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Here are YouTube channels to learn Excel, Power BI & SQL: 1. EXCEL - MyOnlineTrainingHub - Leila Gharani 2. POWER BI - Guy in a Cube - Avi Singh 3. SQL - Programming with Mosh - Kevin Stratvert Who else belongs on this list?

Stephen | Data

1,702 subscribers

30,112 просмотров • 13 дней назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 8

Фото профиля Prachi Rajawat

Prachi Rajawat13 дней назад

Thanks for sharingg.

Фото профиля Bumble bee 🐝

Bumble bee 🐝13 дней назад

Stephen what solid SQL Project do you recommend doing?

Фото профиля Nnabuife Max Okondu

Nnabuife Max Okondu12 дней назад

Business Analyst

Фото профиля ᗩᗩ

ᗩᗩ12 дней назад

🤝

Фото профиля Idris Yusuff || Data Analysis Journey

Idris Yusuff || Data Analysis Journey12 дней назад

Thanks sir

Фото профиля Kaizen

Kaizen12 дней назад

Alex the Analyst

Фото профиля dummy tunnss

dummy tunnss12 дней назад

Can we use Linux as a Data analyst sir?

Фото профиля Edchin

Edchin12 дней назад

Thank you

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Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. 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You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. 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I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. 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Here is the (much interrupted) testimony of Moira Deeming MP about the behaviour toward her of Mr Guy and the pathetic & misogynistic Liberal Victoria, who can smell, but not yet taste power and chose to get rid of a woman, rather than deal with her ongoing safety concerns. 1/3

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Gary Vaynerchuk

24,023 просмотров • 2 лет назад

Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Rob Wiblin

48,437 просмотров • 1 год назад

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Zach Wilson

29,811 просмотров • 1 год назад

Here's my conversation with Pieter Levels (@levelsio), self-taught developer and entrepreneur who designed, programmed, shipped, and ran over 40 startups, many of which are hugely successful. In most cases, he did it all by himself, while living the digital nomad life in over 40 countries and over 150 cities. This was a fun and inspiring conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:03 - Startup philosophy 9:34 - Low points 13:03 - 12 startups in 12 months 19:55 - Traveling and depression 32:34 - Indie hacking 36:37 - Photo AI 1:12:53 - How to learn AI 1:21:30 - Robots 1:29:47 - Hoodmaps 1:53:52 - Learning new programming languages 2:03:24 - Monetize your website 2:09:59 - Fighting SPAM 2:13:33 - Automation 2:24:58 - When to sell startup 2:27:52 - Coding solo 2:33:54 - Ship fast 2:42:38 - Best IDE for programming 2:52:09 - Andrej Karpathy 3:01:34 - Productivity 3:15:21 - Minimalism 3:24:07 - Emails 3:31:20 - Coffee 3:39:05 - E/acc 3:41:21 - Advice for young people

Here's my conversation with Pieter Levels (@levelsio), self-taught developer and entrepreneur who designed, programmed, shipped, and ran over 40 startups, many of which are hugely successful. In most cases, he did it all by himself, while living the digital nomad life in over 40 countries and over 150 cities. This was a fun and inspiring conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:03 - Startup philosophy 9:34 - Low points 13:03 - 12 startups in 12 months 19:55 - Traveling and depression 32:34 - Indie hacking 36:37 - Photo AI 1:12:53 - How to learn AI 1:21:30 - Robots 1:29:47 - Hoodmaps 1:53:52 - Learning new programming languages 2:03:24 - Monetize your website 2:09:59 - Fighting SPAM 2:13:33 - Automation 2:24:58 - When to sell startup 2:27:52 - Coding solo 2:33:54 - Ship fast 2:42:38 - Best IDE for programming 2:52:09 - Andrej Karpathy 3:01:34 - Productivity 3:15:21 - Minimalism 3:24:07 - Emails 3:31:20 - Coffee 3:39:05 - E/acc 3:41:21 - Advice for young people

Lex Fridman

2,442,197 просмотров • 2 лет назад

If you make Power BI reports, I think you might enjoy stepping outside of Power BI to experiment with coding agents and dataviz libraries like Vega, Altair, or d3.js. Here's an example of a dashboard I made with FitBit data. While making the pbir-cli (testing starts this week, thanks to everyone who showed interest!), we learned a lot. Chiefly: we've found it's really, really hard to get an agent to work well with Power BI reports!! Power BI reports are built around abstraction and different intersecting layers. Themes, metadata, extensions, the model... all of it built for a low-code SSBI experience (clicky-clicky-draggy-droppy). That's fine for you and me (mostly). For a coding agent, though, it's inflexible... maybe even hostile. Even with the right tools and context, you simply can't get a Power BI report to reliably do _exactly_ what you want; it's just not meant for that. If you experiment a bit with Vega, Altair, or d3.js... you'll see what I mean; the contrast is pretty clear. This isn't necessarily a bad thing; unlimited flexibility may lead to inconsistency, and there's creativity in constraints. But still, I've been wanting to explore visualization without these constraints... My experiments beyond Power BI reports have been interesting and given me a lot to think about. The most promising approach so far: viz-as-code with custom embedded visuals, pulling from local or remote data sources. This stepcount dashboard is one such example... made in just a few hours between other tasks. Materializing a dashboard like this from natural language wasn't really viable a year ago. Project out another 12–18 months and it's certainly difficult to imagine what the future looks like... That said, reports and dashboards in an enterprise context aren't just visuals. Governance, distribution, integration, etc... years of individual and organizational investment. All of that makes change have a speed limit; we won't be plugging in custom embedded dashboards, overnight. But if you're curious, I do think it's a good investment to step outside Power BI and experiment. Investments you may even be turned into custom visual designs, anyway :). As Havens Consulting put it recently to me: it's time to multiclass...

If you make Power BI reports, I think you might enjoy stepping outside of Power BI to experiment with coding agents and dataviz libraries like Vega, Altair, or d3.js. Here's an example of a dashboard I made with FitBit data. While making the pbir-cli (testing starts this week, thanks to everyone who showed interest!), we learned a lot. Chiefly: we've found it's really, really hard to get an agent to work well with Power BI reports!! Power BI reports are built around abstraction and different intersecting layers. Themes, metadata, extensions, the model... all of it built for a low-code SSBI experience (clicky-clicky-draggy-droppy). That's fine for you and me (mostly). For a coding agent, though, it's inflexible... maybe even hostile. Even with the right tools and context, you simply can't get a Power BI report to reliably do _exactly_ what you want; it's just not meant for that. If you experiment a bit with Vega, Altair, or d3.js... you'll see what I mean; the contrast is pretty clear. This isn't necessarily a bad thing; unlimited flexibility may lead to inconsistency, and there's creativity in constraints. But still, I've been wanting to explore visualization without these constraints... My experiments beyond Power BI reports have been interesting and given me a lot to think about. The most promising approach so far: viz-as-code with custom embedded visuals, pulling from local or remote data sources. This stepcount dashboard is one such example... made in just a few hours between other tasks. Materializing a dashboard like this from natural language wasn't really viable a year ago. Project out another 12–18 months and it's certainly difficult to imagine what the future looks like... That said, reports and dashboards in an enterprise context aren't just visuals. Governance, distribution, integration, etc... years of individual and organizational investment. All of that makes change have a speed limit; we won't be plugging in custom embedded dashboards, overnight. But if you're curious, I do think it's a good investment to step outside Power BI and experiment. Investments you may even be turned into custom visual designs, anyway :). As Havens Consulting put it recently to me: it's time to multiclass...

Kurt Buhler

28,562 просмотров • 6 месяцев назад