📖 SQL Learning Roadmap — 8 Key Steps:

1. Basic:
Start by understanding what SQL is, why it’s used, and the different types of SQL commands (DDL, DML, DCL, TCL). This builds your foundation.

2. Queries:
Learn how to fetch data using commands like SELECT, FROM, WHERE, ORDER BY, and LIMIT. Practice filtering data with operators such as =, !=, LIKE, IN, and BETWEEN.

3. Joins:
To work with multiple tables, you must understand INNER, LEFT, RIGHT, and FULL OUTER JOIN. Know how primary and foreign keys relate tables.

4. Functions:
Use built-in functions like COUNT, SUM, AVG, MIN, and MAX for data analysis. Learn how to group data using GROUP BY and filter groups with HAVING.

5. Subqueries:
Write queries within queries! Learn scalar, correlated, and multi-row subqueries. They’re powerful for solving complex data problems.

6. Data Manipulation:
Master how to change data using INSERT, UPDATE, and DELETE. Also, understand transactions using BEGIN, COMMIT, and ROLLBACK to maintain data integrity.

7. Advanced:
Take it further with window functions (ROW_NUMBER, RANK, LEAD, LAG), CTEs (WITH), views, and indexing to write efficient and optimized queries.

8. Practice:
The final step is consistent practice. Work with real-world datasets, focus on query optimization, and solve challenges on platforms like LeetCode, Mode, or HackerRank.

🔅 Ethical Hacking: SQL Injection

📝 Learn about the SQL command language and SQL injections. Examine SQL injections in MySQL, SQL Server, and Oracle XE, and discover how attackers defeat web application firewalls.

🌐 Author: Malcolm Shore
🔰 Level: Intermediate
⏰ Duration: 1h 45m

📋 Topics: Ethical Hacking, SQL Injection

🔗 Join Data Science for more courses

📱Data Science
📱Ethical Hacking: SQL Injection

📖 Merging and Joining Data

Working with multiple datasets? Combine them just like SQL:

# Inner join (default)
merged = pd.merge(df_sales, df_customers, on='customer_id')

# Left join
pd.merge(df_sales, df_customers, on='customer_id', how='left')

# Concatenate vertically
all_data = pd.concat([df_2023, df_2024], ignore_index=True)

# Join on index
df1.join(df2, on='date')

This wraps up our Data Manipulation Using Pandas Series.

🖥 Data Analyst Roadmap

😉 A list of the best YouTube videos
✅ To learn data science


1️⃣ SQL language


⬅️ Learning

💰 4-hour SQL course from zero to one hundred

💰 Window functions tutorial

⬅️ Projects

📎 Starting your first SQL project

💰 Data cleansing project

💰 Restaurant order analysis

⬅️ Interview

💰 How to crack the SQL interview?

➖➖➖

2️⃣ Python


⬅️ Learning

💰 12-hour Python for Data Science course

⬅️ Projects

💰 Python project for beginners

💰 Analyzing Corona Data with Python

⬅️ Interview

💰 Python interview golden tricks

💰 Python Interview Questions

➖➖➖

3️⃣ Statistics and machine learning


⬅️ Learning

💰 7-hour course in applied statistics

💰 Machine Learning Training Playlist

⬅️ Projects

💰 Practical ML Project

⬅️ Interview

💰 ML Interview Questions and Answers

💰 How to pass a statistics interview?

➖➖➖

4️⃣ Product and business case studies


⬅️ Learning

💰 Building strong product understanding

💰 Product Metric Definition

⬅️ Interview

💰 Case Study Analysis Framework

💰 How to shine in a business interview?

🔅 How To Be a Lead Data Scientist

📝 Build a foundation and develop skills for seasoned data scientists to level up from model builders to AI leaders.

🌐 Author: Matthew Blasa
🔰 Level: Advanced
⏰ Duration: 1h 6m

📋 Topics: Data Science, Team Management

🔗 Join Data Science for more courses

📱Data Science
📱How To Be a Lead Data Scientist