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Showing posts with the label #fullstack

downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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GraphQL with Apollo

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My notes from  Alexey Kureev's  talk titled "Apollo Client: the stuff no-one ever told ya"   by  @klarna  in the React & React Native Meetup. Meetup   link : REST is very widely used but as web applications have evolved over time with most of the processing happening at the client some of its features are now seen as performance bottlenecks. Some examples are that endpoint are separated  as well as the entities  but we typically want to query for data representing some relation between endpoint and slices of the entities. With REST this would require multiple requests and getting the full entities. GraphQL lets us do this using a single request and provides a more sophisticated way to make queries. GraphQL is the evolution... Benchmarks published by facebook claim a  The slides show how to consume a GraphQL data source using react. Rest is now vintage - separated endpoint with separated entities REST cons: ...
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Location: Yigal Alon St 98, Tel Aviv-Yafo, Israel
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Automating web app development with Polymer and Yeoman

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Yeoman lets you configure and  stamp out sophisticated boilerplate projects from the command line. In a digital marketing agency  the data science team may be asked to provide for each client's campaign fairly similar  media reports and explanatory analytics dashboards for both external clients and internal clients. For longer term project we may be asked to also provide predictive analytics. The data comes from advertisers Google, Facebook, Taboola, OutBrain, phone tracking metrics API, outcomes are channels via segment into say Google Analytics which has both an API and polymer components. Usually there will be additional data science products prediction special segment data, funnels, market research, attribution charts which and long term data in BigQuery which has an API as well. Some vendors don't have an API so to access their data it is exported into a CSV and placed into google sheets which has an API and a Polymer component phone tracking. It takes too...
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Popular posts from this blog

downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Random Thoughts on Linear Regressions

Regression Analysis TLDR Regression is the oldest and most powerful tool in a data scientist's toolbox. Under ideal conditions multiple linear regression would be the best and only tool a data scientist would want to use... In reality you would use a modern nonparametric variant or a different algorithm. Still, the main ideas I . discuss here  will pop up in many other models and algorithms. This post is my brain dump on regression - I'll update it as time allows to cover the many aspects of this technique.
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SQL - Selection v.s. Projection

Selection and projection are two high level processes taking place when SQL queries are executed. Selection is choosing some records (rows) from a table and leaving others out. e.g. rows having name='oren'. Projection is the choosing some columns from each record and leaving others out. e.g. only name. So the select keyword performs projection while the where and keyword performs selection. Clearly the choice of using the keyword select for projection (choosing columns) rather than choosing rows, is an unfortunate flaw in the design of SQL, but this oversight is too well established to be fixed.
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