Podcasts about data wrangling

Fanfilm – Darth Maul: Apprentice & Halo – A Hero's Journey – Editing vor dem Dreh Stell dir vor: Eine weit, weit entfernte Galaxie, Lichtschwert-Duelle und epische Raumschlachten. Viele träumen davon, doch manche greifen zur Kamera und erschaffen einfach selbst neue Abenteuer in ihren Lieblingsuniversen. Fanfilme sind mehr als nur Hommagen: Sie sind oft beeindruckende Zeugnisse von Leidenschaft, Kreativität und erstaunlichem handwerklichen Können. Doch wie entstehen diese Werke, die teils Millionen Klicks generieren und sogar die Original-Schöpfer begeistern? In dieser Folge von „Credit to the Edit“ tauchen wir tief ein in die Welt des ambitionierten Fanfilms. Im Mittelpunkt steht Shawn Bu, dessen „Star Wars“-Fanfilm „Darth Maul: Apprentice“ viral ging und der mittlerweile auch Auftragsarbeiten, wie einen „Halo“-Kurzfilm für Microsoft, realisiert hat. Gemeinsam mit Postproduktions-Profi Martin Sundara beleuchtet das Gespräch die einzigartigen Produktionsprozesse hinter solchen Projekten. Es geht um das Arbeiten im eingespielten Freundeskreis und um unkonventionelle Workflows. Ein besonderer Fokus liegt dabei auf dem Thema Previsualisierung („Previs“). Wie detaillierte Planung, nicht nur für Action, sondern auch für Dialogszenen und Kameraführung, schon vor dem Dreh den fertigen Film formt und paradoxerweise mehr kreative Freiheit am Set ermöglicht. Die spannenden Einblicke in diese kreative Nische kommen von zwei Gästen, die das Thema aus erster Hand kennen und sowohl die Perspektive des unabhängigen Machers als auch die des etablierten Branchen-Kenners einbringen:   Shawn Bu Geboren am 25. August 1986 in Aachen, ist ein deutscher Regisseur, Kameramann, Filmeditor, Podcaster und Webvideoproduzent. Bekannt wurde er durch seinen Fan-Kurzfilm „Darth Maul: Apprentice“ (2016), der millionenfach auf YouTube angesehen wurde. Zusammen mit seinem Bruder Julien Bam gründete er 2017 die Produktionsfirma Raw Mind Pictures GmbH. Unter seiner Regie entstand 2021 die Netflix-Miniserie „Life's a Glitch with Julien Bam“, und er arbeitete an der Webserie „Der Mann im Mond“ (2023–2025). Shawn übernimmt oft Regie und Schnitt in seinen Projekten.   Martin Sundara Martin Sundara ist seit 2002 in der deutschen Film- und Fernsehproduktion tätig. Nach seinem Einstieg bei ActionConcept, wo er vom Log-Praktikanten über den Cutterassistenten bis zum stellvertretenden Leiter der Postproduktion aufstieg, wechselte er 2005 zu HeadQuarter (heute ActHQ) und verantwortet dort als Techniker und DI-Operator Bildprozesse wie Backup, Ingest, Data-Wrangling, Conforming, Titelbearbeitung und Mastering. Seit 2019 arbeitet er zusätzlich als Kamera­operator für Dokumentationen, Fernsehformate und Imagefilme und ist seit 2021 lizensierter Drohnenpilot. Parallel unterstützt er Projekte von Shawn Bu und Julien Bam in wechselnden Funktionen, darunter Set-Runner, Regie- und Kameraassistenz, Requisitenbau und VFX-Koordination.   Was braucht es wirklich, um eine Actionszene zum Leben zu erwecken? Wie hilft akribische Vorbereitung dabei, am Set flexibel zu bleiben? Und wie fühlt es sich an, plötzlich Regieanweisungen an Mark Hamill zu geben? Diese Folge von „Credit to the Edit“ liefert nicht nur Antworten, sondern auch inspirierende Geschichten und handfeste Tipps für alle, die sich für neue Arten Filmschnitt zu nutzen und natürlich für die Macht der Fan-Leidenschaft interessieren. Reinhören lohnt sich!   Timeline-Shortcuts 00:00:14 Anmoderation 00:02:52 Gesprächsstart / Julians YouTube-Universum & Der Mann im Mond 00:09:38 Fanfilm-Familie & Guerilla-Workflow 00:17:13 Die Macht der Previsualisierung (Previs) 00:28:07 Case Studies: Darth Maul & Halo 00:39:21 Geheimnisse des Action-Schnitts 00:57:34 Schnitt, Flexibilität & Zukunft 01:00:21 Kategorien 01:28:13 Abmoderation   Links Projekte & Filme Darth Maul: Apprentice – Fanfilm von Shawn Bu Der letzte Song aus der Bohne – Akt 1 Der letzte Song aus der Bohne – Akt 2 Der letzte Song aus der Bohne – Akt 3 Der Mann im Mond – Spielfilm-Finale A Hero's Journey – Halo Fanfilm Personen & Teams Shawn Bu Julien Bam – YouTube Martin Sundara – IMDb Vincent Lee – Instagram Ben Schamma aka Maul Cosplay – Instagram Vi-Dan Tran – Instagram Weitere Inhalte Star Wars – Episode I: Die dunkle Bedrohung – IMDb Halloween (2007) – IMDb The Devil's Rejects (2005) – IMDb Predator (1987) – IMDb

https://bit.ly/LadderPredictorStats How does the distance from the each AFL ground to the nearest KFC or McDonald's affect YOUR football club?

Navnit Shukla is a solutions architect with AWS. He joins me to chat about data wrangling and architecting solutions on AWS, writing books, and much more. Navnit is also in the Coursera Data Engineering Specialization, dropping knowledge on data engineering on AWS. Check it out! Data Wrangling on AWS: https://www.amazon.com/Data-Wrangling-AWS-organize-analysis/dp/1801810907 LinkedIn: https://www.linkedin.com/in/navnitshukla/

As our geo-toolkit expands, how can we equip ourselves to deal with these large volumes of highly diverse, dense data that are available and at higher speeds than ever before? This week's episode is a companion to episode 47 (Core Sensing Technology) and host Britt Bluemel (Global Business Development Manager, ALS GoldSpot Discoveries) is joined by experts in the field of big data wrangling.   They discuss considerations when dealing with data from core sensing systems, with the aim of empowering geologists with better decision-making tools throughout the mining value chain.  New out this week is also a great paper in the SEG Discovery Magazine by Anthony Harris and co-authors - Empowering Geologists in the Exploration Process - Maximizing Data Use from Enabling Scanning Technologies.  Check it out for diagrams and case studies that demonstrate the use of core scanning technology.In this week's episode, our first guest, Dr. McLean Trott (Director, Ore Body Knowledge at ALS GoldSpot Discoveries) just completed his PhD on the topic of tackling big data and integration of various data streams, and how to extract the most value from datasets, including image data. Mac also discusses the utility of point measurement compared to line scanning or full core imaging, with an emphasis on fit-for-purpose data, while considering bottom line factors like speed and cost of data acquisition.  Next,  we're joined by Dr. Jack Milton, VP Geology at Fireweed Metals, and he provides the ‘end user' perspective. Fireweed Metals has used XRF core scanning technology for several years and Jack describes some of the key benefits and real time decision making that is enabled by this technology. Jack also discusses good connectivity for transferring these huge data files (their on-site scanner has its own dedicated Starlink system) and the necessity of high quality calibrations when collecting XRF data in the field.  Our final guest, Brenton Crawford (Datarock's Chief Geoscientist) cautions us not just to choose the coolest machine, but to select the sensor that's right for the job. He discusses utilizing scanning data to create geometallurgical domains, and how project success can be increased by including your IT team in the early stages of the conversation.  Next week, Anne Thompson will be back with three exceptional guests, to discuss the geology of lithium and explore three different host environments, brines, clays and pegmatites.Our theme music is Confluence, by Eastwinds.  

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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define terms related to data. Because, data is the heart of AI. So it's important to understand the role data plays in AI and ML projects. In this episode we go over the terms data engineer, data engineering, and data pipeline. Continue reading AI Today Podcast: AI Glossary Series- Data Engineer, Data Engineering, Data Pipeline, Data Wrangling, Data feed, Data Governance, Data integration at Cognilytica.

This week, Madrona Managing Director Tim Porter talks to Numbers Station Co-founders Chris Aberger and Ines Chami. We announced our investment in Numbers Station's $17.5M Series A in March and are very excited about the work they're doing with foundation models, which is very different than what has been making headlines this year. It isn't content or image generation – Numbers Station is bringing the transformational power of AI inside of those foundation models to the data-wrangling problems we've all felt! You can't analyze data if the data is not prepared and transformed, which in the past has been a very manual process. With Numbers Station, the co-founders are hoping to reduce some of the bifurcation that exists between data engineers, data scientists, and data analysts, bridging the gaps in the analytics workflow! Chris and Ines talk about some of the challenges and solutions related to using foundation models in enterprise settings, the importance of having humans in the loop — and they share where the name Numbers Station came from. But, you'll have to listen to learn that one!

On today's episode, we're joined by Michael Katz, CEO and Co-founder of mParticle an AI-powered customer data platform. We talk about:Helping tame data chaosDemocratizing access to AI & data tools to bridge the divide between haves & have-nots Facilitating communication & minimizing conflictOpportunities & threats of AI… and when human intuition winsWhat activities will make companies breakout

Antony Green is well known as the ABC's Election Data Specialist, and he generously shared his time and expertise in a wide ranging conversation about the statistics of elections, how stats are misused, and what he wishes everyone knew about data. Turns out there's a vast amount of preparation that goes into those fascinating election night broadcasts.

Once upon a time scientists would dream of the day when they could have enough information to make decisions based on data.  Young readers may have to go to history books to see computer science majors take stacks of punch cards to a computer room so they can get an answer in the morning. Fast forward to 2022, we have so much data we don't know how to handle it.  The overview is simple – gather up a reasonable number of data sets and pour it through an algorithm and then out pops the answer.  For example, back in 2017, it was reported that the DoD collected 22 terabytes of data a day. You would have to add many zeros to that number to see what they are collecting today. As a result, people with a doctorate in mathematics, like Dr. Elsa Schaefer from LinQuest, must wrestle with questions about what data to gather to make valid decisions. During the interview, she used terms like Data Wrangling, Machine Language Operations (MLOps), and data brittleness.  It appears that there is as much an art as it is a science to competently gather data for decisions to be made.  The term “brittle” is intriguing.  Let's say you have an application with a large data set that is working well.  It is quite possible that a systems architect can pour that data into a data set, and it may cause problems.  Because it may cause a system to break, it is called “brittle.” LinQuest is developing a platform to help federal leaders gain a better understanding of using machine data. Data scientists try different scenarios and algorithms to see how they hold up.  If you would like to pursue this topic further, you may want to download a fact sheet that details their Harness for Adaptive Learning. 

Theres a lot more to data wrangling than you may think, I certainly learnt a lot on this episode. Camera Assistant Diana Mandic takes us through roles and responsibilities of the Data wrangler, working with a DIT, how to structure your day and the systems she uses to cross check and communicate with post production. 

One of the great aspects of the Cloud software delivery model is the generation of insightful data generated by users and the purported ease of using the data to better inform  decision-making.At the same time, the amount of data being generated in the Cloud makes the job of normalizing, analyzing, and determining what data is truly informative and predictive versus just adding noise and complexity to the system.Adam Wilson, CEO at Trifecta who was purchased by Alteryx after this podcast was recorded, shares his insights in building awareness and a company that takes on the growing challenges of managing and using data generated in the cloud.

Ahhhh, the deeper dive... Fintan and Jon are joined in this week's longer episode by Push Technology's Sean Bowen, discussing the mechanics of data wrangling and the importance of efficiency for operators of all kinds, and Richard Marcus, poacher turned gamekeeper, practitioner of the Dark Arts of table game manipulation and master storyteller. Join us...

Fintan and Jon are joined in this week's TL;DR episode by Push Technology's Sean Bowen, discussing the mechanics of data wrangling and the importance of efficiency for operators of all kinds, and Richard Marcus, poacher turned gamekeeper, practitioner of the Dark Arts of table game manipulation and master storyteller. Join us...

Ashley Davis jumps in to talk to Dan Shappir about wrangling data using JavaScript. Ashley describes his journey into JavaScript and his exposure to the web platform. From there he walks Dan through learning data science and building systems in Python before coming back to JavaScript. He talks through the tools and techniques used to manage data in JavaScript as well as how it can be done! Panel Dan Shappir Guest Ashley Davis Sponsors Dev Influencers Accelerator Raygun | Click here to get started on your free 14-day trial  Links Data Wrangling with JavaScript Data-Forge Project Jupyter Charlie Gerard on Twitter Bootstrapping Microservices with Docker, Kubernetes, and Terraform  Code Capers Data-Forge Notebook JSJ 442: Breaking Into Tech with Danny Thompson | Devchat.tv Twitter: Ashley Davis ( @ashleydavis75 ) Picks Ashley- AshleyDavis- Twitch Dan- Interlude: Rethinking the JavaScript Pipeline Operator

Ashley Davis jumps in to talk to Dan Shappir about wrangling data using JavaScript. Ashley describes his journey into JavaScript and his exposure to the web platform. From there he walks Dan through learning data science and building systems in Python before coming back to JavaScript. He talks through the tools and techniques used to manage data in JavaScript as well as how it can be done! Panel Dan Shappir Guest Ashley Davis Sponsors Dev Influencers Accelerator Raygun | Click here to get started on your free 14-day trial  Links Data Wrangling with JavaScript Data-Forge Project Jupyter Charlie Gerard on Twitter Bootstrapping Microservices with Docker, Kubernetes, and Terraform  Code Capers Data-Forge Notebook JSJ 442: Breaking Into Tech with Danny Thompson | Devchat.tv Twitter: Ashley Davis ( @ashleydavis75 ) Picks Ashley- AshleyDavis- Twitch Dan- Interlude: Rethinking the JavaScript Pipeline Operator

Ashley Davis jumps in to talk to Dan Shappir about wrangling data using JavaScript. Ashley describes his journey into JavaScript and his exposure to the web platform. From there he walks Dan through learning data science and building systems in Python before coming back to JavaScript. He talks through the tools and techniques used to manage data in JavaScript as well as how it can be done! Panel Dan Shappir Guest Ashley Davis Sponsors Dev Influencers Accelerator Raygun | Click here to get started on your free 14-day trial  Links Data Wrangling with JavaScript Data-Forge Project Jupyter Charlie Gerard on Twitter Bootstrapping Microservices with Docker, Kubernetes, and Terraform  Code Capers Data-Forge Notebook JSJ 442: Breaking Into Tech with Danny Thompson | Devchat.tv Twitter: Ashley Davis ( @ashleydavis75 ) Picks Ashley- AshleyDavis- Twitch Dan- Interlude: Rethinking the JavaScript Pipeline Operator

Joining Katie and Ken today is Marie Hibbert, our James Moore colleague and Director of Business Intelligence and Data Analytics. We are going to dive into a pain point for many in collegiate athletics, which is financial reporting. We know it's difficult to cross walk your general ledger to the financial model required for EADA and NCAA reporting. While Excel is a powerful tool many of you use, it may not be enough. The time you spend collecting, organizing and cleaning up your financial information could be better utilized by gaining valuable business insights that drive organizational change. Join us as we explore taking your data beyond Excel—from wrangling your data in a repeatable fashion to gaining insights. 00:10 - Welcome and Introductions 00:40 - Athletics Business Offices Need Help 02:39 - What is Data Wrangling? 03:47 - Identifying Key Metrics That Are Relevant 05:37 - How to Tell a Story with Your Data 09:36 - Using Data to Make Key Business Decisions and Drive Organizational Change 20:10 - Brews of the Month & Wrap-up Sign-up to receive News & Brews Sports Biz notifications when new episodes are released: https://www.jmco.com/news-and-brews/ Learn more about James Moore Collegiate Athletics Services Team: https://www.jmco.com/industries/collegiate-athletics/

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out. --- Send in a voice message: https://anchor.fm/tonyphoang/message

What is data wrangling in data analytics..? Data wrangling is the very important topics in the data analytics field. The answer is… Data wrangling is the process of cleaning, structuring, enriching, validating and analyzing the raw data into a desired usable format for better decision making. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Career transition is all about what you sow so shall you reap. Book a free call consultation on your career roadmap on Instagram @meet_kanth or visit my website https://www.kanthh.com/callto-kanthAbout our Career Transition Programs, Co-Developed by Senior AI Engineers: Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker Q & A Session from Recently Placed Participants: https://www.youtube.com/watch?v=wUy6YWXYQ9w&t=4s Get Free Course on “Statistics for Data Science Audio Book”: https://www.maaog.co.in/statistics-for-data-science-audio-book Read our Machine Learning CaseStudy on Fitness : https://www.maaog.co.in/machine-learning-casestudy-in-fitness_ebookTake Free Webinar on Career Transition Programs: https://www.bepec.in/webinar-registration-form***Worried about how to start with AI Career?? https://www.bepec.in/the-enroute-to-ai-career ***For more video updates, subscribe our YouTube Channel: https://www.youtube.com/channel/UCn1USB9-5UqKJTSHd1JGcVw/?sub_confirmation=1Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1:Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2: Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions.Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform.Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function.Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standardsInterested to look into Data Science Prototype - Click HereI hope you gonna through above Job Descriptions, Once cross-check whether you have the confidence to work on every point mentioned from above JD's?? Do you have a clear understanding of the Data Scientist lifecycle and his role in the company? Or you are just thinking about Statistics, Python, Machine Learning and Accuracies can help you get a job as Data Scientist??To speak in interviews, you must have a proven portfolio on Data Science Projects to Showcase in your resume as well as to speak damn confident in interviews.If you are planning to learn a Data Science Program based on the above job requirements enroll for our Data Science Career Transition Program(YDSCT30) 30% Discount: https://www.bepec.in/data-science-career-transition

Meet our Mentor, Mr Kanth, on Instagram @meet_kanth & book a free call consultation on his Instagram page and construct your career transition roadmap.About our Career Transition Programs, Co-Developed by Senior AI Engineers: Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker Q & A Session from Recently Placed Participants: https://www.youtube.com/watch?v=wUy6YWXYQ9w&t=4s Get Free Course on “Statistics for Data Science Audio Book”: https://www.maaog.co.in/statistics-for-data-science-audio-book Read our Machine Learning CaseStudy on Fitness : https://www.maaog.co.in/machine-learning-casestudy-in-fitness_ebook Take Free Webinar on Career Transition Programs: https://www.bepec.in/webinar-registration-form***Worried about how to start with AI Career?? https://www.bepec.in/the-enroute-to-ai-career ***For more video updates, subscribe our YouTube Channel: https://www.youtube.com/channel/UCn1USB9-5UqKJTSHd1JGcVw/?sub_confirmation=1Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1:Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2: Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions. Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform. Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function. Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standardsInterested to look into Data Science Prototype - Click HereI hope you gonna through above Job Descriptions, Once cross-check whether you have the confidence to work on every point mentioned from above JD's?? Do you have a clear understanding of the Data Scientist lifecycle and his role in the company? Or you are just thinking about Statistics, Python, Machine Learning and Accuracies can help you get a job as Data Scientist??To speak in interviews, you must have a proven portfolio on Data Science Projects to Showcase in your resume as well as to speak damn confident in interviews.If you are planning to learn a Data Science Program based on the above job requirements enroll for our Data Science Career Transition Program(YDSCT30) 30% Discount: https://www.bepec.in/data-science-career-transition

This week on the show we are very Lucky to be joined by Laimonas, better know in the industry as LSDigi, a professional Digi-Tech and all round nice guy. His knowledge and stories flow fast in this episode and there's plenty for photographers to pick up on. As ever links are below for those wanting to delve further into things. You can follow LS Digi on instgram here Inovativ.com - for digi plates etc LSdigi products - see his shop online for cable clamps, hyperjuice holder etc Thingiverse.com - for opensource 3D printing designs TIP: syncing camera clocks every shoot TIP: Filenaming convention: YYMMDD_jobname TIP: Swap from standard hard drives to SSDs Angelbird SD cards - faster cards Angelbird CFast 2.0 Memory Card Reader - rock solid card reader Area 51 cables - as an alternative to Tethertools FotoFortress cable clamp Sidecar for Apple iPad Hollyland 400S transmitters Pentax 6x7 shutter sound (skip to 40 seconds) Desert island camera : Mamiya RB67

Course Data Scientist: He/She cares more about statistics, python, machine learning, NLP, Deep Learning, e.t.c. they do it just they know all the above topics so they are doing it. Real-Time Data Scientist: He/She cares more about value, more about adding impacting to business/process with the help of data. To deliver the impact they may use statistics, python, machine learning, deep learning, NLP. Listen to this entire video and post me which Data Scientist you want to hire? Get Flat 30% Discount(YMLCT30) on your Machine Learning Program: https://www.bepec.in/machinelearningcourseAbout our Career Transition Programs, Co-Developed by Senior AI Engineers: Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker Q & A Session from Recently Placed Participants: https://www.youtube.com/watch?v=wUy6YWXYQ9w&t=4s Get Free Course on “Statistics for Data Science Audio Book”: https://www.maaog.co.in/statistics-for-data-science-audio-book Read our Machine Learning CaseStudy on Fitness : https://www.maaog.co.in/machine-learning-casestudy-in-fitness_ebookTake Free Webinar on Career Transition Programs: https://www.bepec.in/webinar-registration-form***Worried about how to start with AI Career?? https://www.bepec.in/the-enroute-to-ai-career ***For more video updates, subscribe our YouTube Channel: https://www.youtube.com/channel/UCn1USB9-5UqKJTSHd1JGcVw/?sub_confirmation=1Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1:Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2: Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions. Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform. Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function. Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standardsInterested to look into Data Science Prototype - Click HereI hope you gonna through above Job Descriptions, Once cross-check whether you have the confidence to work on every point mentioned from above JD's?? Do you have a clear understanding of the Data Scientist lifecycle and his role in the company? Or you are just thinking about Statistics, Python, Machine Learning and Accuracies can help you get a job as Data Scientist??To speak in interviews, you must have a proven portfolio on Data Science Projects to Showcase in your resume as well as to speak damn confident in interviews.

In the second episode of a series on the machine-learning pipeline, Joe Hellerstein, a professor at UC Berkeley, talks about data wrangling. 

Get Flat 30% Discount(YMLCT30) on your Machine Learning Program: https://www.bepec.in/machinelearningcourseAbout our Career Transition Programs, Co-Developed by Senior AI Engineers: Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker Q & A Session from Recently Placed Participants: https://www.youtube.com/watch?v=wUy6YWXYQ9w&t=4s Get Free Course on “Statistics for Data Science Audio Book”: https://www.maaog.co.in/statistics-for-data-science-audio-book Read our Machine Learning CaseStudy on Fitness : https://www.maaog.co.in/machine-learning-casestudy-in-fitness_ebook Take Free Webinar on Career Transition Programs: https://www.bepec.in/webinar-registration-formFor more video updates, subscribe our YouTube Channel: https://www.youtube.com/channel/UCn1USB9-5UqKJTSHd1JGcVw/?sub_confirmation=1Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1:Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2: Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions.Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform.Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function.Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standardsInterested to look into Data Science Prototype - Click HereI hope you gonna through above Job Descriptions, Once cross-check whether you have the confidence to work on every point mentioned from above JD's?? Do you have a clear understanding of the Data Scientist lifecycle and his role in the company? Or you are just thinking about Statistics, Python, Machine Learning and Accuracies can help you get a job as Data Scientist??To speak in interviews, you must have a proven portfolio on Data Science Projects to Showcase in your resume as well as to speak damn confident in interviews.If you are planning to learn a Data Science Program based on the above job requirements enroll for our Data Science Career Transition Program(YDSCT30) 30% Discount: https://www.bepec.in/data-science-career-transition

Get Flat 30% Discount(YMLCT30) on your Machine Learning Program: https://www.bepec.in/machinelearningcourseAbout our Career Transition Programs, Co-Developed by Senior AI Engineers: Become a Machine Learning Engineer who can work from designing Machine Learning Solution Architecture, Data Understanding, Data Wrangling, Model Building, Model Evaluation and Model Deployment using Docker and Sagemaker Q & A Session from Recently Placed Participants: https://www.youtube.com/watch?v=wUy6YWXYQ9w&t=4s Get Free Course on “Statistics for Data Science Audio Book”: https://www.maaog.co.in/statistics-for-data-science-audio-book Read our Machine Learning CaseStudy on Fitness : https://www.maaog.co.in/machine-learning-casestudy-in-fitness_ebook Take Free Webinar on Career Transition Programs: https://www.bepec.in/webinar-registration-form***Worried about how to start with AI Career?? https://www.bepec.in/the-enroute-to-ai-careerFor more video updates, subscribe our YouTube Channel: https://www.youtube.com/channel/UCn1USB9-5UqKJTSHd1JGcVw/?sub_confirmation=1Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1:Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2: Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions. Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform. Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function. Ability to support and guide end-to-end model lifecycle management Create model documentation as per client/ regulatory standardsInterested to look into Data Science Prototype - Click HereI hope you gonna through above Job Descriptions, Once cross-check whether you have the confidence to work on every point mentioned from above JD's?? Do you have a clear understanding of the Data Scientist lifecycle and his role in the company? Or you are just thinking about Statistics, Python, Machine Learning and Accuracies can help you get a job as Data Scientist??To speak in interviews, you must have a proven portfolio on Data Science Projects to Showcase in your resume as well as to speak damn confident in interviews.If you are planning to learn a Data Science Program based on the above job requirements enroll for our Data Science Career Transition Program(YDSCT30) 30% Discount: https://www.bepec.in/data-science-career-transition

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Florian ist auf dem Weg ein Shooting-Star der deutschen Data Science Szene zu werden, indem er als Co-Founder von 8080 Labs Tools entwickelt, welche die Arbeit von Data Scientists erheblich beschleunigen sollen. Das Flagship-Produkt dabei ist derzeit die Software bamboolib, mit der pandas DataFrames um eine semantische Autovervollständigung sowie eine grafische Benutzeroberfläche erweitert werden, was eine deutlich schnellere Datenexploration und -transformation ermöglicht. In dieser Podcast-Episode beschreibt uns Florian seine Tools nicht nur von der technischen Seite, sondern wir erfahren auch Business-Aspekte und die Geschichte hinter der Gründung von 8080 Labs.

We talk about remote audiovisual production, wrangling data, and genealogy in evolutionary biology. https://images.squarespace-cdn.com/content/567d5c40c647adf832eb7aa5/1589694146158-AM7T3EF4M1Z9U0RBNKSJ/002+Audio+Hijack.png?content-type=image%2Fpng Audio Hijack - Record any audio on Mac (https://rogueamoeba.com/audiohijack/) Bon Appétit (https://www.youtube.com/channel/UCbpMy0Fg74eXXkvxJrtEn3w) Pro Chefs Make 13 Kinds of Pantry Pasta | Test Kitchen Talks @ Home | Bon Appétit (https://www.youtube.com/watch?v=2ECoTEcPv4U) Have I Got News for You (https://en.wikipedia.org/wiki/Have_I_Got_News_for_You) The Mash Report (https://en.wikipedia.org/wiki/The_Mash_Report) Someone Practicing (https://www.youtube.com/channel/UCSJp7z2lBrG6BCozj4w-Flw) Final Cut Pro X (https://en.wikipedia.org/wiki/Final_Cut_Pro_X#Reception) Material Exchange Format (https://en.wikipedia.org/wiki/Material_Exchange_Format) FASTA format (https://en.wikipedia.org/wiki/FASTA_format) FASTQ format (https://en.wikipedia.org/wiki/FASTQ_format) Help desk software (https://en.wikipedia.org/wiki/Help_desk_software) Airtable: Organize anything you can imagine (https://airtable.com) Cryptic and extensive hybridization between ancient lineages of American crows (https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.15377) Allopatric speciation (https://en.wikipedia.org/wiki/Allopatric_speciation) Proto-language (https://en.wikipedia.org/wiki/Proto-language)

Apache Hadoop.HDFS.Apache Hive.Apache Spark.Presto.Architecture Of Giants: Data Stacks At Facebook, Netflix, Airbnb, And Pinterest.Data Wrangling.Null++ Docker Episode.Julia Language.kaggle.SED Podcast, Episode: Slack Data Platform with Josh Wills.Article Software 2.0.Aya's Recommendation for learning:Towards data science.Statistics and Data Science MicroMasters.DataCamp.Udemy: Python for Data Science and Machine Learning Bootcamp.Coursera's Deep Learning Specialization.Lex Fridman Artificial Intelligence Podcast & YouTube channel.Episode Notes:Aya: How To lie with statistics book.Luay: Great Expectations Data Pipeline Testing Framework.Alfy: JAM Stack.

Data wrangling tools have the potential to play a huge role in tax transformation—but what are the best ways to use them? And how can these tools work alongside traditional methods? In this tax podcast, Deloitte Tax LLP partners Emily VanVleet and RJ Littleton, along with managing director Craig Darrah, discuss what data wrangling is, how to build these tools out, and what it takes to deploy a new tax technology.

A discussion of Building Automation Interoperability Issues and Approaches with a Focus on Project Haystack. Semantics, data modeling and a broad variety of applications are examined.

¡Yep! ¡Muy buenas! Otra semana más por aquí con lo más fresco del Big Data

Join Cory and Brett as they sit down with Joe Hellerstein to explore one of the most challenging and time consuming parts of the data science process.  Regardless of if you call it Data Wrangling, Cleansing or Munging anyone who has had to do this k...

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

Victor Coustenoble nous explique aujourd'hui ce qu'est le "Data Wrangling". Trifacta est un outil de préparation de données intégré dans nos plateformes data favorites. Pour en savoir plus sur Trifacta c'est ici : https://www.trifacta.com/fr/ et là : https://www.trifacta.com/news-and-press/?language=frVous pouvez retrouver Victor sur sont twitter : https://twitter.com/vizanalytics ou sur Linkedin : https://www.linkedin.com/in/victorcoustenoble/------------------------------------------------------------- http://www.bigdatahebdo.comhttps://twitter.com/bigdatahebdoVincent : https://twitter.com/vhe74Edité par Affini-Tech ( http://affini-tech.com https://twitter.com/affinitech )On recrute ! venez cruncher de la data avec nous ! écrivez nous à recrutement@affini-tech.com

Victor Coustenoble nous explique aujourd'hui ce qu'est le "Data Wrangling". Trifacta est un outil de préparation de données intégré dans nos plateformes data favorites. Pour en savoir plus sur Trifacta c'est ici : https://www.trifacta.com/fr/ et là : https://www.trifacta.com/news-and-press/?language=frVous pouvez retrouver Victor sur sont twitter : https://twitter.com/vizanalytics ou sur Linkedin : https://www.linkedin.com/in/victorcoustenoble/------------------------------------------------------------- http://www.bigdatahebdo.comhttps://twitter.com/bigdatahebdoVincent : https://twitter.com/vhe74Edité par Affini-Tech ( http://affini-tech.com https://twitter.com/affinitech )On recrute ! venez cruncher de la data avec nous ! écrivez nous à recrutement@affini-tech.com

Daniel (@dwhitena) is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, Datapalooza, DevFest Siberia, GopherCon, and more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects. Interviewer: Rajib Bahar Agenda: - Many of us may or may not be aware of "Jupyter Notebook", which is a web application to write codes in various Languages such is R, Python, Julia, node.js, GoLang, Ruby, & Scala. That appliation in turn creates separate process in the Kernel to receive output from the OS and return the output back to the web application. One of the coolest thing you do is to maintain the Kernela on GoLang aka Go. Currently, Data Scientists tend to gravitate toward either R, or Python as language. You're playing with a bit more modern languages in data science. Why Go? How is it more useful in statistical analysis or Data visualization? - How do you achive reproducibility in data science? - Most of us heard of Virtual Machine tools such as VMWare, Virtual PC, Virtual Box. This is the 1st time I heard of containers. What are some key benefits of it?Are there websites such as Turnkey hub where you can get some good images of various OS / software / DBMS platforms? - What are some best practices around deploying Data Science Models? Do you do something similar to DBAs or DataEngineers to run a job at certain frequencies in the day or hour? - How do you use data pipelines in your project? Is that something used in ETL like Data-Wrangling process? - Please tell us where we can find you in social media? Music: www.freesfx.co.uk

Data Wrangling, featuring Adam Weinstein, MD by RPA

See the full show notes for this episode on the website at talkpython.fm/90.