Chris Beagley

First step towards Databricks

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Ready to supercharge your data analysis with Python? Then give this great post from Maddie Dierkes a read as she has expertly put together a quick-start guide to TabPy in Tableau. TabPy allows you to utilise Python to create a measure more efficiently and although Python may seem daunting to those unfamiliar, there are plenty of easy ways to learn it. So, whether you're a Python pro or just dipping your toes into the world of coding, this blog is your ticket to seamlessly integrating Python scripts into your Tableau workflow. https://lnkd.in/gZQPX9qr #TableauTraining #Python #TabPy

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Big Data London! it's been a blast, thanks to everyone who came and queued to hear me ramble on about data visualisation and storytelling. Not sure what was going faster, my heart or my mouth 😁 you can see two other peaks, one where I had a dream that no one came to my session and one where I thought the tube train had broken down. #bigdataldn #datavisualisation #dataviz

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💻 Where are the UK's Data Engineering Hubs?🌍 1) London (6,111) 2) Manchester (665) 3) Leeds (432) 4) Edinburgh (392) 5) Bristol (347) 6) Glasgow (297) 7) Birmingham (233) 8) Sheffield (185) 9) Liverpool (178) 10) Reading (177) (by number of professionals) #LinkedInTalentInsights #DataEngineeringInsights

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Have to recommend this one for anyone working in data. This course quite literally changed the way I talk to people about data and has made me immeasurably more confident while doing so. The global network of data professionals, and very active WhatsApp group, are as impactful to me now as they were three years ago. Caroline Carruthers Peter Jackson Carruthers and Jackson #data #analytics #datascience #dataLeadership

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tell me you're a data professional without telling me you're a data professional... when I build brio train tacks, I build them only using track pieces. Once it's up and running then I will swap out pieces of track for the fancy bits i.e a windmill. Foundations over fancy features for release 1.0. #dataleaders #dataprofessionals #dataleadership #cdo #dataanalytics

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Discover how eBay created their own language model using three billion item titles in this exciting video. We'll explore the innovative techniques eBay used, including training their BERT model from scratch on eBay's massive dataset, knowledge distillation to compress the model, and fine-tuning the compressed model to learn similarity better. With a 3.5% increased purchase order rate and enhanced customer engagement, this is a must-watch for anyone interested in the power of language models. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: youtu.be/h51nbWr7feo YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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August line up announced! 📢 The Manchester Databricks User Group is excited to announce our August speakers! 🥁 James Cole - Senior Data Engineer at Specsavers will explore the importance of shared code. In this session, you'll learn about different methods for packaging and distributing code to Databricks, gaining practical insights to streamline your shared code deployment process. Gain practical insights to streamline your shared code deployment process. Anna-Maria Wykes - Databricks Solution Architect Champion and Microsoft AI MVP will present an in-depth exploration of Databricks Lakehouse Monitoring, a new feature designed to enhance data integrity and adaptability in data lakes. By the end of this session, you'll have a better understanding of Databricks Lakehouse Monitoring, how to implement it, and if it's the right fit for you. We'll also have Databricks employees on hand to answer any questions you may have. Don't miss this fantastic line up! Thursday 15th August 📅 18:00 - 20:00 ⌚ Sign up using the link in the comments below ⬇️ #meetup #databricks #networking #learning

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Analytics in the UK... There has been a lot of talk here on LI recently about data professionals needing to be more business savvy and vice versa... Data leaders needing to influence business leaders, data being seen as a business asset, data needing to add value blah blah blah... Many of us have been having this same conversation for what seems like an eternity, and yet we are not really any further forward... Why? What are we doing wrong (or not doing)? Why isn't The Business listening? Well, maybe, just maybe, it's time to change the record... Data will never be "given" a seat at the top table, Data must demand one! The Business will not adopt a data culture, Data must create one! Data should not have to apologise for asking questions or driving change, explain why it's not just "tech" and how it is a value creator... Data leaders should be loud and proud... Data should be celebrated and embraced, otherwise there really will be "no future" for us! So, Never mind The Business, here's The Data... Now sing (shout) after me: We're so pretty, oh so pretty, we're Data! We're so pretty...

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🎤🎤🎤🎤🎤🎤 Sheffield Data Analytics User Group have kindly invited me to present my session - From No Code to Low Code: Getting Started with M in Power Query If you're up that way, come and join us on December 4th! Meetup Link: https://lnkd.in/evgAZnCq #PowerQuery #MCode #PowerBI #DataAnalytics

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Week 4 of DataTalksClub LLM-zoomcamp was rich in content. This week the focus was: 1) Setting up an LLM powered Streamlit app. The app provides users the capability to ask questions. We capture feedback from the user as part of the monitoring workflow. All data is stored and in a Postgres database. A visualisation dashboard is used for monitoring model performance (Grafana Labs). 2) We also explored how to use cosine similarity to deliver a verdict on how well an LLM is responding to user queries. A large language model was then used as a secondary evaluation method to guage quality of our user facing model. As always a big thanks to Alexey Grigorev and the team for making these available to many of us who enjoy collaborative learning and hands on projects. https://lnkd.in/drN-v7xv

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Eleven days of time series - day 4: This episode focuses on a popular business forecasting problem – predicting sales and demand. As with every topic, there are some specific challenges here – the most important one being the intermittent nature of the data: our time series frequently have long periods of inactivity (zero values), when vintage methods struggle. We will talk about how to handle such situation both for a single series, as well as jointly analyzing multiple ones.

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If you truly want to make a difference for your business with data, spend as much time understanding how your business works as you do honing your data skills. Deep knowledge of your business context is crucial; one without the other won’t yield significant results. Simply put, if you want to personalise customer experiences using data, first personalise your approach to data for your business. #Data #BusinessIntelligence #DataDriven #BusinessInsight

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Day 52/100: Leverage data skills through business knowledge - data skills on its own don’t provide value As data and analytics are there to support business objectives, it is incredibly important for data practitioners to have a good understand of those objectives and the underlying business strategy. Without this knowledge, we can only guess what could be helpful to succeed. Creating technical solutions becomes more and more democratized in the age of generative AI. Many predict that the separation between pure product management roles and pure engineering roles will become more and more blurry. We will shift towards a setting where those with the business knowledge, e.g. product managers, will be more and more involved in the technical implementation of solutions. Product engineers emerge. This is an excellent opportunity for tech-savvy business folks to build some data skills so they can be at the forefront of this shift. Having expert level domain knowledge with some level of technical knowledge can help them have a huge positive impact on business strategies and outcomes. Having expert level data skills is great, but without business knowledge it is hard to use them to build something meaningful. Business domain experts are in a pretty interesting place right now. Pick a target role, build some data skills, and craft a story. AI will give you wings. #data #ai

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NEW: Questions in Dataviz: How do we bring joy to data visualisations?  https://lnkd.in/efGpzyC5 Alternatively titled "What is visual prosody?" - a review of Joyful Infographics by Nigel Holmes

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Missed Tableau's #DataDev Day back in June? You can now watch the recordings of the talks on YouTube, including my presentation about building an Investment Portfolio Optimisation Tool: https://lnkd.in/eyHNYgQ9 Want to know more about this project? Read this blog post to learn about my original idea and its current architecture: https://lnkd.in/dSzij8UF

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📊 Power BI seems to be continuing to take market share from the likes of Tableau, Qlik and Looker, and I'm keen to hear everyone's thoughts as to why. Many potential reasons go through my head, and it's probably a combination of a few of these. And of course, this is just my view with the customers I speak with, and organisations I hear about, so may not be the case across the board. Is it: 💥 because it's genuinely the best tool out there, for the purpose? 💥 due to business pressures to have more Microsoft products in an MS focussed organisation? 💥 because no one gets told off for buying Microsoft?! 💥 because it's cheaper for the 'out of the box' product? 💥 because it's deemed easier, and possibly cheaper, to hire resource? 💥 because it has the edge when it comes to NLP? 💥 because it's deemed as being easier to learn? 🤯 something else? Let me know your thoughts and what your honest opinion of Power BI when comparing with other tech. #powerbi #tableau #qlik #looker #datavisualisation #analytics

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No-one ever tells you how many aspects of a data project have nothing to do with data. There's easily as much non-data work that goes into a data project as there is data work. 1. A project and rollout plan. 2. Stakeholder training. 3. Ongoing support. 4. Getting buy in and enthusiasm. 5. Change management. 6. Access management. 7. Documentation. In a big organization, you can partner with other teams to manage some of those aspects, but, as anyone who's worked in a big organization knows, that means politics and resource trading. I value soft skills so highly because items like this have a big impact on the outcome of our work, but the skills aren't traditional "data" skills.

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