Unevenness at the edges

Marton Trencseni - Fri 30 October 2020 • Tagged with stats, data

Sometimes we look at the top performers in a field and see obviously uneven representations of groups (gender, ethnicity, etc). There a multitude of factors that can lead to it, such as unfair bias in access to opportunities. Here I will show one unintuitive mathematical effect that can contribute to such unevenness in the case of normal distributions.

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Effective Data Visualization Part 3: Line charts and stacked area charts

Marton Trencseni - Tue 01 September 2020 • Tagged with charts, dashboards, data, visualization

Most charts should be line charts or stacked area chart, because they communicate valuable trend information and are easy to parse for the human eyes and brain.

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Effective Data Visualization Part 2: Formatting numbers

Marton Trencseni - Sun 23 August 2020 • Tagged with charts, dashboards, data, visualization

Format numbers for human consumption. What is more readable, 1.539e+5 or 153,859? Showing numbers effectively on spreadsheets, charts, dashboards, reports is a basic ingredient for readability, like formatting code in programming.

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Effective Data Visualization Part 1: Categorical data

Marton Trencseni - Sat 22 August 2020 • Tagged with charts, dashboards, data, visualization

Making clear, readable charts is part of the craftmanship minimum for any data related role. In part one, I look at how to present categorical data.

A pie chart

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Beyond the Central Limit Theorem

Marton Trencseni - Thu 06 February 2020 • Tagged with data, ab testing, statistics

In the previous post, I talked about the importance of the Central Limit Theorem (CLT) to A/B testing. Here we will explore cases when we cannot rely on the CLT to hold.

Running mean for Cauchy distribution

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A/B testing and the Central Limit Theorem

Marton Trencseni - Wed 05 February 2020 • Tagged with data, ab testing, statistics

When working with hypothesis testing, the desciptions of the statistical method often has normality assumptions. For example, the Wikipedia page for the z-test starts like this: "A Z-test is any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution". What does this mean? How do I know it’s a valid assumption for my data?

Normal distribution from uniform

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Optimizing waits in Airflow

Marton Trencseni - Sat 01 February 2020 • Tagged with data, airflow, python

Sometimes I get to put on my Data Engineering hat for a few days. I enjoy this because I like to move up and down the Data Science stack and I try to keep myself sharp technically. Recently I was able to spend a few days optimizing our Airflow ETL for speed.

Airflow DAG

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SQL best practices for Data Scientists and Analysts

Marton Trencseni - Sun 26 January 2020 • Tagged with data, programming, sql

My list of SQL best practices for Data Scientists and Analysts, or, how I personally write SQL code. I picked this up at Facebook, and later improved it at Fetchr.

SQL code

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How I write SQL code

Marton Trencseni - Fri 24 January 2020 • Tagged with data, programming, sql

This is a simple post about SQL code formatting. Most of this comes from my time as a Data Engineer at Facebook.

SQL code

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Metrics Atlas

Marton Trencseni - Thu 29 August 2019 • Tagged with data, fetchr

The idea is simple: write a document which helps new and existing people—both managers and individual contributors—get an objective, metrics-based picture of the business. This is helpful when new people join, when people start working in new segments of the business, and to understand other parts of the company.

Metrics atlas

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5 things that happened in Data Science in 2018

Marton Trencseni - Wed 09 January 2019 • Tagged with data, openai, waymo, deepmind, tesla, reinforce

2018 was a hot year for Data Science and AI. Here we picked out 5 highlights, which in our opinion shaped the field in the past year.

Deepmind playing CTF

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Warehouse locations with k-means

Marton Trencseni - Wed 26 September 2018 • Tagged with data, data-science, metrics, fetchr

Sometimes, the seven gods of data science, Pascal, Gauss, Bayes, Poisson, Markov, Shannon and Fisher, all wake up in a good mood, and things just work out. Recently we had such an occurence at Fetchr, when the Operational Excellence team posed the following question: if we could pick our Saudi warehouse locations, where would be put them? What is the ideal number of warehouses, and, what does ideal even mean? Also, what should our “delivery radius” be?

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Growth Accounting and Backtraced Growth Accounting

Marton Trencseni - Sun 16 September 2018 • Tagged with data, data-science, metrics, growth-accounting, fetchr

Previously I wrote two articles about data infra and data engineering at Fetchr. This time I want to move up the stack and talk about a simple piece of metrics engineering that proved to be very impactful: Growth Accounting and Backtraced Growth Accounting.

Backtraced Growth Accounting

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Fetchr Data Science Infra at 1 year

Marton Trencseni - Tue 14 August 2018 • Tagged with data, etl, workflow, airflow, fetchr, model, ml

A description of our Analytics+ML cluster running on AWS, using Presto, Airflow and Superset.

Fetchr Data Science Infra

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Beat the averages

Marton Trencseni - Sat 07 July 2018 • Tagged with statistics, data

When working with averages, we have to be careful. There are pitfalls lurking to pollute our statistics and results reported.

Probability distribution

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Building the Fetchr Data Science Infra on AWS with Presto and Airflow

Marton Trencseni - Wed 14 March 2018 • Tagged with data, etl, workflow, airflow, fetchr

We used Hive/Presto on AWS together with Airflow to rapidly build out the Data Science Infrastructure at Fetchr in less than 6 months.

Warehouse DAG

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Don’t build cockpits, become a coach

Marton Trencseni - Wed 09 November 2016 • Tagged with data, science, product, analytics

I used to think that a good analogy for using data is the instrumentation of a cockpit in an airliner. Lots of instruments, and if they fail, the pilot can’t fly the plane and bad things happen. There’s no autopilot for companies. The problem with this analogy is that planes aren’t built in mid-air. Product teams and companies constantly need to build and ship new products.

A big complicated cockpit

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Beautiful A/B testing

Marton Trencseni - Sun 05 June 2016 • Tagged with ab-testing, strata, statistics, data

I gave this talk at the O’Reilly Strata Conference London in 2016 June, mostly based on what I learned at Prezi from 2012-2016.

14. slide

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Luigi vs Airflow vs Pinball

Marton Trencseni - Sat 06 February 2016 • Tagged with data, etl, workflow, luigi, airflow, pinball

A spreadsheet comparing the three opensource workflow tools for ETL.

Comparison

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Pinball review

Marton Trencseni - Sat 06 February 2016 • Tagged with data, etl, workflow, pinball

Pinball is an ETL tool written by Pinterest. Like Airflow, it supports defining tasks and dependencies as Python code, executing and scheduling them, and distributing tasks across worker nodes. It supports calendar scheduling (hourly/daily jobs, also visualized on the web dashboard). Unfortunately, I found Pinball has very little documentation, very few recent commits in the Github repo and few meaningful answers to Github issues by maintainers, while it's architecture is complicated and undocumented.

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Airflow review

Marton Trencseni - Wed 06 January 2016 • Tagged with data, etl, workflow, airflow

Airflow is a workflow scheduler written by Airbnb. It supports defining tasks and dependencies as Python code, executing and scheduling them, and distributing tasks across worker nodes. It supports calendar scheduling (hourly/daily jobs, also visualized on the web dashboard), so it can be used as a starting point for traditional ETL. It has a nice web dashboard for seeing current and past task state, querying the history and making changes to metadata such as connection strings.

Airflow

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Luigi review

Marton Trencseni - Sun 20 December 2015 • Tagged with data, etl, workflow, luigi

I review Luigi, an execution framework for writing data pipes in Python code. It supports task-task dependencies, it has a simple central scheduler with an HTTP API and an extensive library of helpers for building data pipes for Hadoop, AWS, Mysql etc. It was written by Spotify for internal use and open sourced in 2012. A number of companies use it, such as Foursquare, Stripe, Asana.

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Cargo Cult Data

Marton Trencseni - Mon 26 January 2015 • Tagged with data

Cargo cult data is when you're collecting and looking at data when making decisions, but you're only following the forms and outside appearances of scientific investigation and missing the essentials, so it doesn't work.

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