Arthur Feriotti

If you're interested, go check out my first contribution to the Indicium Engineering blog! This post introduces one of the many ways to apply generative AI. I hope you enjoy! https://lnkd.in/dwjtEzBb

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Hey #datafam Hey everyone! I'm excited to share a project I've been working on: the Data Masking Tool! 🚀 🚀 🚀 Most of my tools originate from real needs within our community. Recently, Ivett Kovács brought to my attention a common challenge we all face: the need to protect sensitive information when working with data. We often require a reliable way to anonymize data for testing and development purposes, as well as creating projects for our Tableau Public based on a real use case we've built for a client, without relying on external services like GPT, which isn't recommended for handling sensitive data. To address this, I developed the Data Masking Tool—a Python application that helps you anonymize sensitive data in CSV and Excel files securely and efficiently. Key Features: 🔒 Secure Data Masking: Replace sensitive information with realistic fake data, all processed locally on your machine. ⚙️ Customizable Options: Adjust prefixes, suffixes, numeric ranges, date ranges, and create custom lists to tailor the anonymization. 🎛️ User-Friendly Interface: An intuitive GUI built with Tkinter makes it easy to select columns and configure settings (The current UI is not great - sorry, I'm not used to creating UIs with Python. Looking forward to collaborating on it 😊). 🔄 Consistent Mappings: Option to keep mappings consistent so that the same original value is always replaced with the same fake value. 📝 Introduce Blanks: Specify a percentage of blank values to simulate real-world incomplete datasets. We often need a reliable way to handle sensitive data without risking exposure. This tool processes everything locally, giving you peace of mind when anonymizing datasets. I'm inviting anyone interested to contribute! 🤝 Open-source projects thrive with community involvement. Whether you want to add new features, fix bugs, or improve the documentation, your help is welcome. 👉 GitHub Repository: https://lnkd.in/dMhvN_mU Let's collaborate to make data handling safer and more efficient. Huge thanks to the Swift Insights Tableau Team for providing feedback. Your feedback and suggestions are greatly appreciated. If you have any questions or ideas, don't hesitate to reach out! Thanks for your support! EDIT: I just added a Jupyter Notebook example in case you prefer it. #dataprivacy #datasecurity #opensource #python #dataanonymization #datamaskingtool #tableau #tableauambassador #hastech

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How To Specialize In Data Science / Machine Learning https://ift.tt/RnSuZx4 Is it better to be a generalist or specialist? Photo by Saulo Mohana on Unsplash Eventually, in your data science career, you’ll be asked, “What do you want to specialise in?” It’s quite a daunting question, and knowing what’s best for you is hard to say the least. So, in this article, I will explain why you should specialise, which one is right for you, and how to start. Why should you specialise? In my opinion, you should specialise, but there is no need to rush into this decision. Spend your initial years, like 2–3, learning all the data science and machine learning fundamentals. The things you should cover are: Basic statistics Linear algebra Calculus Linear and logistic regression Random forests Gradient boosted trees Neural networks Support vector machines Modelling best practices: cross-validation, hyperparameter tuning, etc. Writing high-quality Python and SQL This list is non-exhaustive, but it’s a good place to start. It will take a couple of years to fully understand everything. Some may think that’s a long time, but careers are long. If you start as a data scientist in your 20s, you could potentially have four decades working as a data scientist. A couple of years is nothing on that horizon. So, start with the basics and essentially become a generalist. You will then be able to solve most data science and machine learning problems, but you are not necessarily a deep expert in one particular field, which is totally fine at this point. After you have nailed all your fundamentals and have a sound footing, it’s time to decide where you want your expertise to lie in. The specialist vs. generalist debate is years old now, and there are pros and cons to both. Generalist Pros Better for the management track. More job opportunities. Wide variety of work and learning. Stick closer to the business side. Cons Less compensation as not an expert in any one area. Hard to become an IC if that’s something you want to achieve. Harder to get into a research based position. Specialist Pros Deep expertise could be seen as a higher value. Can go down both IC and manager paths. Work in an area you really enjoy. Cons Your expertise may become irrelevant in the future. May have limited opportunities compared to a generalist. I think it’s better to be a specialist because it’s easier to go from a specialist to a generalist than vice versa, and you are likely to be compensated more. However, I don’t think you should be an expert in one niche area. I recommend having T-shaped skills where you know the fundamentals very well and know roughly three areas to a pretty good depth. T-shaped skills diagram by author in Canva. This is kind of the best of both worlds and allows you to be more flexible in the job market. A lot of this assumes you are working in industry. If you want to do research, then it’s probably better to be a deep subject...

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Forms can be customized in various different ways and here is a nice example using the new Layout Container 🙂 Data Capture using Inphinity ∞ from your #Qlik dashboards

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Briefer (YC S23) : The Swiss Army Knife of Data Tools So, I am seeing some major hype in this new open-source Brazilian tool called Briefer that's supposed to revolutionize data analysis and data science. I mean, who wouldn't want to combine Python, SQL, dashboard creation, a Jupyter notebook, and data app development into one platform? ​​ The user-friendly interface is just the cherry on top. Guess it's time to say goodbye to my beloved collection of mismatched tools. Can't wait to dive in... being open source just makes it looks even more brilliant 🔥👏 #DataScience #OpenSource #Briefer

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How To Specialize In Data Science / Machine Learning Is it better to be a generalist or specialist? Photo by Saulo Mohana on Unsplash Eventually, in your data science career, you’ll be asked, “What do you want to specialise in?” It’s quite a daunting question, and knowing what’s best for you is hard to say the least. So, in this article, I will explain why you should specialise, which one is right for you, and how to start. Why should you specialise? In my opinion, you should specialise, but there is no need to rush into this decision. Spend your initial years, like 2–3, learning all the data science and machine learning fundamentals. The things you should cover are: Basic statistics Linear algebra Calculus Linear and logistic regression Random forests Gradient boosted trees Neural networks Support vector machines Modelling best practices: cross-validation, hyperparameter tuning, etc. Writing high-quality Python and SQL This list is non-exhaustive, but it’s a good place to start. It will take a couple of years to fully understand everything. Some may think that’s a long time, but careers are long. If you start as a data scientist in your 20s, you could potentially have four decades working as a data scientist. A couple of years is nothing on that horizon. So, start with the basics and essentially become a generalist. You will then be able to solve most data science and machine learning problems, but you are not necessarily a deep expert in one particular field, which is totally fine at this point. After you have nailed all your fundamentals and have a sound footing, it’s time to decide where you want your expertise to lie in. The specialist vs. generalist debate is years old now, and there are pros and cons to both. Generalist Pros Better for the management track. More job opportunities. Wide variety of work and learning. Stick closer to the business side. Cons Less compensation as not an expert in any one area. Hard to become an IC if that’s something you want to achieve. Harder to get into a research based position. Specialist Pros Deep expertise could be seen as a higher value. Can go down both IC and manager paths. Work in an area you really enjoy. Cons Your expertise may become irrelevant in the future. May have limited opportunities compared to a generalist. I think it’s better to be a specialist because it’s easier to go from a specialist to a generalist than vice versa, and you are likely to be compensated more. However, I don’t think you should be an expert in one niche area. I recommend having T-shaped skills where you know the fundamentals very well and know roughly three areas to a pretty good depth. T-shaped skills diagram by author in Canva. This is kind of the best of both worlds and allows you to be more flexible in the job market. A lot of this assumes you are working in industry. If you want to do research, then it’s probably better to be a deep subject matter expert in a...

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Hello everyone! This is my friend Matthew, Global Developer Advocate on Databricks, responsible to produce and share great content about Apache Spark, Delta Lake, Unity Catalog e MLflow 🔥 I met him through LinkedIn because of the quality content about Delta and Spark and we started talking via message, in San Francisco I decided to meet him in person. Today I want to talk about something important: ENGLISH! English is one of the most important things you need to learn if you want to have a experience in a global company or experience going to a global conference and connecting with people! Few days ago I received SAD news, someone canceled their enrollment in an MBA that I teach simply because my class slides are in english 🤯 I was very sad, not for me, but for opportunity that this person will lose while running away from this. Other example, yesterday I had the opportunity to meet Bill Zhang, Product Director from Cloudera and if I didn't have English, this would NEVER have been possible. Do you want to have experience working in a global data team? English for yesterday!!! Reflect and think about this! Like, comment and share this post if you want Matthew Powers, CFA in BRAZIL next year! Lets make noise!

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Serverless Agentic Workflows with Amazon Bedrock by DeepLearning.AI and Amazon Web Services (AWS).

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Every ML team needs an MLOps Expert. You don't think so? In the following article, I can prove you wrong. #MLOps #DataScience #MachineLearning

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Hago muchos posts en LinkedIn, but… 𝐰𝐡𝐚𝐭 𝐜𝐚𝐧 𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨 𝐟𝐨𝐫 𝐲𝐨𝐮? 🤔 I’m a Data Scientist, and here’s how my team and I can help your company: -> 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬: 📊 Power BI or Tableau. We define scope, set limits, and agree on pricing upfront. -> 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: 🔄 Repeated tasks? We automate them. I’ll choose the best tech to streamline your workflows. -> 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 🤖 Want to explore AI but don’t know where to start? I’ll guide you with a custom plan and the right tools. If something already exists that fits your needs, I’ll tell you. _ _ _ Oh, and by the way—I’m fluent in English, Spanish, and French. 🌍 So no matter what language you speak, we’ll connect seamlessly. Ready to make a move? 🚀 Book a free 30-minute meeting with me via my Calendly link, and let’s talk about how to grow your business. 👉 Calendly: https://lnkd.in/ez8Y2D-4

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💡 Faster B-tree index scans in Postgres 17 for queries that involve IN lists or other cases where multiple array values are being passed to Postgres... - https://lnkd.in/dGzDNnSm

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The syntax is a little strange, but the KEEP clause is very useful whenever you are doing aggregation in SQL. https://lnkd.in/gc_8xpWr

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I recently started Chapter 1 of "Fluent Python" by Luciano Ramalho, and it's a game-changer, it really level up your python skills! 📚✨ 🔍 Key Takeaways: Pythonic Thinking: Embrace Python’s unique features and idioms to write cleaner, more efficient code. The Python Data Model: Leverage special methods (magic methods) like __init__, __str__, __len__, and __getitem__ to integrate seamlessly with Python’s built-in operations. Emulate Built-in Types: Create custom objects that behave like Python’s built-in types by implementing these special methods. Example in Action: By defining methods like __len__ and __getitem__, a simple card deck class can support len(deck) and indexing (deck[0]), making it intuitive and powerful. 💡 Tip: Understanding and implementing these special methods leads to more readable, maintainable, and efficient code 🌟 #Python #Coding #Programming #FluentPython #Learning #Tech #SoftwareDevelopment Code example of the learning material

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EN Hey guys, post I wrote about how to use PySpark with Airflow, I hope you like it! PT Hey guys, post que escrevi sobre como usar PySpark com Airflow, espero que gostem! https://lnkd.in/du9T7kGQ #dataengineering #datascience

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Meet the StarTree All-Stars: Mateus Henrique Cândido de Oliveira 🌟 Mateus is a data architect at One Way Solution. He has worked with data for 8+ years, developing streaming data pipelines for 3+ years, and has been a champion for the Apache Pinot community in Brazil! Learn more about Mateus, and let us know which movie his favorite quote is from: 𝗤: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗺𝗼𝘃𝗶𝗲 𝗾𝘂𝗼𝘁𝗲? A: “The body cannot live without the mind.” 𝗤: 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗣𝗶𝗻𝗼𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝘄𝗵𝘆? A: Ingestion, we can have both Streaming and Batching ingestion in a single place. 𝗤: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗿 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗽𝗿𝗼𝘂𝗱 𝗼𝗳? A: Kafka Summit APAC 2021 - Tim made a recap, and my session was highlighted in the blogpost of Confluent 𝗤: 𝗪𝗵𝗮𝘁'𝘀 𝘀𝗼𝗺𝗲 𝗮𝗱𝘃𝗶𝗰𝗲 𝗳𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗮𝗻 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆? A: Don’t worry if you can’t contribute with code, make content about Pinot and show the world this incredible technology #StarTree #Community #ApachePinot

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UniSuper's Google Cloud deletion incident has been traced back to a blank parameter in the setup. This crucial error led to the unintended loss of data, highlighting the importance of meticulous configuration in cloud management. Discover the full story and its implications for cloud users here: https://hubs.la/Q02zbJfw0 https://hubs.la/Q02zbJ2q0

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