Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
Enabling Multimodel Graphs with Apache TinkerPopJason Plurad
Graphs are everywhere, but in a modern data stack, they are not the only tool in the toolbox. With Apache TinkerPop, adding graph capability on top of your existing data platform is not as daunting as it sounds. We will do a deep dive on writing Traversal Strategies to optimize performance of the underlying graph database. We will investigate how various TinkerPop systems offer unique possibilities in a multimodel approach to graph processing. We will discuss how using Gremlin frees you from vendor lock-in and enables you to swap out your graph database as your requirements evolve. Presented at Graph Day Texas, January 14, 2017. http://graphday.com/graph-day-at-data-day-texas/#plurad
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
Enabling Multimodel Graphs with Apache TinkerPopJason Plurad
Graphs are everywhere, but in a modern data stack, they are not the only tool in the toolbox. With Apache TinkerPop, adding graph capability on top of your existing data platform is not as daunting as it sounds. We will do a deep dive on writing Traversal Strategies to optimize performance of the underlying graph database. We will investigate how various TinkerPop systems offer unique possibilities in a multimodel approach to graph processing. We will discuss how using Gremlin frees you from vendor lock-in and enables you to swap out your graph database as your requirements evolve. Presented at Graph Day Texas, January 14, 2017. http://graphday.com/graph-day-at-data-day-texas/#plurad
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
It's all about introduction to a blog which speaks about Destinations, Arts, Culture, People, Cuisines...Everything you would want to know about Kerala
Discover Life. Feel Divinity. Find Yourself...........Experience God's Own Country
An overview of Teraproc cluster-as-a-service offerings for high-performance distributed analytics. This overview presentation includes a step-by-step demonstration of the process of deploying a ready-to-run R Studio cluster environment on Amazon Web Services. More information available at http://teraproc.com
Top Insights from SaaStr by Leading Enterprise Software ExpertsOpenView
Market Research
SHARE
I had the pleasure of attending the SaaStr Annual 2016 Conference in San Francisco earlier this month and wanted to share some of the insights I gathered from that event with you here. The findings below are arranged by functional area with attribution. I tried to compress the content as much as possible, but there was A TON of great information at the conference so would highly recommend spending the time to read through.
In era of Morden Technology, AngularJS is a structural open source Web and Mobile Application Development Framework popular because of it's strong features. Brainvire Provide the Best Development services for AngularJS Technology. See more on : http://www.brainvire.com/angular-js-and-react-js/
A look at where the market of the Internet of Things is and how technologies like Node.js (JavaScript) and the Intel Edison are making it easier to create connected solutions.
Learn more at https://losant.com.
The major topics include:
* What is the Internet of Things
* Where is IoT Today
* 4 Parts of IoT (Collect, Communicate, Analyze, Act)
* Why JavaScript is Good for IoT
* How Node.js is Making a Dent in the Internet of Things
* What npm Modules are used for Hardware (Johnny-Five, Cylon.js, MRAA)
* What is the Intel Edison
* How to Best Work with the Edison
* Tips for Edison (MRAA, Grove Kit, UPM)
* Where the World of JavaScript and IoT is Going
Databricks Spark Chief Architect Reynold Xin's keynote at Spark Summit East 2016, discussing streaming, continuous applications, and DataFrames in Spark.
It's all about introduction to a blog which speaks about Destinations, Arts, Culture, People, Cuisines...Everything you would want to know about Kerala
Discover Life. Feel Divinity. Find Yourself...........Experience God's Own Country
An overview of Teraproc cluster-as-a-service offerings for high-performance distributed analytics. This overview presentation includes a step-by-step demonstration of the process of deploying a ready-to-run R Studio cluster environment on Amazon Web Services. More information available at http://teraproc.com
Top Insights from SaaStr by Leading Enterprise Software ExpertsOpenView
Market Research
SHARE
I had the pleasure of attending the SaaStr Annual 2016 Conference in San Francisco earlier this month and wanted to share some of the insights I gathered from that event with you here. The findings below are arranged by functional area with attribution. I tried to compress the content as much as possible, but there was A TON of great information at the conference so would highly recommend spending the time to read through.
In era of Morden Technology, AngularJS is a structural open source Web and Mobile Application Development Framework popular because of it's strong features. Brainvire Provide the Best Development services for AngularJS Technology. See more on : http://www.brainvire.com/angular-js-and-react-js/
A look at where the market of the Internet of Things is and how technologies like Node.js (JavaScript) and the Intel Edison are making it easier to create connected solutions.
Learn more at https://losant.com.
The major topics include:
* What is the Internet of Things
* Where is IoT Today
* 4 Parts of IoT (Collect, Communicate, Analyze, Act)
* Why JavaScript is Good for IoT
* How Node.js is Making a Dent in the Internet of Things
* What npm Modules are used for Hardware (Johnny-Five, Cylon.js, MRAA)
* What is the Intel Edison
* How to Best Work with the Edison
* Tips for Edison (MRAA, Grove Kit, UPM)
* Where the World of JavaScript and IoT is Going
Databricks Spark Chief Architect Reynold Xin's keynote at Spark Summit East 2016, discussing streaming, continuous applications, and DataFrames in Spark.
Slides from my workshop on how to use Kicad 7 at Bitraf Hackerspace and Makerspace in Oslo. In the workshop, we made a small circuit that all attendees could have produced.
Slides from my beginners workshop on the great Open Source extension for Visual Studio Code called PlatformIO. Covers getting started using an Arduino or ESP32 as well as the GIT integration.
Slides from my Arduino Basics workshop at bitraf hackerspace in Oslo, Norway. Covers the basics - getting started, connecting things, blinking leds, using buttons and simple sensors.
Slides from my Maintenance workshop at Bitraf November 2022. Covers manitaining laser cutters, 3d printers, cnc, workshop and all our usual tools as well as Bitraf itself.
Elektronikk Workshop, Dag 2 (montering og testing)Jens Brynildsen
Slides from day two of my two day workshop on electronics design. Day 1 was a month ago where participants learned how to design an ESP32 based dev board. Now that the boards are produced, we're building them at Bitraf (Norway's largest Hackerspace/Makerspace)
Slide deck from my Multimeter workshop at Bitraf December 8th 2021. Feel free to reuse the deck for hosting your own Multimeter training, but please leave all credits in. CC-BY-SA-4.0
Slides from my soldering workshop at Bitraf 30th November 2021. The slides are in Norwegian, but I might make an English version later. Workshop goes through what is needed, what to do and what to avoid, tips and tricks as well as the advanced soldering gear found at the Bitraf Hackerspace in Oslo. (CC BY-SA 4.0)
Slides from our beginners workshop on Arduino hosted at the Bitraf hackerspace in Oslo. Made to guide someone that is completely new to Arduino to the point they can blink an LED, use buttons, sounds and simple sensors. (CC BY-SA 4.0)
6. Plan for kvelden
@jenschr / jensa@flashgamer.com
• De som ikke har Photon og komponenter kan kjøpe i
Bitmart
• Hjelpe dere gjennom et sett med standard øvelser
• Se på litt mer avansert bruk (Webhooks)
• Hjelpe dere videre med eget prosjekt
• Hvorfor Particle er nyttig om du vil lage egne IoT dingser
• Fordeler og bakdeler med Photon
Photon IoT
44. Metode 1
Embedded systems@jenschr / jensa@flashgamer.com
• Koble Photon til PC med USB kabel (for strøm)
• Hvis den ikke blinker blått, hold nede Setup-knappen i 3
sekunder
• Last ned Particle-appen for din mobiltelefon
• Følg instruksjonene i app’en. Navnet på din Photon står
på undersiden av den lille boksen den kommer i.
SSID: bitraf Passord: grimbadgerassault
47. Metode 2
Embedded systems@jenschr / jensa@flashgamer.com
• Installer Photon Commando Linje Interface (CLI)
https://docs.particle.io/guide/tools-and-features/cli/photon/
• Sjekk at Photon CLI er riktig installert:
$ particle
• Sett opp wifi:
$ particle setup wifi
• For å identifisere hvilken Photon du har tilkoblet
$ particle identify
SSID: bitraf Passord: grimbadgerassault
48. Debugging
Embedded systems@jenschr / jensa@flashgamer.com
• For å bruke Serial til debugging:
$ particle serial list
$ particle serial monitor COM3
76. Fordeler
Embedded systems@jenschr / jensa@flashgamer.com
• Enkelt å komme i gang med prototyping
• Raskt å gå fra prototype til produkt
• Over The Air (OTA) oppdateringer
• Device management/ownership
• Enkelt oppsett av Wifi
77. Ulemper
Embedded systems@jenschr / jensa@flashgamer.com
• Dyrt pr enhet
• Deler av API er lukket (Broadcom)
• Ingen direkte kontroll på API
• Avhengighet av en leverandør
• Løpende kostnad (ikke for prototyping)
78. + gave fra Particle
Jobb med egne ideer
@jenschr / jensa@flashgamer.com