Podcasts about Data science

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  • 1,979PODCASTS
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Best podcasts about Data science

Show all podcasts related to data science

Latest podcast episodes about Data science

DataFramed
#76 Providing Financial Inclusion with Data science, with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma

DataFramed

Play Episode Listen Later Nov 29, 2021 52:29


In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.Throughout the episode, Vishnu discusses his background, Credit Karma's mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more. Relevant links from the interview:You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. Data roles at Credit KarmaCredit Karma's mission

Data And Analytics in Business
E83 - Steven Tye - The Data in Your Water - Smart Water Metering

Data And Analytics in Business

Play Episode Listen Later Nov 28, 2021 57:10


What is Smart Water Metering and what does it mean for sustainability across Australia? Is this the future of water conservation? Enter into the movement of smart cities and have a look into a brighter future - one step at a time - with Steven Tye. Meet Steven Tye Steven's Role as a Smart Water Utilities Leader at Tyeware Steven Tye has been the Owner, Director, and Solutions Consultant at Tyeware for the past 14 years and counting. He has been involved in smart metering projects since 2011 and has experience working with over 50 water utilities on smart metering pilots and rollouts. Tyeware is an internationally recognized digital water metering & IoT solutions provider leading Australia's IoT revolution in smarter water solutions for agriculture. Data Science in Smart Water Metering Solution They developed the MiWater and MyH2O systems for Mackay Regional Council and now partner with Taggles Systems for the Australian and international commercialisation of this software suite. MiWater processes, interprets and integrates data received from Automated Meter Infrastructure (AMI) to give utilities control and oversight over their water infrastructure, from the largest networks down to individual services. MiWater has assisted in identifying leaks totaling six billion litres of water, in turn, saving residents of the Mackay region hundreds of dollars every year. In addition, MiWater assisted in regulating water consumption among the town's population, allowing the Mackay Regional Council to reach its goal of reducing annual water consumption by 10%. Customer Experience and Telemex More recently, Tyeware has developed "Telemex", an industry-first integrated CRM, demand management and customer portal for water irrigation schemes. It includes the full management of water allocations, titles, transfers and changeovers, as well as meter management, scheme management and automated notifications. At the heart of Telemex is a customer portal used by water customers to lodge, manage and visualise the impact of water orders. Telemex builds on Tyeware's expertise and innovation across the Australian water industry to cultivate water sustainability through customer-centric solutions. Data Analytics and Smart Water In this exclusive analytics podcast episode, Steven shares: His entrepreneurial journey and experience in growing a software business What a smart water metering solution is and why it plays a vital role in solving climate change Real-life examples of how utilities, local governments, and agriculture businesses use the solution in their own businesses The type of data collected for these solutions How analytics is used to identify water leakages and plans for infrastructure development How he started his journey in developing smart water metering solutions The challenges and advice in applying data analytics with a digital water metering solution If you are in a C-suite position, working for a utility company or local council, and find yourself intrigued about how data science can assist you with infrastructure planning and engaging with your customers, this is the episode you do not want to miss out on. --- Send in a voice message: https://anchor.fm/analyticsshow/message

Tech Talks
"You kind of want to learn about this as a data scientist".

Tech Talks

Play Episode Listen Later Nov 26, 2021 38:14


Sophia is a Data Scientist and currently works at Sky in a senior position. She's moved from academia, to startups to enterprise, and we ask her why? Data Science remains under huge pressure (from a lack of talent), and today's episode contains plenty of insight into how these professionals tick!

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion
AI Today Podcast: Data Science through a Human Lens – Interview with Felipe Flores, host of Data Futurology Podcast

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

Play Episode Listen Later Nov 26, 2021


On the AI Today podcast we regularly interview thought leaders who are implementing AI and cognitive technology at various companies and agencies. However in this episode hosts Kathleen Walch and Ron Schmelzer interview Felipe Flores, host of Data Futurology Podcast. On his podcast he talks about Data Science through a human lens and discusses the human side of data with leaders from across the globe. Continue reading AI Today Podcast: Data Science through a Human Lens – Interview with Felipe Flores, host of Data Futurology Podcast at Cognilytica.

The Artists of Data Science
NLP and Philosophy | Kourosh Alizadah

The Artists of Data Science

Play Episode Listen Later Nov 26, 2021 61:43


Watch the video of this episode: https://youtu.be/3GG9snF8p7o Find Kourosh Alizadah online: https://www.linkedin.com/in/kcalizadeh/ https://philosophydata.com/ Memorable Quotes from the Episode: [00:20:22] "...one word that's very commonly used in philosophy is the word substance and in everyday language. It just means like stuff. But in philosophy, it means like the substrate upon which all the properties change, right? So like what is the substance of a stone that stays the same even when it changes color or breaks or something like that." Highlights of the Show: [00:01:16] Guest Introduction. [00:03:34] Where you grew up and what it was like there? [00:04:41] How did you figure out who you want to be? - What did you think your feature is going to look like? [00:06:13] Do we still have philosophers who study "philosophy and ideas"? [00:07:59] The philosophy of Data science is if we had to kind of pin that, would there be a philosophy to Data science or of Data science? [00:09:22] What is Data? How is it different from information or data and information? Are they the same thing? [00:10:29] The concept of "philosophy data project". [00:11:41] Transition from a capstone project to flat iron Data science boot camp. [00:18:39] Did you actually read a lot of books? * [00:55:10] Random Rround [00:55:12] When do you think the first video to hit $1 trillion views on YouTube will happen and what will it be about? [00:56:07] What do most people think within the first few seconds of meeting you for the first time? [00:56:24] What are you currently reading right now? [00:57:18] What song do you currently have on repeat? [00:58:29] Pet, peeves. [00:58:37] Who are some of your heroes? [00:59:30] When people come to you for help, what do they usually want help with? [01:00:01] If you lost all of your possessions, but one, what would you want it to be? [01:00:14] What fictional place would you most like to go to? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

The Turing Podcast
Careers in data science with Accenture

The Turing Podcast

Play Episode Listen Later Nov 26, 2021 50:28


The latest episode of the Turing Podcast features a special roundtable discussion with our strategic partner Accenture about career options in the data science sector. The latest episode of the Turing Podcast features a special roundtable discussion with our strategic partner Accenture about career options in the data science sector. Our hosts Jo Dungate and Bea Costa Gomes were joined by three influential figures in AI and data science - Henrietta Ridley (Data Science Manager at Accenture), Alice Aspinall (Senior Manager at Mudano), and Kirstie Whitaker (the Turing's Director for the tools, practices and systems programme). Our guests brought their different experiences and perspectives to an insightful discussion on various aspects of the data science industry, from how they first got into their fields, their career motivations and lessons learned along the way. The episode concludes with each guest offering advice to anyone at the beginning of their career.

The Accad and Koka Report
Ep. 185 James Heathers on Ivermectin, Data Science, and Trust

The Accad and Koka Report

Play Episode Listen Later Nov 23, 2021 66:31


Our guest is James Heathers, physiologist, scientist, and part-time "data thug" who's developed the technique of sniffing out fraudulent or highly erroneous scientific publications into an art. He has recently published "https://www.theatlantic.com/science/archive/2021/10/ivermectin-research-problems/620473/ (The Real Scandal About Ivermectin)," an article in The Atlantic featuring an analysis that he and his colleagues have performed identifying devastating flaws in some randomized control trials that purportedly showed Ivermectin to be effective against COVID-19. The article discusses the Ivermectin story in the broader context of the reliability of scientific publications. James is also Chief Scientific Officer at the technology company Cipher Skin. GUEST: James Heathers, PhD: https://twitter.com/jamesheathers (Twitter), and https://everythinghertz.com/ (podcast) PREVIOUS APPEARANCE ON THE PODCAST: https://accadandkoka.com/episodes/episode65/ (Ep. 65) James Heathers: Why Science Needs Data Thugs. WATCH ON YOUTUBE: https://youtu.be/n0USAdlkU34 (Watch the episode) on our YouTube channel Support this podcast

Radio Duna | Hablemos en Off
Las miradas al centro de los candidatos a las presidencia y el rol del PDG y la DC

Radio Duna | Hablemos en Off

Play Episode Listen Later Nov 23, 2021


Nicolás Vergara, Consuelo Saavedra y Matías del Río revisaron las principales informaciones de la mañana y conversaron con Cristóbal Huneeus, director de DataScience, Unholster, quien hizo un análisis sobre las elecciones presidenciales y parlamentarias ocurridas este domingo, donde hizo énfasis en qué se viene para Gabriel Boric y José Antonio Kast.

Python Bytes
#260 It's brutally simple: made just from pickle and zip

Python Bytes

Play Episode Listen Later Nov 23, 2021 48:49


Watch the live stream: Watch on YouTube About the show Sponsored by Shortcut - Get started at shortcut.com/pythonbytes Special guest: Chris Patti Brian #1: Using cog to update --help in a Markdown README file Simon Willison I've wanted to have a use case for Ned Batchelder's cog Cog is a utility that looks for specially blocks [[[cog some code ]]] and [[[end]]] These block can be in comments, [HTML_REMOVED] for markdown. When you run cog on a file, it runs the “some code” and puts the output after the middle ]]] and before the [[[end]]]. Simon has come up with an excellent use, running --help and capturing the output for a README.md file for a CLI project. He even wrote a test, pytest of course, to check if the README.md needs updated. Michael #2: An oral history of Bank Python Bank Python implementations are effectively proprietary forks of the entire Python ecosystem which are in use at many (but not all) of the biggest investment banks. The first thing to know about Minerva is that it is built on a global database of Python objects. Barbara is a simple key value store with a hierarchical key space. It's brutally simple: made just from pickle and zip. Applications also commonly store their internal state in Barbara - writing dataclasses straight in and out with only very simple locking and transactions (if any). There is no filesystem available to Minerva scripts and the little bits of data that scripts pick up has to be put into Barbara. Barbara also has some "overlay" features: # connect to multiple rings: keys are 'overlaid' in order of # the provided ring names db = barbara.open("middleoffice;ficc;default") # get /Etc/Something from the 'middleoffice' ring if it exists there, # otherwise try 'ficc' and finally the default ring some_obj = db["/Etc/Something"] Lots of info about modeling with classes (instruments, books, etc) If you understand excel you will be starting to recognize similarities. In Excel, spreadsheets cells are also updated based on their dependencies, also as a directed acyclic graph. Dagger allows people to put their Excel-style modelling calculations into Python, write tests for them, control their versioning without having to mess around with files like CDS-OF-CDS EURO DESK 20180103 Final (final) (2).xlsx. Dagger is a key technology to get financial models out of Excel, into a programming language and under tests and version control. Time to drop a bit of a bombshell: the source code is in Barbara too, not on disk. Remain composed. It's kept in a special Barbara ring called sourcecode. Interesting table structures, like Pandas, but closer to a DB (MnTable) Over time the divergence between Bank Python and Open Source Python grows. Technology churns on both sides, much faster outside than in of course, but they do not get closer. Minerva has its own IDE - no other IDEs work if you keep your source files in a giant global database. What I can't understand is why it contains its own web framework. Investment banks have a one-way approach to open source software: (some of) it can come in, but none of it can go out BTW, I “read” this with naturalreaders app Chris #3: Pyxel Pyxel is a ‘retro gaming console' written in Python! This might seem old and un-shiny, but the restrictions imposed by the environment gift simplicity Vastly decreased learning time and effort compared to something like Unity or even Pygame Straight forward simple commands, just like it was for micro-computers in the 80s cls(), line(), rect(), circ() etc. Pyxel is somewhat more Python and less console than others like PICO-8 or TIC-80 but this is a feature! Use your regular development environment to build. Brian #4: How to Ditch Codecov for Python Projects Hynek Schlawack Codecov is a third party service that checks your coverage output and fails a build if coverage dropped. It's not without issues. Hynek is using coverage.py --fail-under flag in place of this in GitHub actions. It's not as simple as just adding a flag if you are using --parallel to combine coverage for multiple test runs into one report. Hynek is utilizing the coverage output as an artifact for each test, then pulling them all together in a coverage stage combine and check coverage. He provides the snippet of GH Action, and even links to a working workflow file using this process. Nice! Michael #5: tiptop (like glances) via Zach Villers tiptop is a command-line system monitoring tool in the spirit of top. It displays various interesting system stats, graphs it, and works on all operating systems. Really nice visualization for your servers Good candidate for pipx install tiptop Chris #6: pyc64 A Commodore 64 emulator written in pure Python! Not 100% complete - screen drawing is PETSCII character mode only This still allows for a lot of interesting apps & exploration Actual machine emulation using py65 - a pure Python 6502 chip emulator! You can pop to a Python REPL from inside the emulator and examine data structures like memory, registers, etc! An incredible example of what Python is capable of 0.6 Mhz with CPython and over 2Mhz with pypy! Extras Michael: Michael's FlaskCon 2021 HTMX Talk Chris: Amazon OpsTech IT is hiring! (If deemed appropriate :) Joke: I hate how the screens get bright so early this time of year

Talk Python To Me - Python conversations for passionate developers
#342: Python in Architecture (as in actual buildings)

Talk Python To Me - Python conversations for passionate developers

Play Episode Listen Later Nov 23, 2021 61:28


At PyCon 2017, Jake Vanderplas gave a great keynote where he said, "Python is a mosaic." He described how Python is stronger and growing because it's being adopted and used by people with diverse technical backgrounds. In this episode, we're adding to that mosaic by diving into how Python is being used in the architecture, engineering, and construction industry. Our guest, Gui Talarico, has worked as an architect who help automate that world by bringing Python to solve problems others were just doing by point-and-click tooling. I think you'll enjoy this look into that world. We also touch on his project pyairtable near the end as well. Links from the show Pyninsula Python in Architecture Talk: youtube.com Using technology to scale building design processes at WeWork talk: youtube.com Revit software: autodesk.com Creating a command in pyRevit: notion.so IronPython: ironpython.net Python.NET: github.com revitpythonwrapper: readthedocs.io aec.works site: aec.works Speckle: speckle.systems Ladybug Tools: ladybug.tools Airtable: airtable.com PyAirtable: pyairtable.readthedocs.io PyAirtable ORM: pyairtable.readthedocs.io Revitron: github.com WeWork: wework.com Article: Using Airtable as a Content Backend: medium.com Python is a Mosaic Talk: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training

Interviews: Tech and Business
CIO Strategy: How to Lead Enterprise Data and Analytics?

Interviews: Tech and Business

Play Episode Listen Later Nov 22, 2021 43:25


Data and analytics should be a core competence for every Chief Information Officer and Information Technology organization. Given the business, technical, and cultural challenges of using data to make informed business decisions, it's no surprise that data science and analytics are hard.So, how do you make your enterprise data and analytics program a success? Bruno Aziza, head of data and analytics for Google Cloud, explains his approach with valuable lessons based on practical experience.The conversation includes these topics:-- CIO Strategy: How to Lead Enterprise Data and Analytics?-- How to choose a meaningful data problem?-- How important is data infrastructure?-- What is a data mesh?-- How can small organizations take advantage of data and analytics?-- Should the CIO own data and analytics?-- Chief financial officer as the data owner?-- Chief Digital Officer as the data owner?-- Value of low-code and no-code products to CIOs?Bruno Aziza is head of data and analytics for Google Cloud. He specializes in scaling businesses & turning them into global leaders. He helped launch Alpine Data Labs (bought by Tibco), AppStream (bought by Symantec), SiSense (bought Periscope Data) & AtScale. He was at Business Objects when they went IPO (after acquiring Acta & Crystal Reports, & before SAP bought them for $7B). He was at Microsoft when they turned the Data & Analytics business into a $1B giant. Bruno has written 2 books on Data Analytics and Enterprise Performance Management. His allegiance is to the Analytics Community worldwide.Subscribe to the CXOTalk newsletter: https://www.cxotalk.com/subscribeRead the complete transcript: https://www.cxotalk.com/episode/google-cloud-how-manage-enterprise-data-science

The Artists of Data Science
Data Science Happy Hour 59 | 19NOV2021

The Artists of Data Science

Play Episode Listen Later Nov 21, 2021 60:37


Watch the video of this episode: https://youtu.be/SOW9wUY3FpA Resources: https://medium.com/@grepdennis/how-a-sql-database-engine-works-c67364e5cdfd https://medium.com/building-the-metaverse/evolution-of-the-creator-economy-9e038e8411af https://medium.com/data-driven-fiction https://snap.stanford.edu/data/roadNet-CA.html https://theartistsofdatascience.fireside.fm/guests/anderson-silver https://theartistsofdatascience.fireside.fm/guests/donald-j-robertson https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309 https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507/ref=sr11?keywords=inspired&qid=1637361494&s=books&sr=1-1 https://www.amazon.com/The-Feed-Season-1/dp/B086HVT7JH https://www.hel.fi/uutiset/en/kaupunginkanslia/a-new-minecraft-city-model-introduces-helsinki-in-more-detail https://www.linkedin.com/in/dkjapan/ https://www.tigergraph.com/resources/ https://www.youtube.com/watch?v=YT0CScFzp1o Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

The Artists of Data Science
Turn Ideas into Gold | Steven Cardinale

The Artists of Data Science

Play Episode Listen Later Nov 19, 2021 71:07


Watch the video of this episode: https://youtu.be/cSOXStI5sjg Find Steven Cardinale online: https://twitter.com/scardinale https://www.linkedin.com/in/stevencardinale/ Memorable Quotes from the Episode: [00:53:20] "I was talking to somebody the other day and I said, What are you selling because I'm selling media coverage for football teams? I'm like, OK, great, you know, because all football teams need people to know where they're at. Nothing but what are you really selling? Well, I'm selling it to mostly the high school teams, and really what I'm selling is, you know that parents can see their kids and media coverage. Great. What are you selling? It took him a minute and goes, Well, I'm selling the fact that parents are spending money to be have their kids on a football team. They want to see their kids names in the newspaper. So now we're starting to understand something a little more interesting." [00:11:21] "...if you think about a data scientist, you guys are alchemists, people who work with, you know, the big data lakes and the uncertainty of data and then convert it into a decision that is the essence of alchemy." Highlights of the Show: [00:01:24] Guest Introduction. [00:04:43] Where you grew up and what it was like there? [00:04:41] How did you figure out who you want to be? [00:07:34] What are the two definitions of entrepreneurship as mentioned in your book? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Test & Code - Python Testing & Development
170: pytest for Data Science and Machine Learning - Prayson Daniel

Test & Code - Python Testing & Development

Play Episode Listen Later Nov 18, 2021 45:12


Prayson Daniel, a principle data scientist, discusses testing machine learning pipelines with pytest. Prayson is using pytest for some pretty cool stuff, including: * unit tests, of course * testing pipeline stages * counterfactual testing * performance testing All with pytest. So cool. Special Guest: Prayson Daniel.

CTStartup Podcast
Episode 141: Creating a data technology hub with the Stamford Data Science Initiative

CTStartup Podcast

Play Episode Listen Later Nov 17, 2021 31:14


Data is the future, and UConn is working with CTNext and StamfordNext to create a unique pilot program with a multi-component initiative around data science. Tune in to hear from Executive Director Dan Schwartz on why Stamford is poised to be the next big data startup hub, and how this program is laying the foundation for bigger things to come. This podcast sponsored by CTNext and Connecticut Innovations.

The Data Scientist Show
Develop product sense to uplevel your data science career, how to influence product managers with data, crack product sense interview questions with Peter Knudson - The Data Scientist Show #013

The Data Scientist Show

Play Episode Listen Later Nov 17, 2021 71:06


Peter Knudson is a product manager of 10 years who focuses on innovative new experiences that help drive engagement in the ever evolving landscape of mobile and console games. He is also the author of the Amazon best selling book “Product Sense.” We talk about what is product sense, how do data scientist develop product sense, what are product manager's frustration when working with data scientists, how can data scientists influence product managers better, misconceptions about product management, common mistakes in product management. Peter's best selling book “Product Sense”: https://www.amazon.com/Product-Sense-Problems-Interviews-Management-ebook/dp/B0998SRN37 Website: ProductSenseBook.com Linkedin: https://www.linkedin.com/in/thisispeterk/

Python Bytes
#259 That argument is a little late-bound

Python Bytes

Play Episode Listen Later Nov 17, 2021 47:24


Watch the live stream: Watch on YouTube About the show Sponsored by Shortcut - Get started at shortcut.com/pythonbytes Special guest: Renee Teate Michael #1: pypi-changes via Brian Skinn, created by Bernát Gábor Visually show you which dependencies in an environment are out of date. See the age of everything you depend upon. Also, shoutout again to pipdeptree Brian #2: Late-bound argument defaults for Python Default values for arguments to functions are evaluated at function definition time. If a value is a short expression that uses a variable, that variable is in the scope of the function definition. The expression cannot use other arguments. Example of what you cannot do: def foo(a, b = None, c = len(a)): ... There's a proposal by Chris Angelico to add a =: operator for late default evaluation. syntax still up in the air. => and ?= also discussed However, it's non-trivial to add syntax to an established language, and this article notes: At first blush, Angelico's idea to fix this "wart" in Python seems fairly straightforward, but the discussion has shown that there are multiple facets to consider. It is not quite as simple as "let's add a way to evaluate default arguments when the function is called"—likely how it was seen at the outset. That is often the case when looking at new features for an established language like Python; there is a huge body of code that needs to stay working, but there are also, sometimes conflicting, aspirations for features that could be added. It is a tricky balancing act. Renee #3: pandas.read_sql Since I wrote my book SQL for Data Scientists, I've gotten several questions about how I use SQL in my python scripts. It's really simple: You can save your SQL as a text file and then import the dataset into a pandas dataframe to do the rest of my data cleaning, feature engineering, etc. Pandas has a built-in way to use SQL as a data source. You set up a connection to your database using another package like SQL Alchemy, then send the SQL string and the connection to the pandas.read_sql function. It returns a dataframe with the results of your query. Michael #4: pyjion by Anthony Shaw Pyjion is a JIT for Python based upon CoreCLR Check out live.trypyjion.com *to see it in action.* Requires Python 3.10, .NET Core 6 Enable with just a couple of lines: >>> import pyjion >>> pyjion.enable() Brian #5: Tips for debugging with print() Adam Johnson 7 tips altogether, but I'll highlight a few I loved reading about Debug variables with f-strings and = print(f``"``{myvar=}``") Saves typing over print(f``"``myvar={myvar}") with the same result Make output “pop” with emoji (Brilliant!) print("

Talk Python To Me - Python conversations for passionate developers
#341: 25 Pandas Functions You Didn't Know Existed

Talk Python To Me - Python conversations for passionate developers

Play Episode Listen Later Nov 17, 2021 59:16


Do you do anything with Jupyter notebooks? If you do, there is a very good chance you're working with the pandas library. This is one of THE primary tools of anyone doing computational work or data exploration with Python. Yet, this library is massive and knowing the idiomatic way to use it can be hard to discover. That's why I've invited Bex Tuychiev to be our guest. He wrote an excellent article highlighting 25 idiomatic Pandas functions and properties we should all keep in our data toolkit. I'm sure there is something here for all of us to take away and use pandas that much better. Links from the show Bex Tuychiev: linkedin.com Bex's Medium profile: ibexorigin.medium.com Numpy 25 functions article: towardsdatascience.com missingno package: coderzcolumn.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training

Millennial Success Podcast
#35: What is Blockchain and Data Modeling REALLY with Sol Girourd

Millennial Success Podcast

Play Episode Listen Later Nov 17, 2021 78:34


Join Sammy Warrayat and Sol Girouard as they discuss blockchain and data modeling. Sol is a mathematical economist, data scientist, quant, and the CEO and founder of Data Innovations Lab. In this episode, she sheds light on what blockchain really is and everything they do related to machine learning.What Really Is Blockchain?Almost everyone equates blockchain to cryptocurrency, but Sol says otherwise. Blockchain is the base for everything known as distributed ledger technology (DLT) or decentralized ledger. Cryptocurrency is just one of the functions that can be given to a blockchain. Sol says that in a company, it is very smart to have a blockchain to work on a clear function or to add multiple functions per ledger because, in that way, it is clean. A lot of good usable data can also be taken from it without doing extra data processing. There are private and public chains, and whatever happens on one side must also happen to the other side, just like an accounting ledger. It also has a consensus that nodes are peers or blocks, so they have to say and approve the identity of whoever does the transaction, which makes excellent use of blockchain for identity protection. The level of security differs between private and public chains, but both have benefits and uses depending on the functions. Domain KnowledgeBlockchain is only one component, but not all data go into the chain; there have to be other data sources. A data scientist cannot work on every data set because they are not knowledgeable about it, so Sol highlights that domain knowledge is necessary. Data science is a field of intersection and being proficient in coding algorithms, deep learning, and knowing statistics is not enough. A data scientist should also have expertise aside from data science itself as it solves clear points. All of this comes to play. If the data scientist does not know where the questions are assessed from, they cannot perform the models properly nor acquire the skills needed to make them intersect. Having no knowledge from a field the data is extracted from results in wrong insights. Thus, an area of concentration is required even if one can handle large data very well. About Sol Girouard:Sol graduated top of her class from Harvard University and holds Academic Teaching Fellow positions for Data Science courses at Harvard's Engineering School—SEAS, Institute of Applied Computer Science, and Harvard Extension School. She has judged the University of Chicago Financial Mathematics Master Program Graduating Competition.Sol is the CEO and founder of Data Innovation Labs, a full-service data science and decision intelligence consulting group deploying 4IR technologies and implementing digital transformation across business verticals. She is also Blockchain APAC venture Laclary's founding partner and holds Data Science Advisory positions in international financial and 4IR firms. Sol was the chief economist and head of Fundamental and Quantitative Research at Trevinci Capital Partners—liquid Ag Hedge Fund. She was the managing partner and head quant for Oracle Management—a proprietary global macro trading firm. Outline of the Episode:[0:02:45] Sol's introduction about herself and her journey[0:06:51] What was it like going to Harvard and the issue of underrepresentation[0:13:33] An ecosystem of companies built around machine learning and blockchain[0:16:43] Distributed ledgers, decentralized banking, blockchain, and cryptocurrency[0:22:40] Latency benefit that private chains have over public chains[0:23:53] How blockchain nodes make life easy for data scientists[0:28:35] The program that allows for continuous machine learning[0:37:09] Sponsor advertisements[0:39:34] Why should you care about artificial intelligence?[0:42:26] The silver lining in Sol's hearing disability&

intuitions behind Data Science
Ground Truths in Data Science

intuitions behind Data Science

Play Episode Listen Later Nov 16, 2021


What is a population and what is a sample? What exactly do we want to do with them?

Expert Dojo
E225 How SynctacticAI is becoming today's Hybrid Multi-Cloud Data Science Platform

Expert Dojo "The Art of Startup War"

Play Episode Listen Later Nov 16, 2021 38:39


This week Brian speaks with Chethan Athreyas, CEO of SynctacticAI SynctacticAI is a platform to power the future of Artificial Intelligence and Machine Learning. With the proliferation in Data Science and its fraternity, the platform aims at providing a complete automated Data engineering and DevOps suit. SynctacticAI provides an end to end platform where anyone can come connect their data sources, define workflows, visualize insights and build ML/AI models. Teams can collaborate with each other while working on a single source of truth for all their data needs. The flexibility of Hybrid Multicloud provides data teams the power to optimize their workflows and bring in their data or store their data anywhere they please, right from On-Premise deployments to any of the cloud providers. For more information, visit https://synctactic.ai/ If you have the next big idea, apply to the Expert Dojo Accelerator: www.expertdojo.com

Innovating with Scott Amyx
Interview with Alberto Rizzoli Co-Founder of V7

Innovating with Scott Amyx

Play Episode Listen Later Nov 16, 2021 28:17


V7 is a platform for deep learning teams to manage training data workflows and create image recognition AI.

The Douglas Coleman Show
The Douglas Coleman Show w_ Zaira Prizada and Edward Willett

The Douglas Coleman Show

Play Episode Listen Later Nov 15, 2021 39:05


Zaira Pirzada is a multi-lingual poet, an artist, a technologist, and an academic. Her art is inspired by her wide range of professional roles and the double-conscious experience of being a Indian-Pakistani-American woman. Zaira is a principal advisor at one of the world's leading information technology research and advisory companies. Additionally, Zaira is a board member of Women at Gartner. She holds an M.A. in International Affairs focused on security, intelligence, and crisis communications; and is in the midst of furthering her education by pursuing an M.S. Eng in Data Science and Security Informatics from Johns Hopkins. Zaira has worked at leading think tanks and appeared in international media for her expertise in intelligence gathering. She is also a Meisner-trained actress from the William Esper Studio and counts acting and spoken word among her greatest passions.https://lucidhousepublishing.com/authors/zaira-pirzada/Edward Willett is the award-winning author of more than 25 nonfiction books for both adults and children on topics ranging from health and science to history, computers, authors and rock stars. He also writes science fiction and fantasy for both adults and young adults. http://edwardwillett.comThe Douglas Coleman Show now offers audio and video promotional packages for music artists as well as video promotional packages for authors. We also offer advertising. Please see our website for complete details. http://douglascolemanshow.comIf you have a comment about this episode or any other, please click the link below.https://ratethispodcast.com/douglascolemanshow

Tech Unlocked
EP 47| How to launch your data science career with Marizza Delgado

Tech Unlocked

Play Episode Listen Later Nov 15, 2021 33:06


Data science is one of the fastest-growing careers in the tech industry. According to Linkedin, there is a 650% job growth in data science since 2012 and the U.S. Bureau of Labor Statistics estimated 11.5 million new data science jobs will be created by 2026. You don't need to be a math expert, have a master's degree or Ph.D. to become a data scientist. Today on the show, Grace chats with Marizza Delgado a Data Scientist on Etsy's Product Analytics about how you can launch your data science career and the skills needed to thrive in this career. Marizza Delgado is a Data Scientist on Etsy's Product Analytics team, fashion model represented in NY and SF, and Miss New York Earth USA 2021. As an advocate for Women in STEM, she uses social media to increase the visibility of women in stem role models and expose young girls to the opportunities that come with working in technology, such as financial responsibility, work/life balance, continuous learning, and how empowering it is to work in a male-dominated field.     Key takeaways: Three different types of data scientists that exists in this career field What happens in the data science interview process Technical skills needed to become a data scientist A day in the life of a Data Scientist How to handle post-grad anxiety & rejection The power of community   Resources: Google Data Analytics Professional Certificate on Coursera Towards Data Science Podcast and Medium Blog, Algorithms of Oppression Book by Safiya Noble Follow Tech Unlocked for career tips: Website Substack Twitter Instagram   Connect with Grace: Twitter LinkedIn   Connect with Marizza: https://www.instagram.com/marizzadelgado/ https://www.instagram.com/missnewyorkearth/ https://www.linkedin.com/in/marizza-delgado/   Enjoyed listening to this episode? Please leave a review on iTunes and Spotify. Questions about sponsorship? Email us techunlockedpod@gmail.com  

Tcast
Helping Enterprises Ethically Establish Relationships with End Customers: TARTLE Welcomes New Chief Revenue Officer Martin Herrick to Champion this Process

Tcast

Play Episode Listen Later Nov 15, 2021 32:50


TARTLE is adding to its leadership team with Martin Herrick as Chief Revenue Officer.  Martin has a wealth of experience in education and finance, having worked in higher education for seven years before transitioning to the private education lending space.  He has held roles as Chief Revenue Officer in the fintech space, working on a new financial asset class called Income Share Agreements (ISAs). Prior to this, Martin has made an impact in other C-suite and executive positions. He worked as the Vice President for Business Development, and eventually the Senior Vice President, for Education Loan Source in the Greater San Diego Area from 2014 to 2019. Martin's work with TARTLE brings him closer to individuals who are struggling to gain access to higher education. “The users who will be using TARTLE to fill out education packets are probably in the blind spot of most colleges and universities, who don't have a direct line of access to these students,” he explained, “They're not going in to talk to their college counselors, filling out surveys, or completing their FAFSAs. They're searching for platforms like TARTLE to find revenue that they can make for their family, to help with the tough times that they're going through right now.” “I'm looking to prove to the higher education administrators that we go out to, in the colleges that we partner with to buy data off of our platform, that these are students they would have otherwise missed out on had they not taken a look at TARTLE to see who's interacting with our platform.” Martin's unique skill set and experience in education, finance, and technology will be instrumental in the TARTLE platform's evolution into a marketplace that is better equipped to cater to its Big 7, particularly in initiatives related to educational access. This episode also deep-dives into: How the rising cost of education affects our capacity for human understanding The impact of COVID-19 on how people perceive higher education and student loans Discussing how student loans are mostly accessible to those who are already backed with generational wealth TARTLE's capacity to reinvent higher education Illustrating the difficulties people may run into when trying to pay for higher education Finding clear and data-driven solutions to making higher education more accessible Emphasizing the urgency of finding tangible methods to affordable higher education For podcast, radio, TV interviews, media opportunities, and other press-related inquiries, please contact Media Director Shiela Pialago at sp@tartle.co   Tcast is brought to you by TARTLE. A global personal data marketplace that allows users to sell their personal information anonymously when they want to, while allowing buyers to access clean ready to analyze data sets on digital identities from all across the globe.   The show is hosted by Co-Founder and Source Data Pioneer Alexander McCaig and Head of Conscious Marketing Jason Rigby.   What's your data worth?   Find out at: https://tartle.co/   YouTube: https://www.youtube.com/c/TARTLE   Facebook: https://www.facebook.com/TARTLEofficial/   Instagram: https://www.instagram.com/tartle_official/   Twitter: https://twitter.com/TARTLEofficial   Spread the word!

Data And Analytics in Business
E81 - Max Métral - How Data Drives Racing - The Data Science Behind the Sports Industry

Data And Analytics in Business

Play Episode Listen Later Nov 14, 2021 65:03


With over half a billion fans following its events worldwide, Formula 1 is the greatest racing spectacle in the world. But very few of us know about Formula 1 as a corporate entity. What makes F1 such a highly successful organisation with raving fans? Max Métral, the Senior Analytics Manager at Formula 1®, is here to give us an insider view into the inner workings of data and analytics in the corporate sports entertainment media landscape. At F1, Max is currently involved in leading the business analytics department, which works to maximize commercial opportunities of both B2B and B2C ventures of F1 by optimising decision making using data analytics. Max uses data to deepen the customers/consumers/fans knowledge and make better data-informed decisions. Before becoming the Senior Analytics Manager, Max was the Insight Manager. As the Insights Manager, he developed the organisation's first-ever fan data analytics strategy. He also built up the F1 data team from scratch. Max also enjoys teaching and sharing his real-life experience as a visiting lecturer at various universities across London, Paris, and Brussels. Previously, Max has worked as a data and insights analyst at City Football Group, Accenture Uk, and Adidas. Academically, Max studied at the University of North Carolina at Chapel Hill - Kenan-Flagler Business School and has a Master of Science in Management (MSc) degree from ESSEC Business School in France. He frequently speaks at reputed industry events involving data analytics, sports business analytics, and sports marketing. In this exclusive episode, Max shares how data and analytics are used in the landscape of sports media and sports analytics. The interview kicks off with Max sharing his passion and experience in teaching and writing, when he is not working. Not only does he find joy from these activities, but equally, he uses the opportunity to sharpen his knowledge too. Max provides a lot of business context for Formula 1 and how it's evolving from B2B to B2C and the DTC model. How data analytics are becoming ever more critical in their B2B business because broadcasters and promoters are asking more questions than ever before to justify the ROI. How Max and his team had to create the fans database from scratch when he first started the job at Formula One. How, eventually, the fans database has allowed Formula One to start building its B2C business. Why data analytics is becoming more critical for Above the Line Marketing. Why we can't blindly trust the data and spend all our marketing budget for Below the Line Marketing. How to build a customer database with a global perspective and some of the challenges it would come with. How to create a new B2C market if you're traditionally a B2B business and why data plays such an important role to make both of them complement each other. If you are Chief Marketing Officer in a large corporation or work in the media industry, listening to this episode is highly recommended. This episode is sponsored by the new program at DDA. It's an analytics leader mentorship program for senior managers and executives in the business team who want to develop a data-driven business to drive customer experience excellence. For a small one-off annual fee, you get to book Unlimited Strategy Sessions for a Full Year. For more information about this program, please reach out to DDA! BusinessAnalytics, CustomerExperience, DataScience --- Send in a voice message: https://anchor.fm/analyticsshow/message

The Artists of Data Science
Data Science Happy Hour 58 | 12NOV2021

The Artists of Data Science

Play Episode Listen Later Nov 14, 2021 66:38


Watch the video of this episode: https://youtu.be/IRkGuRMnZ6o Resources: https://fossa.com/blog/analyzing-legal-implications-github-copilot/ https://github.com/jupyter-naas/awesome-notebooks https://hbr.org/2009/01/picking-the-right-transition-strategy https://papermill.readthedocs.io/en/latest/ Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Enterprise Podcast Network – EPN
Brandon Taubman on the Importance of Data Science, from Baseball Recruiting to Real Estate Investing

Enterprise Podcast Network – EPN

Play Episode Listen Later Nov 13, 2021 15:08


Brandon Taubman, the Chief Information Officer, and experienced data scientist at Stablewood Properties who is known for his time as Assistant General Manager with the Houston Astros, where he combined qualitative analysis with cutting-edge technology and data science to transform the team's recruiting strategy joins Enterprise Radio. The post Brandon Taubman on the Importance of Data Science, from Baseball Recruiting to Real Estate Investing appeared first on Enterprise Podcast Network - EPN.

The Artists of Data Science
Turn the Lights on Data | George Firican

The Artists of Data Science

Play Episode Listen Later Nov 12, 2021 62:41


Watch the video of this episode: https://youtu.be/6UJED0scgy4 Find George Firican online: https://twitter.com/georgefirican https://www.linkedin.com/in/georgefirican Memorable Quotes from the Episode: [00:42:51] "So I think everything needs to start on the business side first, so ideally, that's very clear for everybody where the business with a five year plan, if you will, for the business is so that anything else is a strategy to support that plan, right? Otherwise, it's kind of just wishful thinking. If if you want to go to Mars from a Data perspective, how can you create models for the company to be able to do that? But then if the company doesn't want to get there, then it's pointless. So that's why it's you need a business to take that first step." Highlights of the Show: [00:01:29] Guest Introduction. [00:02:53] Where you grew up and what it was like there? [00:04:02] What did you think your future was going to look like at the age of 15? [00:08:2] What was the nudge that got you into Data? What was the experience that you had that made you realize that Data was right for you as a great teacher? [00:09:45] As data scientist, machine learning practitioners, we're end users of the data, right? [00:12:22] What the heck is Data governance? [00:14:26] Responsibilities of a data analyst. [00:15:47-00:15:50] Can anybody be a data steward? What does a data steward mean? [00:19:33] Metadata, master data, what are those? What do they have to do with data governance? [00:22:19] Why should Data scientists care about these types of data? [00:23:48] Discuss data governance in action in the workplace. [00:27:28] When you say business driver, what does that mean? [00:29:1] So what is the goal of the organization at a high level? [00:30:02] What are your concerns around data governance or is there kind of a a business way to ask the question so that we can translate it into our own lingo? [00:31:06] Why is it so painful to get to have the report or access them from a dashboard in a timely fashion? [00:33:14] What would be the types of individuals that we would want to see on the council? [00:35:11] What are the biggest challenges you foresee her facing when he's starting out a Data strategy at this massive organization? [00:37:05] What can Stephen King teach us about Data governance? [00:38:41] What are Data Management and other such principles? How do we identify these principles? [00:41:18] What does Data strategy have to do with helping us get ahead in our Data careers? [00:42:24] How can we help our organizations define a data strategy if we find ourselves in this position of having to to build a Data strategy? [00:43:30] Are there any blueprints that exist to help create a Data strategy? [00:44:24] What the heck are the maturity models like? [00:45:48] Can we have the George tech and maturity model? Does that exist? [00:50:37] What is the difference between data scientists and data analysts? [00:53:33] Does data governance care about unstructured data or is it only about structured data; how's that? [00:54:32] It's 100 years in the future. What do you want to be remembered for? [00:54:59] When do you think the first video to hit one billion views on YouTube will happen, and what will it be about? [00:55:55] What do most people think within the first few seconds of meeting you for the first time? [00:56:46] What are you currently reading? [00:56:46] What are you currently reading? [00:58:13] Pet peeves? [00:58:44] What's on your bucket list this year? [01:00:35] Do you ever sing when you're alone? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

The Next CMO
Dealing with Marketing Data without a Data Science Team with John Wall from Trust Insights

The Next CMO

Play Episode Listen Later Nov 11, 2021 33:39


In this episode of The Next CMO podcast, we speak to John Wall, partner and head of business development at Trust Insights, a marketing data consultancy helping organizations who don't have their own data science team with all things marketing data.He is also the producer of Marketing Over Coffee, a weekly audio program that discusses marketing and technology with his co-host Christopher S. Penn, and has been featured on iTunes. Notable guests include Chris Brogan, David Meerman Scott, Simon Sinek and Seth Godin. More info about John hereMore info about Trust Insights hereMore info about Marketing Over Coffee hereMore info about Plannuh hereMore info about The Next CMO podcast here  Produced by PodForte

Women in Data Science
Allison Koenecke | Researching algorithmic fairness and causal inference in public health

Women in Data Science

Play Episode Listen Later Nov 11, 2021 27:31


Allison Koenecke, who received her PhD from Stanford's Institute for Computational and Mathematical Engineering (ICME), describes how her experiences in academia and industry shaped her decision to return to academia. Currently a postdoc at Microsoft Research in the Machine Learning and Statistics group, she starts as an Assistant Professor of Information Science at Cornell University next year. Her research interests lie at the intersection of economics and computer science, with projects focusing on fairness in algorithmic systems and causal inference in public health.​Allison says in her career so far, she has always tried to keep as many doors open as possible but recognized, at some point, you have to start closing doors and specialize. After getting her bachelor's degree in mathematics from MIT, she worked in economic consulting for a few years and realized she wanted to do something with more social benefit. While she was working in industry and during summer internships, she kept in touch with professors and kept up with her research so she could have that option open if she wanted to go back to school. One of the main reasons she chose to stay in academia was industry and government did not offer what she was looking for. For example, if you stay in industry long-term and your research is critiquing big tech companies, you may run into roadblocks or not be seen as a neutral third-party observer, as you would be seen in academia. Or at a government think tank, your work wouldn't necessarily have as much reach as in academia. But even more, a lot of the reason she stayed in academia was the people. Allison's research is interdisciplinary and falls into two categories. The first is a fairness in online services and algorithmic services, such as speech-to-text or online ads and looking at the racial disparities in those services. And the second branch is on causal inference, which is usually applied to things like public health. Most of her thesis focuses on fairness with the services that we use every day.One of her research projects is about Google ads used to enroll people in food stamps and how to make decisions about fairness when it costs more to show those ads to Spanish speakers versus English speakers. She is also doing fairness research on racial disparities on speech-to-text systems developed by large tech companies to ensure systems are usable for African American populations that may not able to use their tools simply because they speak with a different variety of English than standard English. She says you need to have people thinking about fairness problems at all steps of the pipeline before you build a product that might harm certain groups of people. She's hoping to bring awareness to different blind spots to make sure technology actually works for everyone.RELATED LINKSConnect with Allison on LinkedIN and TwitterFind out more about the Microsoft Research Machine Learning and Statistics groupFind out more about Cornell University Information ScienceConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Python Bytes
#258 Python built us an anime dog!

Python Bytes

Play Episode Listen Later Nov 11, 2021 43:09


Watch the live stream: Watch on YouTube About the show Sponsored by Shortcut - Get started at shortcut.com/pythonbytes Special guest: Karen Dalton Brian #1: stale : github bot to “Close Stale Issues and PRs” Was one response to a question by Will McGugan Something like “An issue filed on an open source project, I've asked a followup question about the issue, and filer doesn't respond. Is there an easy way to close the issue after a set time period of inactivity.” Just trying to get a reference to Will out of the way early in the episode. stale does this: Warns and then closes issues and PRs that have had no activity for a specified amount of time. The configuration must be on the default branch and the default values will: Add a label "Stale" on issues and pull requests after 60 days of inactivity and comment on them Close the stale issues and pull requests after 7 days of inactivity If an update/comment occur on stale issues or pull requests, the stale label will be removed and the timer will restart If defaults seem too short or harsh, everything is configurable Michael #2: jut - JUpyter notebook Terminal viewer via kidpixo The command line tool view the IPython/Jupyter notebook in the terminal. Even works against remote ipynb files (via http) Karen #3: JupyterLyte via Marcel Milcent @MarcelMilcent JupyterLite is a JupyterLab distribution that runs entirely in the browser and is interactive Built from using JupyterLab components and extensions Being developed by core Jupyter developers, but the project is still unofficial Example: https://jupyterlite.readthedocs.io/en/latest/_static/lab/index.html Offers JupyterLab or RetroLab (a.k.a JupyterLab Classic) look No application server required, cacheable Try "import this"! Brian #4: Feature comparison of ack, ag, git-grep, GNU grep and ripgrep ack now, supplies are limited! Tangent for those unfamiliar with grep grep is an essential tool for many developers that prints lines that match a pattern grep foo *.py - list all lines containing “foo” in this directory grep -l foo **/*.py | grep -v venv **``*/**``.py Recursively find all Python files this directory and all subdirectories -l Print just the name of the file if it contains a “foo” in it. | grep -v venv Exclude virtual environments, because there's a lot of “foo” in there. (There's gotta be a better way to do this, someone suggest a better way, please). Article compares ack, ag “The silver Searcher”, git-grep, grep, and rg “ripgrep” Language, Licence, and regex versions Features like parallelism, config, etc. Fine grain feature comparisons searching capability regular expression style search output file presentation file finding inclusion, exclusion file type specification random other features This is on the ack website, and kinda makes my want to try ripgrep. Michael #5: Python Client for Airtable: pyairtable by Gui Talarico What is Airtable? Hmm kind of like: Excel Trello boards CI Pipelines A big player on nocode/lowcode community Check out the quickstart to see how it works. Karen #6: Black can now format notebooks via Marco Gorelli gh: MarcoGorelli (creator of nbQA [isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks]) pip install black[jupyter] black mynotebook.ipynb “…it should be significantly more robust than the current third-party tools” Extras Michael Trying a new password manager (sorta): Bitwarden The PSF is looking for an Executive Director Want a person in anime form? Python 3.11.0a2 is out (via PyCoders) Karen Volunteer in your local Python community (or volunteer to speak) Joke:

Hush Loudly
iTopia, the Employee Resource Group (ERG) of a data science company, raises awareness about introverts, and is making an impact

Hush Loudly

Play Episode Listen Later Nov 11, 2021


HushLoudly’s Jeri Bingham speaks with iTopia Founder Ryan Showalter and co-founder Meagan Connley regarding their Employee Resource Group at 8451, a data science, insights and media company. Hear about how Ryan came up with the idea, and founded this group. Together with Meagan and others, they are educating, making inroads, and gaining understanding for introverts. […]

The Logistics of Logistics Podcast
Supply Chain: Cash or Trash with Seth Page

The Logistics of Logistics Podcast

Play Episode Listen Later Nov 10, 2021 71:27


Supply Chain: Cash or Trash with Seth Page Seth Page and Joe Lynch discuss supply chain: cash or trash. Seth is the COO of TroughPut.ai, an artificial intelligence (AI) supply chain pioneer that enables companies to detect, prioritize and alleviate dynamic operational bottlenecks in real-time. Webinar - Demand Planning in VUCA Times with Ali Raza About Seth Page Seth Page is a senior technology executive, 8x entrepreneur, operator and cross-border deal-making expert who seamlessly bridges the worlds of technology, operations and finance. An expert in equity investments and scaling start-ups to venture-capital backed high-growth companies and into successful exits, divestitures, and IPO trajectories. Deep, hands-on technology roots underpin over two decades of business development, operations and venture activity. Tech pioneer and founder providing deal flow origination for angels, venture capital firms, corporations and family offices in diverse yet interconnected areas including Industrial AI, IOT, Artificial Intelligence, Machine Learning, Data Science, Operations Technology, Enterprise, Telecommunications,  Security & Access Control. He has founded, funded, scaled and exited multiple start-ups for investors, including: ThroughPut.ai; DataRPM (acquired: Progress); UniKey; PV Media Group (acquired: RhythmOne / Blinkx); RayV (acquired: Yahoo); Oyster Optics (acquired: Techquity); AdOnNetwork (acquired: PVMG); Trade.com (acquired: ABM AMRO); as well as deals including Xoom.com (IPO & acquired: NBC); LendingTree (IPO & acquired: IAC); Fetchback (acquired: eBay / GSI); Samsung (acquired: mSpot); xanox (joint acquisition by Axel Springer and PubliGroupe); Litronic (acquired Pulsar & IPO), and many other transactions. Seth earned an Executive MBA with honors in International Business from the Thunderbird School of Global Management, as well as a BS in Economics and a BS in German Linguistics & Literature, both from the University of California, Irvine, as well as a scholarship to study Volkswirtschaft and Germanistik at the Georg-August-Universität in Göttingen, Germany. About ThroughPut Inc ThroughPut.ai is a Silicon Valley-based Supply Chain AI leader that puts Industrial Material Flow on Autopilot by leveraging existing Enterprise Data to achieve superior Business, Operations, Financial and Sustainability Results. ThroughPut's AI-powered Supply Chain software predicts Demand, reorients Production Capacity, reassigns Warehouse Space, and reorders Materials optimally, so businesses minimize overpromising and under-delivering. By way of ThroughPut's Supply Chain AI Orchestration software that sits on top of existing data architectures, ThroughPut improves material flow and free-cash-flow across the entire end-to-end value chain more than 5-times faster than leading contemporary solutions. The founding team is led by seasoned serial entrepreneurs with real-world AI, Supply Chain, Manufacturing, Transportation and Operational experience, from the shop-floor to the top-floor, at leading Fortune 500 Industrial Companies & pioneering Enterprise Technology companies. Key Takeaways: Supply Chain: Cash or Trash Seth Page is the COO of ThroughPut.ai, an artificial intelligence (AI) supply chain pioneer that enables companies to detect, prioritize and alleviate dynamic operational bottlenecks in real-time. In the podcast interview, Joe and Seth discuss the enormous waste in supply chains. While supply chains create all the wonderful goods and services we enjoy, they also produce a lot of waste. Approximately one-third of supply chain output is waste – it adds no value for anyone. The waste is horrible for bottom lines and the environment. According to Boston Consulting Group's recent report, 80% of greenhouse gases are created by supply chains so to improve sustainability and profitability, companies must address the waste in the supply chain. The waste occurs because supply chain data is in separate silos and decisions are made to optimize locally – not globally. In other words, each player in the supply chain makes a rational decision based on the information that they have. While that decision might be good for their organization, it might be a bad for the end-to-end supply chain. Supply chain practitioners make decisions using faulty forecasts, old assumptions, and outdated tools. ThroughPut provides an integrated view of company-wide operations by pulling data from all of your disparate systems. Throughput can identify and manage constraints to free cash flow, while meeting revenue targets with output. To make better decisions, supply chain practitioners need demand sensing with real-time intelligence that can be used to create better demand forecasts. With demand sensing, companies can easily predict near-future demand patterns to streamline the flow of materials, processes, output, and free cash flow across your integrated supply chain. Seth and the team at ThroughPut unlock operations agility and efficiency, to meet unpredictable customer demands, while creating uninterrupted flow of materials through supply chain networks. This approach minimizes waste and maximizes profitability. Learn More About Supply Chain: Cash or Trash Seth Page LinkedIn Throughput.ai The New Retail Paradigm with Ali Raza Putting Supply Chains on Autopilot with Ali Raza Webinar - Demand Planning in VUCA Times with Ali Raza The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

Ken's Nearest Neighbors
Can You Learn Data Science From a Book? (Tyler Richards) - KNN Ep. 73

Ken's Nearest Neighbors

Play Episode Listen Later Nov 10, 2021 56:06


We are actually giving away 4 copies of Tyler's book! Comment on the YouTube video with why you want to learn streamlit for a chance to win one of the copies! You can also win by commenting on the twitter post, my instagram post, or my linkedin post related to this podcast! Tyler is a data scientist at Facebook who recently published a book on the Python library Streamlit called 'Getting Started with Streamlit for Data Science'. He graduated from the University of Florida in 2018, and worked on election integrity problems for nonprofits and research labs while there.

Talk Python To Me - Python conversations for passionate developers
#340: Time to JIT your Python with Pyjion?

Talk Python To Me - Python conversations for passionate developers

Play Episode Listen Later Nov 10, 2021 73:38


Is Python slow? We touched on that question with Guido and Mark last episode. This time we welcome back friend of the show, Anthony Shaw. Here's there to share the massive amount of work he's been doing to answer that question and speed things up where they answer is yes. He's just released version 1.0 of the Pyjion project. Pyjion is a drop-in JIT compiler for Python 3.10. Pyjion uses the power of the .NET 6 cross-platform JIT compiler to optimize Python code on the fly, with NO changes to your source code required. It runs on Linux, macOS, and Windows, x64 and ARM64. Links from the show Anthony on Twitter: @anthonypjshaw Pyjion: github.com Restarting Pyjion Presentation: youtube.com Hathi: SQL host scanner and dictionary attack tool: github.com Try Pyjion online: trypyjion.com Pyjion optimizations: readthedocs.io Pyjion docs: readthedocs.io .NET: dotnet.microsoft.com PEP 523: python.org Pydantic validation decorator: helpmanual.io Tortoise ORM: github.com pypy: pypy.org Numba: numba.pydata.org NGen AOT Compiler: microsoft.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training

Hipsters Ponto Tech
Pesquisa Operacional e Otimização – Hipsters Ponto Tech #278

Hipsters Ponto Tech

Play Episode Listen Later Nov 9, 2021 45:37


Você sabe o que é pesquisa operacional e quais as principais ferramentas utilizadas nesse processo? No episódio de hoje do Hipsters Ponto Tech vamos conhecer mais sobre pesquisa operacional e otimização atreladas à ciência de dados e saber como elas podem ser aplicadas nas rotinas de trabalho. Participantes: Paulo Silveira, o host que ficou arrepiado com o episódio de hojeLuiza Biasoto, Cientista de Dados na SuzanoCristina Ota, Cientista de Dados na SuzanoHelton Polli, Cientista de Dados no Grupo BoticárioKarina Oliveira, Cientista de Dados no Grupo BoticárioGuilherme Silveira, lider da escola de Data Science da Alura Links: Conheça o Alura CasesInscreva-se no YouTube da AluraInscreva-se na newsletter Imersão, Aprendizagem e Tecnologia Produção e conteúdo: Alura Cursos de Tecnologia - https://www.alura.com.br === Caelum Escola de Tecnologia - https://www.caelum.com.br/ Edição e sonorização: Radiofobia Podcast e Multimídia

The John Batchelor Show
1795: 2/2: Data Science Is the Third Wave of Economics since 1776. Callum Williams @TheEconomist

The John Batchelor Show

Play Episode Listen Later Nov 9, 2021 10:00


2/2: Data Science Is the Third Wave of Economics since 1776.  Callum Williams @TheEconomist https://www.economist.com/briefing/2021/10/23/enter-third-wave-economics

The John Batchelor Show
1795: 1/2: Data Science Is the Third Wave of Economics since 1776. Callum Williams @TheEconomist

The John Batchelor Show

Play Episode Listen Later Nov 9, 2021 13:40


1/2: Data Science Is the Third Wave of Economics since 1776.  Callum Williams @TheEconomist https://www.economist.com/briefing/2021/10/23/enter-third-wave-economics

Human Capital Innovations (HCI) Podcast
S27E15 - Helping Employers Attract and Retain STEM+ Women, with D Sangeeta

Human Capital Innovations (HCI) Podcast

Play Episode Listen Later Nov 8, 2021 28:32


In this HCI Podcast episode, Dr. Jonathan H. Westover (https://www.linkedin.com/in/jonathanhwestover/) talks with D Sangeeta about helping employers attract and retain STEM+ women. See the video here: https://youtu.be/9PenzRjaayI.  At 29, Sangeeta (https://www.linkedin.com/in/dsangeeta/) was at the end of her rope, about to quit a STEM career she loved because she couldn't navigate the roadblocks that were being thrown in front of her — as a woman, as an immigrant, as a person of color. Luckily, a mentor reached out, guided her, and she stayed in her career for another two decades, achieving 26 patents and leading global teams of 5,000+ with budgets of more than $200 million. She has mentored other STEM women, but could only help one woman at a time. That's why she is building a scalable, for-profit career advice platform so 30+ million STEM women around the world have access to the advice they need from top professionals to stick with their careers and thrive. Today, she can lead her global company because of the early leadership, business, and life lessons she learned - first as a scientist with GE; then as Director of Marketing and various managerial positions with GE Aviation; then as Global Head of Data Science and Chief Diversity Officer with Nielsen; then as VP of Connections with Amazon - all thanks to her sponsor who reached out in the very beginning.  Check out Dr. Westover's new book, 'Bluer than Indigo' Leadership, here: https://www.innovativehumancapital.com/bluerthanindigo.  Check out Dr. Westover's book, The Alchemy of Truly Remarkable Leadership, here: https://www.innovativehumancapital.com/leadershipalchemy.  Check out the latest issue of the Human Capital Leadership magazine, here: https://www.innovativehumancapital.com/hci-magazine.  Ranked #6 Performance Management Podcast: https://blog.feedspot.com/performance_management_podcasts/  Ranked #6 Workplace Podcast: https://blog.feedspot.com/workplace_podcasts/  Ranked #7 HR Podcast: https://blog.feedspot.com/hr_podcasts/  Ranked #12 Talent Management Podcast: https://blog.feedspot.com/talent_management_podcasts/  Ranked in the Top 20 Personal Development and Self-Improvement Podcasts: https://blog.feedspot.com/personal_development_podcasts/  Ranked in the Top 30 Leadership Podcasts: https://blog.feedspot.com/leadership_podcasts/ --- Support this podcast: https://anchor.fm/hcipodcast/support

Casual Inference
Hanging out in the data science trough of disillusionment with Hilary Parker | Season 3 Episode 5

Casual Inference

Play Episode Listen Later Nov 8, 2021 69:42


In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Hilary Parker about design thinking for data analysis, the Dunning-Kruger effect, and the potential data behind baby Yoda. Follow along on Twitter: Hilary: @hspter The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats

20 Minute Leaders
Ep628: Yorai Fainmesser | General Partner, Disruptive AI

20 Minute Leaders

Play Episode Listen Later Nov 7, 2021 26:40


Yorai is the co-founder and General Partner at Disruptive AI Fund, investing in early-stage startups with great entrepreneurs and remarkable AI. He also founded AI-SQUARE Forum Arab & Israeli AI community. In 2019 he retired as a Colonel from the IDF, as head of a Technological R&D unit (3060/8200) in the Intelligence Corps, responsible for developing modern Data Science systems and AI applications, acting as an exclusive center for Computer Vision and imaging technology, receiving the Israel Defence Prize Award. 

The Artists of Data Science
Data Science Happy Hour 57 | 05NOV2021

The Artists of Data Science

Play Episode Listen Later Nov 7, 2021 51:06


Watch the video of this episode: https://youtu.be/t4HevyAyMbo Resources: https://www.amazon.com/Superminds-Surprising-Computers-Thinking-Together/dp/0316349135 https://www.forbes.com/sites/bernardmarr/2021/10/27/glenfiddich-sells-18000-super-rare-whisky-as-nfts--heres-what-that-means/ Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Machine Learning Guide
MLA 018 Descript

Machine Learning Guide

Play Episode Listen Later Nov 7, 2021 6:22


(Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed. Descript The Ship It Podcast How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io Brandbeats Podcast by BASIC An agency podcast with views on design, technology, art, and culture. Explore the new microsite at www.brandbeats.basicagency.com

Machine Learning Guide
MLA 017 AWS Local Development

Machine Learning Guide

Play Episode Listen Later Nov 6, 2021 64:04


Show notes: ocdevel.com/mlg/mla-17 Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment, rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs (Lambda, SageMaker Studio, Cloud9 Connect to deployed infrastructure via Client VPN Terraform example YouTube tutorial Creating the keys LocalStack Infrastructure as Code Terraform CDK Serverless

Machine Learning Guide
MLA 016 SageMaker 2

Machine Learning Guide

Play Episode Listen Later Nov 5, 2021 59:43


Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker Jumpstart Deploy Pipelines Monitor Kubernetes Neo

The Artists of Data Science
The Industrial Philosopher | Cristina Digiacomo

The Artists of Data Science

Play Episode Listen Later Nov 5, 2021 63:14


Watch the video of this episode: https://youtu.be/Zm2wrWgKn_g Find Cristina Digiacomo online: https://www.linkedin.com/in/cristinadigiacomo Memorable Quotes from the Episode: [00:36:55-00:36:55] "... I know that's sort of like a pithy answer, but it's the truth. Our thoughts shape our reality. This is a very fundamental idea and concept from many, many, many, many philosophers across the millennia. We shape the circumstances in our lives just by the way that we look at them." [00:23:14-00:23:16] "Philosophy s not just about thinking, it's about acting and acting appropriately. And so all those four things, you know, the perception of the truth and the truth. Managing your thoughts being deliberate and acting accordingly. Wisdom is the word for all of that." Highlights of the Show: [00:01:12] Guest Introduction [00:02:54] Where did you grow up and what it was like there? [00:07:08] How did you get into the DJ world? [00:14:24] How did you get into into philosophy? [00:15:41-00:15:41] Why is it that philosophy and wisdom [they] get lumped into these categories of being like "Woo Woo" out there? Why do you think that is? [00:16:41] How do you define philosophy? [00:19:46-00:19:52] Speaking of being wise, what is what is the difference between being wise and acting wise? [00:24:23-00:24:25] How do we pause? How do we first of all, get to wisdom? How do we mitigate that knee jerk reaction? [00:26:26] Talk to us about clarity as discussed in your book. [00:28:52] Did you encounter any struggles when you're first trying to think in this way? I guess almost like metacognition, thinking about the way you're thinking and forcing yourself to answer these questions? Was that a bit of a challenge for you? And how did you overcome that? [00:34:12] What are your thoughts on constantly being in thought? [00:36:15] How can we help ourselves find out when we're having those detrimental thoughts and natural way back into something more productive, right? [00:38:57-00:38:58] In your book you're talking about how people get really attached to their thoughts and their ideas. How can we avoid that? [00:39:19] How do thought patterns affect our activities and what are some detriments of that? [00:46:03] What is the real flow and how can we distinguish that from a fake flow? [00:48:03] We talked about the importance of of inaction being just as important as as action. But if you were to just spelll it out clearly for us here, why is it that this inaction is just as important as as the action? [00:49:48] What has philosophy taught you about being a better strategist? [00:56:02] Is wisdom a trait that can be cultivated? [00:56:27] Where can we cultivate this act of being wise everywhere that we are? Do we do it alone by ourselves as we interact with other people? How can we can we do that? [00:57:20] What do you want to be remembered for? [00:58:05] What do you think the first video to hit one billion views on YouTube will be about? And when will that happen? [00:58:43] What do you think most people think within the first few seconds of meeting you? [00:59:46] What are you currently reading? [01:01:28] What languages do you speak? [01:01:38] What's the story behind one of your scars? [01:02:09] What's your favourite candy? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Talk Python To Me - Python conversations for passionate developers
#339: Making Python Faster with Guido and Mark

Talk Python To Me - Python conversations for passionate developers

Play Episode Listen Later Nov 4, 2021 61:02


There has a been a bunch of renewed interested in making Python faster. While for some of us, Python is already plenty fast. For others, such as those in data science, scientific computing, and even the large tech companies, making Python even a little faster would be a big deal. This episode is the first of several that dive into some of the active efforts to increase the speed of Python while maintaining compatibility with existing code and packages. Who better to help kick this off than Guido van Rossum and Mark Shannon? They both join us to share their project to make Python faster. I'm sure you'll love hearing what they are up to. Links from the show Guido van Rossum: @gvanrossum Mark Shannon: linkedin.com Faster Python Plan: github.com/faster-cpython The “Shannon Plan”: github.com/markshannon Sam Gross's nogil work: docs.google.com Watch this episode on YouTube: youtube.com ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training

Python Bytes
#257 Python Launcher - Launching Python Everywhere

Python Bytes

Play Episode Listen Later Nov 4, 2021 40:25


Watch the live stream: Watch on YouTube About the show Sponsored by Shortcut Special guest: Morleh So-kargbo Michael #1: Django 4.0 beta 1 released Django 4.0 beta 1 is now available. Django 4.0 has an abundance of new features The new *expressions positional argument of UniqueConstraint() enables creating functional unique constraints on expressions and database functions. The new scrypt password hasher is more secure and recommended over PBKDF2. The new django.core.cache.backends.redis.RedisCache cache backend provides built-in support for caching with Redis. To enhance customization of Forms, Formsets, and ErrorList they are now rendered using the template engine. Brian #2: py - The Python launcher py has been bundled with Python for Windows only since Python 3.3, as py.exe See Python Launcher for Windows I've mostly ignored it since I use Python on Windows, MacOS, and Linux and don't want to have different workflows on different systems. But now Brett Cannon has developed python-launcher which brings py to MacOS and various other Unix-y systems or any OS which supports Rust. Now py is everywhere I need it to be, and I've switched my workflow to use it. Usage py : Run the latest Python version on your system py -3 : Run the latest Python 3 version py -3.9 : Run latest 3.9 version py -2.7 : Even run 2.x versions py --``list : list all versions (with py-launcher, it also lists paths) py --``list-paths : py.exe only - list all versions with path Why is this cool? - I never have to care where Python is installed or where it is in my search path. - I can always run any version of Python installed without setting up symbolic links. - Same workflow works on Windows, MacOS, and Linux Old workfow Make sure latest Python is found first in search path, then call python3 -m venv venv For a specific version, make sure python3.8, for example, or python38 or something is in my Path. If not, create it somewhere. New workflow. py -m venv venv - Create a virtual environment with the latest Python installed. After activation, everything happens in the virtual env. Create a specific venv to test something on an older version: py -3.8 venv venv --``prompt '``3.8``' Or even just run a script with an old version py -3.8 script_name.py Of course, you can run it with the latest version also py script_name.py Note: if you use py within a virtual environment, the default version is the one from the virtual env, not the latest. Morleh #3: Transformers As General-Purpose Architecture The Attention Is All You Need paper first proposed Transformers in June 2017. The Hugging Face (

Machine Learning Guide
MLA 015 SageMaker 1

Machine Learning Guide

Play Episode Listen Later Nov 4, 2021 46:46


Show notes Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker DataWrangler Feature Store Ground Truth Clarify Studio AutoPilot Debugger Distributed Training And I forgot to mention JumpStart, I'll mention next time.