📖 Learn Database

Databases power everything from websites and apps to enterprise systems. Here’s a learning map that can help you master databases:

1 - Database Fundamentals
This includes topics like “What is a database”, RDBMS, SQL vs NoSQL, ACID vs BASE, OLTP vs OLAP, Transactions, and Isolation Levels.

2 - Data Models and Types
Consists of topics like Relational Databases, Non-Relational Databases, and Data Types (Integer, String, Boolean, Date, JSON, etc).

3 - Querying and Language
This includes topics like SQL Basics (SELECT, INSERT, etc), Advanced SQL (Views, Indexes, CTEs, etc), and NoSQL Querying (Aggregation and Key-Value Lookups).

4 - Indexing and Optimization
Consists of topics like Indexing (B-Tree, Hash, and Bitmaps), Query Execution Plans, Denormalization vs Normalization, Sharding, Connecting Pooling, and Query Batching.

5 - Security, Backups, and Scaling
This includes topics like User Roles, Permissions, Encryption, SQL Injection, High Availability (Replication and Failover), Horizontal vs Vertical Scaling.

6 - Tools and Ecosystem
Consists of topics like Popular SQL Databases, NoSQL Database, GUI Tools, ORMs, Cloud DB services (RDS, DynamoDB, Google Cloud SQL, etc.)

🖥 8 Common database types explained

🔅 Decision Intelligence: Data Stories

📝 Learn how to use key lessons from famous data stories around the world to improve decision-making, interpret data effectively, and communicate insights responsibly.

🌐 Author: Franz Buscha
🔰 Level: Beginner
⏰ Duration: 45m

📋 Topics: Data Science, Decision Sciences, Data-driven Decision Making

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📱Data Science
📱Decision Intelligence: Data Stories

🔰 📙 Python Data Science Handbook 2nd Edition

🔄 Life Cycle of a Data Analytical Project

🔅 Data Science Reporting with Quarto for Python

📝 Leverage the power of Quarto to build publication-quality reports, engaging presentation decks, and rich interactive webpages from Jupyter Notebook for Python.

🌐 Author: Charlie Joey Hadley
🔰 Level: Intermediate
⏰ Duration: 2h 26m

📋 Topics: Data Reporting, Data Science, Data Analytics

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📱Data Science
📱Data Science Reporting with Quarto for Python

📖 Data Structures, you need to know for Coding interview

🔢 Data Cleaning Tips Every Analyst Should Know

If your analysis feels off, it’s probably your data.

These 5 tips will help you clean your dataset like a pro:
✔️ Handle missing values
✔️ Remove duplicates
✔️ Fix data types
✔️ Standardize formats
✔️ Detect and remove outliers

Clean data = better insights = better decisions.

🔅 Advanced Python: Top Tools for Data Science and Engineering

📝 This comprehensive course is designed to equip you with the essential skills for data analysis and application development using Python and popular data tools and libraries.

🌐 Author: Joe Marini
🔰 Level: Intermediate
⏰ Duration: 2h 5m

📋 Topics: Pandas, Data Engineering, Data Science

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📱Data Science
📱Advanced Python: Top Tools for Data Science and Engineering

📖 Data Structure Cheat Sheet

📋 Checklist to become Data Analyst

🔅 From Excel to SQL

📝 As big data gets bigger, it's useful to be able to pull insights directly from MySQL. Learn how to apply your knowledge of Excel to gain new skills at capturing data from MySQL.

🌐 Author: James Parkin
🔰 Level: Beginner
⏰ Duration: 1h 24m

📋 Topics: SQL, Microsoft Excel

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📱Data Science
📱From Excel to SQL