Empowering Data Analysis And Manipulation

1. Empowering
Data Analysis and Manipulation

2. Introduction to SQL and Python
This presentation introduces SQL and Python, and demonstrates how
combining the two can empower data analysis and manipulation. The
presentation reviews the overview of SQL, introduction to Python,
combining SQL and Python, use cases, benefits, challenges and
considerations, and conclusion. The presentation emphasizes the importance
of combining SQL and Python for efficient data analysis and increased
flexibility through a simple example code snippet.

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Overview of SQL
Introduction to Python
Conclusion
Use Cases
Agenda
Combining SQL and Python

4. Overview of SQL
● Definition: Structured Query Language
● Purpose: Database management and manipulation
● Key Concepts: SELECT, FROM, WHERE, JOIN, GROUP BY

5. Introduction to Python
Brief Overview
● Generalpurpose
● Highlevel
● Interpreted

6. Python is widely used in data
science and analytics.
Its vast range of libraries such as
NumPy, Pandas, and Matplotlib
make it an excellent choice for
scientific computing.
Data Science and Analytics with Python
The Python ecosystem also
includes popular tools like Jupyter
Notebook and Spyder, which are
great for data analysis and
visualization.

7. 01
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Basic Python Code Example
Here is a basic example of a Python program:
print("Hello, World!")
Python code is easy to read and has a straightforward
syntax.

8. Combining SQL and Python
Why Combine?
● SQL for data querying
● Python for data analysis and
visualization
● SQL in Python (e.g., SQLite3)

9. Example of combining SQL and Python using the SQLite3.

10. Use Cases
● Data Analysis
● Retrieving data with SQL
● Analyzing and visualizing with Python
● Machine Learning
● SQL for data preprocessing
● Python for model training and evaluation

11. 01
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Data Security
Performance Issues
Compatibility
Best Practices
Challenges

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Conclusion
SQL and Python integration offers a powerful
combination for data manipulation and analysis.
SQL excels at querying databases, while Python
provides a versatile environment for data science tasks.
Experiment with combining SQL and Python in your
projects to enhance your data processing workflows.

13. Thank you for your time 😊