, which uses algorithms to learn the most effective strategy for engaging each individual client.-MultiThreaded

Bridging the Gap Between Business Analytics & DevOps

How the analytics team at BetterCloud has worked in harmony with their DevOps team to ensure they're accessing the data they need without sacrificing security.-BetterCloud

An Introduction to the Data Product Management Landscape

Because of data’s new-found role as a key product and competitive advantage, data product management has emerged as a new career path. These roles run the gamut from infrastructure to analytics to applied AI and machine learning.-Insight Data Science

What is Production?

“While there are good reasons to be careful whenever you make changes that could impact customers, I believe that as software becomes more data-driven it is critical to find safe ways to empower Analytics teams to build and deploy data-driven applications.”-Locally Optimistic

From Data to Action With Airbnb Plus

A peek into the lives of Airbnb Data Science interns. Particularly interesting is the explanation of Airbnb's three data science tracks: Analytics, Algorithms, and Inference.-Airbnb Engineering & Data Science

The Role of an Analyst in the Age of the Citizen Data Scientist

What do analysts do when the people to whom they have been providing data begin to get that data for themselves?-Mode

Experimentation & Measurement for Search Engine Optimization

When the Airbnb product team wanted to test new landing pages to boost SEO, they realized that a traditional A/B test wouldn't cut it. Here's the “market-level” framework they implemented to measure how the new pages affected traffic.-Airbnb Engineer & Data Science

deon: An ethics checklist for data scientists

This command line tool takes the concept of data science ethics from theoretical to practical by allowing you to easily add an ethics checklist to your projects.-DrivenData

3 Analytics Leaders on Building Efficient Teams

How do you tell if your analytics team is efficient? Making an impact? If you're steering the analytical ship at your organization, you'll want to give this a read.-Mode

The Big Four Reasons Companies Struggle to Hire Data Talent

We hear from both sides of the data talent market from the thousands of data scientists, analysts and others who use Mode every day. Here are four common problems we’ve noticed companies face when hiring for data talent, and how you might fix them.-Mode

Doing good data science

“Moving fast and breaking things is unacceptable if we don’t think about the things we are likely to break. And we need the space to do that thinking: space in project schedules, and space to tell management that a product needs to be rethought.”-O’Reilly

Apple is rebuilding Maps from the ground up

Google Maps’s moat may dry up soon. Apple Maps is moving from relying on third-party data to owning all of the data that goes into making a map, without compromising their firm stance on protecting user privacy.-TechCrunch

3 Weird Analytical Practices at Mode

We offer a peek into Mode’s internal analytics culture, including the fact that the most technically proficient folks actually sit on our Customers Success team.-Mode

Reporting is a Gateway Drug

Some advice on how to use reporting as a means to create strong stakeholder relationships in your organization.-Locally Optimistic

Data Violence and How Bad Engineering Choices Can Damage Society

“If you have the temerity to insert your work into a political issue that… doesn’t immediately affect your life, you should also be prepared to accept the consequences—or, at the very least, answer a few hard questions.”-Medium

How to Attract Top Data Science Talent: Lessons from 1,400+ Insight Fellows

The head of Insight Data Science shares three key areas for differentiation to focus on throughout the hiring process.-Mode

How Grubhub Analyzed 4,000 Dishes to Predict Your next Order

Grubhub had 14 million menu items and the only thing they had in common was that sometimes people ate them. Here’s how their data team built their own taxonomy of food.-Wired

Use These Data Analytics Tips to Find Your Film’s Audience

The film industry is years behind others—like interactive and music—with regards to access to data. This post breaks down how filmmakers can collect data at all times throughout their art-making to build audiences and maximize revenue.-Sundance Institute

The (Data Science) Notebook: A Love Story

Speaking of which… what’s driving the rapid adoption of the notebook interface as the preferred environment for data science work?-Mode

Data-Driven or Insights-Driven? Data Analytics vs Data Science

This is a great example of how to handle objections that come up in conversations with folks who aren’t entrenched in data all day.-Jen Stirrup

8 Great In-App Analytics Pages in B2B Software

You probably have data that can help your users do their jobs better and frame your business in a positive light. Here are some great examples of companies providing valuable usage data to their customers through in-app analytics.-Mode

How to Create a Great In-App Analytics Page

If you’re kicking off a project to build your own in-app analytics, keep these six considerations in mind.-Mode

Scaling Event Tables with Redshift Spectrum

As Mode’s customer base grew, we reached a point where our infrastructure wasn’t capable of handling the exponentially increasing volume of event data. Here’s how we saved Redshift performance by offloading 75% of our event data to S3 in less than a week.-Mode

So here’s my postmortem after hunting for a data science job

“I’m tired of all the Medium thought pieces on how to become a data scientist because they don’t reflect the reality of getting a relevant job from the applicant’s side. And it’s hard, especially without a Masters/PhD.”-Max Woolf

How Mode’s User Stats Page Answers Customer Questions While Increasing Feature Use

Looking for a way to highlight underused features? Are customers hounding you about the value they’re getting out of your product? A usage page might just kill those two birds with one stone.-Mode

How Quora’s Head of Data Science Conducts Candidate Interviews

Anyone who has considered leaving the ivory tower should give this a read.-Mode

Transitioning From Academia to Industry: Perspectives from Indeed’s Data Scientists

Eric Mayefsky has assessed hundreds of job candidates in his half decade in management at various tech companies. Here are the five key lessons that have helped him build an amazing data team.-Indeed Data Science

Recommended companies for early-career data scientists

This is a goldmine for junior data scientists looking for companies with defined career paths and mentorship opportunities.-Hilary Mason

One Year as a Data Scientist at Stack Overflow

Lots of great reflections in here about being a data scientist at a small company, the R workflow, and working remotely. We especially love this: “Data science is highly technical work, but the value of my technical work would be much lower if I could not communicate what it means in clear and compelling ways.”-Julia Silge

Directions of Ascent

What can individuals who work with data and code do to “fix the mess that tech has enabled”?-arg min blog

Google Maps's Moat

Twitter user @msquinn put it best: “This is a great look at how the Google Maps data strategy first conceived over a decade ago continues to unfold in the product you see today.”-Justin O’Beirne

Bridging the Trust Gap: Data Misuse and Stewardship by the Numbers

Consumers don’t understand how companies actually use their data. Meanwhile, companies think more consumers understand data stewardship practices than actually do.-The Boston Consulting Group

Engaging the Ethics of Data Science in Practice

“The critical writing on data science has taken the paradoxical position of insisting that normative issues pervade all work with data while leaving unaddressed the issue of data scientists’ ethical agency. Critics need to consider how data scientists learn to think about and handle these trade-offs, while practicing data scientists need to be more forthcoming about all of the small choices that shape their decisions and systems.”-Association for Computing Machinery

Data Meta-Metrics

How do you communicate confidences and doubts about data to a non-technical audience? Check out one analyst’s method for adding a “state of the data” aspect into her presentations to get the whole team involved in the data improvement process.-Caitlin Hudon

Corporate Data Science

A great talk for those tasked with growing a data science team, covering how to optimize a team's makeup across many dimensions and instill in them the importance of caring deeply about data collection, security, ethics, and interpretability.-Angela Bassa

Communication in data science

When people stress the importance of good communication in data science, they're usually talking about communicating results—the last step in a data scientist's workflow. But communication is more than just a final bottleneck. It’s important at every stage.-University of British Columbia

Avoiding Being a 'Trophy' Data Scientist

A collection of the challenges data scientists face in their quest to add value to a company.-Peadar Coyle

How to Job Interview a Data Scientist

Mode CEO Derek Steer explains where most data scientist job interviews fall short and three key criteria for evaluating candidates.-Mode

The Four Cringe-Worthy Mistakes Too Many Startups Make with Data

HotelTonight's Chief Data and Strategy Officer shares why you should run your data team like product and think twice before hiring a data scientist.-First Round Review

Big Data Processing at Spotify: The Road to Scio (Part 1)

Using Scio, a built in-house Scala API, Spotify is able to run the majority of their workloads with a single system, with little operational overhead.-Spotify Labs

From Power Calculations to P-Values: A/B Testing at Stack Overflow

If you're a little fuzzy on the relationships between sample size, effect size, false positive, and false negative rates, this post and the accompanying interactive calculator will clear things up.-StackOverflow

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

“In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment’s outcome, which if deployed could hurt the business by millions of dollars.”-Microsoft

ZATA: How we used Kubernetes and Google Cloud to expose our Big Data platform as a set of RESTful web services

An inside look at zulily's data platform, which makes data accessible to analysts, systems, and applications without sacrificing speed or storage options.-Tech @ zulily

Landing a Data Science Gig in New York City

Trying to break into the NYC data science job market? Sans a PhD? This guide was tailor-made for you.-Ground Truth

How Stitch Consolidates A Billion Records Per Day

Ever wanted to know how the people who make ETL tools set up their data infrastructure? Wonder no more.-StackShare

Data Science for Fraud Detection

A primer on the inner workings of fraud analysis, from dimensionality reduction to anomaly detection.-codecentric

5 Ways to Make Sure Your Analytics Spark Growth

From setting the right KPIs to sending events to the right place, here are the steps every company should take to make their product and marketing analytics worthwhile.-Astronomer.io

Data Security for Data Scientists

The Equifax breach is yet another reminder that data security is no longer a niche speciality of database admins and network engineers. Here are 10 suggestions for ensuring the data you work with is properly protected.-Andrew Therriault

Train, Score, Repeat, Watch Out! Zillow's Andrew Martin on modeling pitfalls in a dynamic world.

One of Zillow's data scientists addresses the challenges that don’t crop up in standard textbook problems or most ML competitions: feedback loops, dynamic datasets, and temporal consistency. A great read for Kagglers and non-Kagglers alike.-No Free Hunch

Switching to a Probabilistic Model for Venue Search in Foursquare

How Foursquare’s engineering team improved the accuracy and user experience of their location intelligence by switching from a search ranking algorithm to regression trees and probabilities.-Foursquare Engineering

The Data Trust Gap and How to Close It

78% of people have trouble trusting companies with their data; that means if your customers do trust you with their data, you have a competitive advantage. How do you get there? By making sure data transparency isn’t an afterthought.-InVision

Data Science without Borders

In his JupyterCon keynote, Wes McKinney makes the case for a shared infrastructure for data science, discusses the open source community’s efforts on Apache Arrow, and offers a vision for seamless computation and data sharing across languages.-O'Reilly

Choosing an ETL tool for your analytics stack

In the market for an ETL solution? Here's the criteria we employed when we evaluated ETL vendors for our own use here at Mode.-Mode

Scaling Analytical Insights with Python (Part 1)

FloSports' VP of Product shares the subscriber retention analysis that allows their business to move quickly as the number of data sources and volume of data increases.-Kevin Boller

Data Science: Challenges and Directions

Carve out some time in your schedule for this academic paper on “the gaps between the world of hidden data and existing data science immaturity.” It's a dense but rewarding read.-Communications of the ACM

Timing is everything: what our data says about the best time to send a message

Intercom analyzed millions of in-app and email messages from B2B companies to figure out when to send messages so they'll reach the maximum amount of people possible. This is a cool example of turning product analytics into actionable insights for customers.-Inside Intercom

Cargo cult data science

A “cargo cult” emerges when people copy a set of behaviors without understanding the “why” behind them. Many data science projects fail because they focus on implementing the technical side without pushing for a cultural shift to become data-driven. Here's how to avoid that pitfall.-Richard Weiss

I have data. I need insights. Where do I start?

What to do when your boss dumps a bunch of data in your lap and says “tell me something interesting.”-Towards Data Science

You Say Data, I Say System

Every spreadsheet or database view or visualization is the result of an entire system of decisions: how to collect, compute, and represent the data. This article provides an excellent framework for being mindful of the choices that shape the end product you see on your screen.-Hacker Noon

Rise of the Data Product Manager

“Working with data at the core of a product requires… a deep appreciation for what is possible and what will soon be possible by taking full advantage of the flow of data.”-Trey Causey

How To Get People to Value Your Analytics work — the Principle of Scarcity

It doesn’t matter if you deliver the best analyses in the world if you can’t persuade people to make use of those analyses.-Jon Tesser

How R Powers Data Science at Microsoft

“Every application we have today… can be made more intelligent by having a layer of R in the middle.”-insideBIGDATA

Two years as a Data Scientist at Stack Overflow

Two years in, David Robinson chronicles hiring a second data scientist, teaching R to developers, and writing production code.-Variance Explained

Getting started: the 3 stages of data infrastructure

Setting up data infrastructure no longer feels like 'trying to build a skyscraper using a toy hammer.' Nowadays there are so many options that assembling a data stack is downright daunting. Take a deep breath, and start here.-Nate Kupp, Thumbtack

Athos Changes the Game

Athos is revolutionizing sports performance with muscle activity based data. Learn how this smart startup uses Mode for fast access to business intelligence.-Mode

How to Hire a Product Analyst

6 things to look for in a product analyst + 14 questions you can use to tell a good candidate from a great one.-Amplitude

The Algorithms Behind Moana’s Gorgeously Animated Ocean

Disney engineers used a series of algorithms to simulate realistic water movement, like splashes, eddies, and wakes. The simulation output millions of new points of animation data that were smoothed into the final rendering of the film.-The Atlantic

New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll

KDnuggets released the results of their 18th annual analytics and data science software poll. TL;DR: Python barely overtook R and Deep Learning usage surged to 32%.-KDnuggets

How Airbnb Democratizes Data Science With Data University

“In order to inform every decision with data, it wouldn’t be possible to have a data scientist in every room—we needed to scale our skillset.”-Airbnb Engineering & Data Science

Methodologies as Vanity Metrics

“When you work on learning new methods (Now I know Random Forest! Now I know K-L Divergence! Now I know Deep Learning!) it feels good—you’re exercising your brain, you know something you didn’t before—and it’s easy to think you’re progressing. But methods don’t in and of themselves drive value.”-Ian Blumenfeld

Spotting a million dollars in your AWS account

“Shooting for 80% completeness (being willing to say 'it's good enough') ended up saving us again and again from rabbit-holing into analysis that didn’t meaningfully impact our spend.”-Segment

How we learn how you learn

Duolingo leveraged their product data to create a new statistical model for effective language learning.-Making Duolingo

Data Driven Products Now!

Here's Etsy's exact process for validating new products and features with data, before starting in on the development process.-Dan McKinley

Building Data Science Teams

Should data science teams be standalone or embedded or integrated completely? How should a company conduct their data science hiring process? Instacart's VP of data science shares their secret sauce for keeping data folks happy and productive.-Jeremy Stanley

The Key to Growth? Illuminating Your Best Bets

How Airbnb and Facebook identified their North Star metrics for growth.-Startup Grind

Reliable export of Cloud Pub/Sub streams to Cloud Storage

Here's how Spotify's data infrastructure team set up an event delivery system to handle over 100 billion events generated each day.-Spotify Labs

7 Disruptive Trends to Watch For in Analytics in 2017

A wide-sweeping look at why data tools are changing to address problems like governance, collaboration, and backlogs of data requests.-Graphiti XYZ

Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest

Learn how tech behemoths store and process petabytes of interactions, page views, and customer data.-Keen.io

Using dbt and Mode to Help Eko Rebuild its Analytics Stack

Learn how interactive video player Eko built a new data modeling layer and kept data consistent across internal and external reporting.-Mode

Custom Data Visualizations in the Workplace

ICYMI, here's a recap video of of presentations from data team leaders at Envoy, Good Eggs, and Thumbtack on their highest-impact work, how projects are conceived and received, and tips for identifying when to to invest in custom data viz.-Mode

How You Battle the "Data Wheel of Death" in Growth

“Most companies that want to get more serious about data approach it as a project. Something with a definitive start and a definitive end. In reality, data is an ongoing, never-ending project, similar to building a product.”-Coelevate

Data Minds Episode 6

Mode's CEO talks the most common misconceptions about analytics, the skills that data people need to foster to become team leaders, and how the data team can make the case to invest in the tools they need.-Data Minds

How to stay out of analytic rabbit holes: avoiding investigation loops and their traps

“[O]ver-analysis begins when the data scientist starts focusing on the hypothesis instead of the decision.” Here's how to tell the two approaches apart and avoid analysis paralysis.-cyborgus

The Startup Founder’s Guide to Analytics

“How do I build a business that produces actionable data?” is much harder to answer than “What metrics should I track?” Here's a quick start implementation guide based on the growth stage of your company.-ThinkGrowth.org

3 Tips for Centralizing Your Analytics Team Structure As You Grow

In practice, centralized analytics teams often report into product while supporting the needs of the entire business.-Mode

Data Science On The Silicon Beach

In this interview, the Chief Data Officer for the city of San Diego discusses his team’s ad-hoc approach, integrating their stack with legacy systems, and his plans for employing data to alleviate traffic congestion.-Partially Derivative

Hiring a data scientist

Hiring for a data analyst is no easy task. Wikimedia shares how they drew on existing resources to synthesize a better approach to interviewing and hiring a new member of their data team.-Wikimedia

How Fitbit’s data science team scales machine learning

Workout regimens need to be tailored to each individual. Directional correctness isn’t enough. Fitbit’s head of data science shares how his team builds a model for every user to increase motivation and prevent injuries.-Mixpanel

Scaling Data Science at Stitchfix

Not many companies can say they employ 80 data scientists. The folks at Stitchfix share their tactics for making data and compute resources more accessible—which in turn keeps data scientists happy and infrastructure healthy.-MultiThreaded

Building & Maintaining a Master Data Dictionary: Part 2

Check out these ideas for structuring key metric definitions to keep everyone at your organization on the same page.-The Data Point

What’s it like to work in sports analytics?

From the outside, crunching numbers for a national sports league seems glamorous. The cold hard truth? It’s an often thankless job with low pay and long hours. The only thing that’ll prevent burnout is a pure love of the game.-StatsbyLopez

Quora Session with Monica Rogati

The Former VP of Data at Jawbone did a Quora session last week.-Quora

How To (Actually) Calculate CAC

Quick: What’s the difference between customer acquisition cost (CAC) and cost per acquisition (CPA)? If you hesitated, this post is for you.-Brian Balfour

Breaking the Vanity Metric Cycle

“[B]reaking free of worthless metrics is hard because it is breaking a psychological reward, not just adopting some new stats.”-Amplitude

The Limitations Of Data And Benchmarks

“All the quantitative analysis in the world won’t lead me to the next great idea for startup. Those figures can’t create empathy, develop the right culture, or hire the right people.”-Tomasz Tunguz

Trust in Data Science

“An untrusted analysis is an unused one, regardless of the quality. So how does one go about building, or rebuilding, trust in the face of challenges and failure?”-Clover Health

8 Data Science Skills That Every Employee Needs

A nice primer to share with your colleagues.-Amplitude

Practical advice for analysis of large, complex data sets

“This document has been read more than anything else I’ve done at Google over the last eleven years. Even four years after the last major update, I find that there are multiple Googlers with the document open any time I check.”-The Unofficial Google Data Science Blog

Ten Ways Your Data Project is Going to Fail

“Many companies seem to go through a pattern of hiring a data science team only for the entire team to quit or be fired around 12 months later. Why is the failure rate so high?”-Martin Goodson

Why I’m Teaching Twitch to Predict the Future

Forecasting is a good habit to adopt in the workplace. It’ll help you figure out the odds of delivering on your goals. Plus, having a record of accurate predictions builds trust in your work and analytical thinking in general.-Twitch

Tracking Customer Service Metrics With SQL

This guide includes a dozen SQL queries for calculating customer service metrics with raw Intercom data.-Mode

Data Literacy, Product Design and the Many-Faced God

“Building a team that’s doing ‘cutting-edge research in deep learning, machine intelligence, and artificial intelligence’ is not easy—not in this hiring environment. But infusing data thinking throughout a company is orders of magnitude harder. This matters, because data thinking permeates your products and can make them feel ‘smart’—or not.”-Monica Rogati

Don’t Become a Victim of One Key Metric

“[T]he search for one key metric at all for a complex ecosystem like Pinterest over-simplifies how the ecosystem works and prevents anyone from focusing on understanding the different elements of that ecosystem. You want the opposite to be true.”-Casey Winters

GGPlot2 As a Creativity Engine, and Other Ways R is Transforming the Financial Times Data Journalism

Learn how the Finanical Times produces high-quality data visualizations in this presentation, complete with the R code and data used for their piece, Explore the changing tides of European footballing power.-Financial Times

Surviving Data Science “at the Speed of Hype”

Complex optimization models work best when they’re asked to deal with stable business problems, like airline scheduling or ad targeting at Google. But at a startup, where the business model is constantly changing, simply summarizing data is a much better way to find answers.-John Foreman

How We Rebuilt the Wall Street Journal’s Graphics Team

The WSJ used to have two Graphics teams—one for print and one for the web. Combining the two has allowed editors to focus on storytelling from the start of projects, instead of the medium.-Source

What I Wish I Knew About Data For Startups

One entrepreneur reflects on his learnings from four years of working with data at a startup. It’s a goldmine of advice on building a strong, scaleable data culture. Don’t skip this one. Seriously.-Jean-Nicholas Hould

Simple requirements gathering questions for dashboard design

Next time someone asks you to make a dashboard, pull this list out. It provides a framework for sussing out what’s needed for the dashboard to be useful and effective.-Paint by Numbers

FiveThirtyEight’s data journalism workflow with R

FiveThirtyEight’s quantitative editor shares the analytical process behind some of their publication’s most popular articles.-useR!

The Data Driven Daily

This newsletter provides definitions of business KPIs, how to calculate them for your business. This week they’re covering how to determine the size of your potential customer market. The archive is well worth perusing; past segments include revenue calculation and pricing strategy.-Outlier

One year as a Data Scientist at Stack Overflow

The chronicle of one data scientist’s transition from academia to the tech industry, combined with a peek into Stack Overflow’s machine learning and data infrastructure projects.-David Robinson

Whom the Gods Would Destroy, They First Give Real-time Analytics

Every few months, I try to talk someone down from building a real-time product analytics system. When I'm lucky, I can get to them early.-Dan McKinley

Real-time dashboards considered harmful

There’s a certain allure to real-time data: your team can see what’s happening right now and take action immediately. Ultimately, though, most real-time dashboards create a bunch of noise that distracts you from more important metrics.-Basecamp

Boosting Sales With Machine Learning

One developer shares how his team used natural language processing and machine learning in Python to pre-qualify sales leads so reps don’t have to spend hours doing it manually.-Xeneta

Scaling Knowledge at Airbnb

Airbnb’s data team shares their solution to ensuring insights don’t get lost in Google docs or email threads: a centralized knowledge repository.-Airbnb Engineering

Bridging the Gap Between Data Science and Data Engineering

Josh Wills, Director of Data Engineering at Slack, shares his thoughts on how data engineers and data scientists work best together.-Hakka Labs

Statistically Interesting

Craving a new data science podcast? Check out Statistically Interesting, a series of interviews with data science leaders at companies like Twitch, Vimeo, and Weebly.-Statistically Interesting

How to Make Reps Care About Data Quality

When a sales rep fails to record information about her activities or clients, it can lead to incomplete and inaccurate reports and forecasts. These tips and tricks will help sales leaders encourage reps to be vigilant about consistently logging data.-InsightSquared

The View From The Data

Making data-informed decisions has a lot more to do with people than it does with the actual data.-Karen Roter Davis

Building Data Science in Healthcare

Many tech companies have complete control over the format of the data they collect. Healthcare, which relies on external data about patients and their interactions, has no such luxury. Ian Blumenfield, Head of Data Science at Clover Health, shares how they handle messy data and the other unique data challenges the industry faces.-Clover Health

This Is How You Build Products for the New Generation of ‘Data Natives’

We’ve grown used to the idea of digital natives—the toddler who expects everything to be a touchscreen and pinches and swipes her fingers on TVs and magazines. But data natives are something different: they expect “their world to not just be digital, but to be smart and to adjust immediately to their taste and habits.” Monica Rogatti, former VP of Data at Jawbone, shares ideas for harnessing data to build products for these new consumers.-First Round Review

So you want to build a data business? Play the long game

Foursquare has demonstrated, once again, that it’s capable of predicting public company earnings with an incredible degree of accuracy based on real world foot traffic data.-Michael Carney

Hot property: How Zillow became the real estate data hub

Zillow is a real estate powerhouse, and one of their biggest competitive advantages is their massive dataset of property listings. The most interesting part of this article goes into how their data science team brings together messy data from disparate sources to create one coherent super-dataset.-InfoWorld

The Art and Science of Storytelling Through Data at Jawbone

Analysis can be worthless if it’s not communicated well. Jawbone data scientist Kirstin Aschbacher shares how she develops a data story that inspires action, from concept to presentation.-Insight Data Science

How Does the Data Science Team Work at Twitch?

In this interview, Twitch's Director of Science shares how the data science team thinks about mentorship, gaining leverage, and qualitative research.-Mode

Analyzing Your Stripe Data, Part 1: Measuring Subscription MRR

Got raw Stripe data? Want to calculate your subscription monthly recurring revenue? Lucky for you, this post provides the SQL queries you’ll need, tips for data prep, and ways to tailor the analysis to your business.-Analyst Collective

Doing Data Science Right — Your Most Common Questions Answered

This is a must-read for startup founders who want to build data science teams. It’s packed with details on the inner-workings of data-driven businesses and advice on where to start based on your company’s needs.-First Round Review

Building a high-throughput data science machine

Scaling is a problem every data science team faces. How do you go from one nomadic analyst roaming between departments to a structured team? The answer is a little different for every company, but this interview introduces some best practices to keep in mind.-O’Reilly

How to Find Correlative Metrics For Conversion Optimization

A thorough walk-through of how to find correlative metrics and leverage them for conversion. It’s jam-packed with examples and advice from experts, plus a handy list of tools.-ConversionXL

Why Airbnb Has a Data Scientist on Every Leadership Team

Airbnb's head of data science shares his keys for success in data and business.-Inc.

Riley Newman on Data Science for Startups

In this interview, Airbnb’s head data scientist Riley Newman talks about building a strong data culture, balancing technical skills with storytelling ability, and scaling data science at a high-growth startup.-Intercom

How does Lumosity use data science?

An inside look at the structure of Lumosity’s data science team and the internal tools and product features they build.-Quora

CAC Payback Period: The Most Misunderstood SaaS Metric

If you’re calculating customer acquisition cost payback period for a SasS product, keep these two things is mind: payback metrics are about risk, not return, and that most SaaS products operate on an annual model, not monthly.-Kellblog

How to catch million dollar mistakes before they cost you millions of dollars

Are you measuring the impact of back-end updates on user behavior? Failure to do so could cost you big time.-Lucidchart

The Five-Step Guide to Robust Help Center Metrics

When a documentation manager set out to revamp her company’s help site content, she was surprised to find very few resources on how to measure her project. Thankfully, she documented her journey so we can all learn from it. Great tips in here for anyone looking to make their help center more, well… helpful.-RJMetrics

The Role of Statistical Significance in Growth Experiments

When you run an experiment, you’re looking for statistically significant results. But if you’re running growth experiments on a product—iterating quickly to optimize—the standard rules of statistical significance may not apply.-Medium

Minimum Viable Onboarding for PMs

A product manager from Doordash shares his thoughts on the most successful employee onboarding process he’s experienced. Spoiler—it involves data analysis.-Charlton Soesanto

7 Steps to Measuring the Success of a Feature

You’ve spent months working on a feature and now it’s live. How do you tell if users actually like it? Dig into your user data and start measuring with this detailed walkthrough.-Amplitude

Highly Effective Data Science Teams

To do great data science work, you need more that a huge heap of data. This article offers 14 criteria for assessing your team’s effectiveness.-Twitch

What BuzzFeed’s Dao Nguyen Knows About Data, Intuition, And The Future Of Media

This entire article is worth reading, but skip to the middle for the real gem—publisher Dao Nguyen’s holistic philosophy on data at BuzzFeed. “[Y]ou can’t only use comments, you can’t only use data, you can’t only use anything. You can’t only use your own intuition, either. It has to be all of those things you use.”-Fast Company

Diligence at Social Capital, Epilogue: Introducing the 8-ball and 'GAAP for Startups'

Figuring out what metrics to present to investors can be a struggle for startups. That’s because there’s really no standardized metrics or reporting in the startup world. Venture capital firm Social Capital is hoping to change that with their tool for gauging product-market fit at early stage companies. Plug in your own data and give it a whirl.-Jonathan Hsu

Building a business that combines human experts and data science

An insightful interview with Eric Colson about algorithms, human computation, and building data science teams at Stitch Fix and Netflix.-O’Reilly Data Show Podcast

You’re Measuring Daily Active Users Wrong

A high number of daily active users (DAU) may sound impressive, but does it actually mean anything? To make your DAU metric actionable, you need to measure how often users are getting core value out of your product, not how many times they log in.-Amplitude

The Ecommerce Holiday Customer Benchmark

Those new customers from the holiday season are more valuable than you thought. So when should you engage these shoppers to turn them into repeat customers?-RJMetrics

How Toyota Revamped Its Collections Biz with Big Data Analytics

Toyota Financial Services (TFS) used to collect car payments with a one-size-fits-all approach. Then the recession hit. For the first time, 100,000 people a day were behind on their payments. With a massive analytics overhaul, TFS were able to personalize their collection strategies and help 6,000 customers keep their cars from being repossessed.-Datanami

How Instacart Uses Redshift to Drive Growth

In this interview, Fareed Mosavat, growth PM at Instacart, shares how his team combines behavior, shipping, and fulfillment data to inform product decisions. Check out how his team uses SQL to define internal metrics, conduct A/B tests, and discover how many touches it takes before a user makes their first order.-Segment

Wrangle Conference 2015

If you didn’t get to attend the Wrangle Conference in October, now’s a good time to catch up.-Cloudera

Fashion Goes Deep. Data Science at Lyst

Fashion moves quickly. So, too, does the data science that powers e-commerce sites. In this interview, Lyst lead data scientist Eddie Bell shares the ins and outs of their recommendation engine. Learn how his team has tackled the challenges of constantly changing merchandise and kept suggestions fresh using machine learning and image analysis.-Fast Forward Labs

Data Down on the Farm

This episode is part of a series about data and food from Andreessen Horowitz. Learn how farmers are using software and analytics programs to monitor crop health and performance, implement agricultural policies, and adopt revenue-focused business practices.-Andreessen Horowitz

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