Data Science

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📂 Full description In this course, Lachezar Arabadzhiev provides an introduction to the world of data visualization using Canva and Flourish, and explains why visualization is critical to the big picture of product design. Lachezar shows you: how Canva's tools can be leveraged for data sharing and storytelling; how Flourish can add richness and context to data visualization; the key types of data visualization for designers and product teams; how to build a story around your data that gives the bigger context; and how to structure your narrative to reach the right audience. Whether youre a designer, creative, marketing pro to convey rich stories to accompany your data analysis, or an experienced Canva user who wants to be more prolific in business scenarios, check out this course to get started creating data visualizations.

🔅 Data Visualization with Canva and Flourish 🌐 Author: Lachezar Arabadzhiev 🔰 Level: Beginner + Intermediate ⏰ Duration: 30m

🌀 Get an introduction to the world of data visualization using Canva and Flourish, and learn why visualization is critical to the big picture of product design.

📗 Topics: Data Visualization, Canva 📤 Join Data Analysis for more courses

📖 Types of Database

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📖 Data Engineering Roadmap 2025 𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 (𝗔𝗪𝗦 𝗥𝗗𝗦, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟, 𝗔𝘇𝘂𝗿𝗲 𝗦𝗤𝗟) 💡 Why? Cloud-managed databases are the backbone of modern data platforms. ✅ Serverless, scalable, and cost-efficient ✅ Automated backups & high availability ✅ Works seamlessly with cloud data pipelines 𝟮. 𝗱𝗯𝘁 (𝗗𝗮𝘁𝗮 𝗕𝘂𝗶𝗹𝗱 𝗧𝗼𝗼𝗹) – 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝗟𝗧 💡 Why? Transform data inside your warehouse (Snowflake, BigQuery, Redshift). ✅ SQL-based transformation – easy to learn ✅ Version control & modular data modeling ✅ Automates testing & documentation 𝟯. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 – 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 💡 Why? Automate and schedule complex ETL/ELT workflows. ✅ DAG-based orchestration for dependency management ✅ Integrates with cloud services (AWS, GCP, Azure) ✅ Highly scalable & supports parallel execution 𝟰. 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 – 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗖𝗜𝗗 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 💡 Why? Solves data consistency & reliability issues in Apache Spark & Databricks. ✅ Supports ACID transactions in data lakes ✅ Schema evolution & time travel ✅ Enables incremental data processing 𝟱. 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 (𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲, 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, 𝗥𝗲𝗱𝘀𝗵𝗶𝗳𝘁) 💡 Why? Centralized, scalable, and powerful for analytics. ✅ Handles petabytes of data efficiently ✅ Pay-per-use pricing & serverless architecture 𝟲. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 – 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 💡 Why? For real-time event-driven architectures. ✅ High-throughput 𝟳. 𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗦𝗤𝗟 – 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 💡 Why? Every data engineer must master these! ✅ SQL for querying, transformations & performance tuning ✅ Python for automation, data processing, and API integrations 𝟴. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 – 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜 💡 Why? The go-to platform for big data processing & machine learning on the cloud. ✅ Built on Apache Spark for fast distributed computing

📖 SQL Data Types

📖 SQL Interview Questions asked In FAANG💯

📖 SQL Interview Questions asked In FAANG💯

📖 SQL Interview Questions asked In FAANG💯

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📖 Data Science Roles Explained

📖 SQL For Everything

📖 Data Science Roadmap

Ever wondered what the difference is between a Data Analyst and a Data Scientist? Both roles are in high demand, but they tackle data in different ways.

📖 Top Tools To Learn Data Engineering

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