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11 - Extra PostGreSQL with Python | 95 | 0 | 0 | 0 | Loading... | |
10 - Conditional Expressions and Procedures | 90 | 0 | 0 | 0 | Loading... | |
09 - Assessment Test 3 | 88 | 0 | 0 | 0 | Loading... | |
08 - Creating Databases and Tables | 88 | 0 | 0 | 0 | Loading... | |
07 - Assessment Test 2 | 86 | 0 | 0 | 0 | Loading... | |
06 - Advanced SQL Commands | 77 | 0 | 0 | 0 | Loading... | |
05 - JOINS | 73 | 0 | 0 | 0 | Loading... | |
04 - Assessment Test 1 | 67 | 0 | 0 | 0 | Loading... | |
03 - GROUP BY Statements | 67 | 0 | 0 | 0 | Loading... | |
02 - SQL Statement Fundamentals | 65 | 0 | 0 | 0 | Loading... | |
01 - Course Introduction | 64 | 0 | 0 | 0 | Loading... | |
 📖 The Complete SQL Bootcamp: Go from Zero to Hero
🌟 4.7 - 223280 votes 💰 Original Price: $74.99
📖 You'll learn how to read and write complex queries to a database using one of the most in demand skills - PostgreSQL. These skills are also applicable to any other major SQL database, such as MySQL, Microsoft SQL Server, Amazon Redshift, Oracle, and much more.
Learning SQL is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills! In this course you'll learn quickly and receive challenges and tests along the way to improve your understanding!
🔊 Taught By: Jose Portilla, Pierian Training
📤 Download Full Course 📤 Download All Courses | 66 | 0 | 0 | 0 | Loading... | |
 📖 Most important SQL commands
These key SQL commands are the basics you need to handle and organize data like a pro. | 55 | 0 | 0 | 0 | Loading... | |
📖 30 days roadmap to learn Python for Data Analysis 😄👇
Days 1-5: Introduction to Python
1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook).
2. Day 2-5: Learn Python basics (variables, data types, and basic operations).
Days 6-10: Control Flow and Functions
6. Day 6-8: Study control flow (if statements, loops).
9. Day 9-10: Learn about functions and modules in Python.
Days 11-15: Data Structures
11. Day 11-12: Explore lists, tuples, and dictionaries.
13. Day 13-15: Study sets and string manipulation.
Days 16-20: Libraries for Data Analysis
16. Day 16-17: Get familiar with NumPy for numerical operations.
18. Day 18-19: Dive into Pandas for data manipulation.
20. Day 20: Basic data visualization with Matplotlib.
Days 21-25: Data Cleaning and Analysis
21. Day 21-22: Data cleaning and preprocessing using Pandas.
23. Day 23-25: Exploratory data analysis (EDA) techniques.
Days 26-30: Advanced Topics
26. Day 26-27: Introduction to data visualization with Seaborn.
27. Day 28-29: Introduction to machine learning with Scikit-Learn.
30. Day 30: Create a small data analysis project.
Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems.
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