Data Science

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🔅 SQL Practice: Basic Queries 📝 Practice writing basic queries in SQL in this hands-on, interactive course with coding challenges in CoderPad. 🌐 Author: David Gassner 🔰 Level: Beginner ⏰ Duration: 17m 📋 Topics: SQL 🔗 Join Data Analysis for more courses

📖 Roles and Responsibilities in Big Data Technology

📊 9 Key Database Types 🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial). 🔗 Blockchain: Secure, immutable ledgers (BigchainDB, IBM Blockchain). 🌐 Distributed: Scales across servers (Cassandra, Amazon DynamoDB). ⚡️ In-Memory: Lightning-fast access (Redis, Memcached, H2). 🗂 NoSQL: Flexible, schema-free (MongoDB, Couchbase, HBase). 📋 Relational: Structured with tables & SQL (MySQL, PostgreSQL, Oracle). 🧩 Object-Oriented: Models complex objects (db4o, Object DB). 🕸 Graph: Perfect for relationships (Neo4j, Amazon Neptune). ⏱️ Time-Series: Optimized for timestamps (InfluxDB, Prometheus). Pick the right tool for your data challenge.

🔅 NoSQL Essential Training 📝 Get a high-level view of the basics of NoSQL, from how it differs from relational databases to its pros and cons. 🌐 Author: Melanie McGee 🔰 Level: Beginner ⏰ Duration: 43m 📋 Topics: NoSQL 🔗 Join Data Analysis for more courses

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📖 Data Visualization CheatSheet

🔅 Learning Apache Airflow 📝 Get an introduction to Apache Airflow—its uses, structure, how to get it up and running, and how to create and execute workflows. 🌐 Author: Janani Ravi 🔰 Level: Advanced ⏰ Duration: 2h 10m 📋 Topics: Apache Airflow, IT Automation 🔗 Join Data Analysis for more courses

Here’s a practical, code-first playbook for exploring numerical data 👇 How we approach EDA (with Python code + outputs): - Basics: shape, dtypes, and missing values. - Descriptives: mean/median, variance, and percentiles for quick sanity checks. - Distributions: histograms, boxplots, density to spot skew and spread. - Relationships: scatter plots and a correlation heatmap to find signals. - Outliers: z-scores/IQR to flag anomalies worth investigating. - Scaling: MinMax vs Z-score depending on the model and metric. - Segments: groupby comparisons to surface patterns you miss in globals. - Decisions: tie insights back to the question and next steps. What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.

📁 Master Excel like a pro Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost your productivity!

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📖 SQL Joins