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I hereby claim:

  • I am drewfustin on github.
  • I am drewfustin (https://keybase.io/drewfustin) on keybase.
  • I have a public key ASC-73bOZhc3_DutuKYqamSwo2LEbzz1P1OOaalJXFtU-Qo

To claim this, I am signing this object:

@drewfustin
drewfustin / logs-28.txt
Created March 15, 2022 13:46
Airbyte logs resulting in missing records after replicating shortened_urls from Heroku Postgres to Redshift
2022-03-15 13:29:15 INFO i.a.w.w.WorkerRun(call):49 - Executing worker wrapper. Airbyte version: 0.35.54-alpha
2022-03-15 13:29:15 INFO i.a.w.t.TemporalAttemptExecution(get):105 - Docker volume job log path: /tmp/workspace/28/0/logs.log
2022-03-15 13:29:15 INFO i.a.w.t.TemporalAttemptExecution(get):110 - Executing worker wrapper. Airbyte version: 0.35.54-alpha
2022-03-15 13:29:15 INFO i.a.w.DefaultReplicationWorker(run):104 - start sync worker. job id: 28 attempt id: 0
2022-03-15 13:29:15 INFO i.a.w.DefaultReplicationWorker(run):116 - configured sync modes: {public.shortened_urls=full_refresh - append}
2022-03-15 13:29:15 INFO i.a.w.p.a.DefaultAirbyteDestination(start):69 - Running destination...
2022-03-15 13:29:15 INFO i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/destination-redshift:0.3.27 exists...
2022-03-15 13:29:15 INFO i.a.c.i.LineGobbler(voidCall):82 - airbyte/destination-redshift:0.3.27 was found locally.
2022-03-15 13:29:15 
@drewfustin
drewfustin / exp__autograde_vs_price.ipynb
Created February 11, 2020 22:58
Experiment: Autograde/Odometer vs Wholesale Price
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@drewfustin
drewfustin / metis_panel_answers.md
Created May 24, 2018 20:16
Panel of Hiring Partners: How to Get Hired as a Data Scientist

My answers for Panel of Hiring Partners: How to Get Hired as a Data Scientist, an event put on by Metis tonight.

What are the top 3 qualities you look for when hiring a data scientist?

  • Can they figure things out? With the rare exception (e.g. when I'm looking to fill a particular exact skillset role with someone with marketing experience), I don't expect you to know a common series of steps to go from point A to point B in an analysis. This isn't what the job typically looks like in the environments I've been in. What I expect is that you can understand the need, be able to come up with a strategy that improves our ability to fill that need, understand how to check your solution for gotchas (by writing quality tests and by knowing which quality statistics to check against to make sure your solutions isn't diverging from reality), and implement a solution that allows you to iterate on its quality but also to move on to the next

Keybase proof

I hereby claim:

  • I am drewfustin on github.
  • I am drewfustin (https://keybase.io/drewfustin) on keybase.
  • I have a public key ASAWFQnNe348_u-iPA4wGcJeucsjq0p51Vbq8RhqZFoZ9Qo

To claim this, I am signing this object:

@drewfustin
drewfustin / gist:ecca70d2b9779e97ff57
Created November 17, 2015 18:50
Predicting Future Orders (boolean)
This file has been truncated, but you can view the full file.
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
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drewfustin / gist:85183dba59d2b2ed8c11
Created May 13, 2015 15:03
Deflategate temperature heatmap
{
"cells": [
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
{
"nbformat_minor": 0,
"cells": [
{
"execution_count": 68,
"cell_type": "code",
"source": "import pandas as pd\nimport numpy as np\nfrom science.loaders.forecasting.single_well_prod import model_battle_royale\n\nfc_field = 'monthly_well_oil_prod'\nmodels = ['hw', 'aa', 'mc']\n\ndf = pd.DataFrame({\n '{}'.format(fc_field): [7, 10, 12, 12, 14, np.nan],\n '{}_{}_fc'.format(fc_field, models[0]): [np.nan, 10, 11, 12, 13, 14],\n '{}_{}_lb'.format(fc_field, models[0]): [np.nan, 9, 10, 10, 11, 12],\n '{}_{}_ub'.format(fc_field, models[0]): [np.nan, 11, 12, 14, 16, 17],\n '{}_{}_fc'.format(fc_field, models[1]): [np.nan, 12, 9, 5, 4, 4],\n '{}_{}_lb'.format(fc_field, models[1]): [np.nan, 9, 7, 3, 1, 0],\n '{}_{}_ub'.format(fc_field, models[1]): [np.nan, 16, 11, 8, 6, 6],\n '{}_{}_fc'.format(fc_field, models[2]): [np.nan, 10, 11, 12, 10, 11],\n '{}_{}_lb'.format(fc_field, models[2]): [np.nan, 7, 8, 9, 6, 6],\n '{}_{}_ub'.form