Last project related to programming and data analysis:

• Use Python and SQL together to perform data analysis, and build data-driven applications.

Breakdown of the essential skills:

Python Skills:

Basic Python Programming:

• Understanding of Python syntax and basic constructs (variables, loops, functions, etc.).
• Familiarity with data structures like lists, dictionaries, and tuples.

Libraries for Database Interaction:

• SQLite: sqlite3 module.
• MySQL: mysql-connector-python or PyMySQL.
• PostgreSQL: psycopg2.
• SQLAlchemy: An ORM (Object Relational Mapper) for Python.

Data Manipulation and Analysis:

• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Matplotlib/Seaborn: For data visualization.

File Handling:

• Reading from and writing to CSV, Excel, and other file formats.

Exception Handling:

• Managing exceptions and errors that occur during database operations.

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SQL Skills:

Basic SQL Commands:

• SELECT, INSERT, UPDATE, DELETE.
• CREATE, ALTER, DROP tables.

Advanced SQL Queries:

• Joins (INNER, LEFT, RIGHT, FULL).
• Subqueries and nested queries.
• Aggregate functions (SUM, AVG, COUNT, etc.).
• Grouping and sorting results.

Database Design:

• Normalization and database schema design.
• Understanding primary keys, foreign keys, and indexes.

Transaction Management:

• BEGIN, COMMIT, ROLLBACK.

Stored Procedures and Functions:

• [80%] Creating and using stored procedures and user-defined functions.

–

Integrating Python with SQL:

Connecting to a Database:

• Establishing a connection to the database from Python.
• Understanding connection parameters (host, port, database name, user, password).

Executing SQL Queries from Python:

• Using cursor objects to execute SQL commands.
• Fetching results from executed queries (e.g., fetchone(), fetchall()).

Data Insertion and Retrieval:

• Inserting data into tables from Python.
• Retrieving and processing data within Python.

Data Cleaning and Transformation:

• Cleaning and transforming data before inserting it into the database.
• [70%] Using Python for ETL (Extract, Transform, Load) processes.

Automating Database Tasks:

• Automating routine database tasks and maintenance using Python scripts.

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Practical Application:

Building a Sample Project:

• Combining Python and SQL skills to build a sample project, such as a simple web application, data analysis tool, or a data pipeline. (so far 1 project: EV market in Germany and the US)

Debugging and Optimization:

• [%50] Debugging database connectivity issues.
• Optimizing SQL queries for better performance.

Example Workflow:

• Connect to Database:

import sqlite3
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

• Create a Table:

cursor.execute('''
CREATE TABLE IF NOT EXISTS employees (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    salary REAL
)
''')
conn.commit()

• Insert Data:

cursor.execute('''
INSERT INTO employees (name, salary) VALUES (?, ?)
''', ("John Doe", 60000))
conn.commit()

• Fetch Data:

cursor.execute('SELECT * FROM employees')
rows = cursor.fetchall()
for row in rows:
    print(row)

• Close Connection:

    conn.close()

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• mastering these skills
• mastering these skills not yet