Unlock your potential as a Python AI & ML Engineer with our intensive 15-day workshop designed to provide you with unparalleled expertise in SQL, PostgreSQL, MySQL, and SQLite. This hands-on program not only covers the full spectrum of database management and optimization but also integrates these skills seamlessly with Python for advanced AI and ML applications.
Workshop Highlights:
- In-Depth Learning: Gain advanced skills in SQL, PostgreSQL, MySQL, and SQLite, tailored to the needs of AI and ML projects.
- Real-World Projects: Apply your knowledge to practical, industry-relevant projects, including customer churn prediction, sales reporting, and more.
- Python Integration: Master how to effectively integrate databases with Python, essential for robust AI and ML pipelines.
- Career Readiness: Prepare for top roles with insights into job descriptions, industry standards, and interview preparation.
- Expert Guidance: Benefit from instruction by seasoned professionals with extensive real-world experience.
- Networking Opportunities: Connect with peers and industry experts, and receive personalized career support and guidance.
Prerequisites:
- Python Programming: Proficiency in Python, including familiarity with libraries such as Pandas and NumPy.
- Basic SQL Knowledge: Understanding of fundamental SQL commands and database concepts.
- Basic Machine Learning Concepts: Awareness of basic ML concepts and techniques.
- Familiarity with Data Handling: Experience in handling and manipulating datasets.
Join us for this unique opportunity to advance your career and make a significant impact in the world of data and AI.
Table of Contents
- Workshop Overview
- Why Python AI & ML Engineers Should Attend
- Workshop Schedule
- Job Description (JD) and Career Planning for Python AI & ML Engineers
- How This Workshop Helps in Job Planning
Workshop Overview
This intensive 15-day workshop is meticulously designed for Python developers with a focus on AI and ML who aim to master SQL, PostgreSQL, MySQL, and SQLite. Unlike typical online courses, this workshop provides a hands-on learning experience with real-world projects, industry best practices, and personalized career support.
Objectives:
- Develop Advanced Skills: Gain expertise in SQL, PostgreSQL, MySQL, and SQLite, and integrate these skills with Python for effective AI and ML applications.
- Engage in Real-World Projects: Apply your knowledge through practical projects tailored to industry scenarios.
- Prepare for Top Roles: Equip yourself with the skills and insights needed to excel in high-impact roles within data management and AI/ML.
Why Python AI & ML Engineers Should Attend
- Industry-Relevant Skills:
- Cutting-Edge Curriculum: Learn advanced database concepts and integration techniques critical for modern AI/ML applications.
- Expert Guidance: Benefit from instruction by industry professionals with extensive real-world experience.
- Hands-On Learning Experience:
- Practical Projects: Work on industry-relevant projects, such as customer churn prediction and financial forecasting, to build your portfolio.
- Capstone Project: Demonstrate your ability to integrate SQL with Python in a comprehensive capstone project.
- Career Preparation:
- Job Descriptions Insight: Understand the key skills and responsibilities for SQL and AI/ML roles, helping you align your career objectives.
- Interview Readiness: Prepare for technical interviews with targeted practice and feedback.
- Networking and Career Advancement:
- Professional Connections: Network with peers, instructors, and industry professionals.
- Career Support: Receive guidance on resume building, job planning, and interview preparation.
Workshop Schedule
Days 1-10: Mastering SQL, PostgreSQL, and MySQL
Day 1: Introduction to SQL, PostgreSQL, and MySQL
- Topics Covered: Overview, basic commands (SELECT, INSERT, UPDATE, DELETE), database design.
- Practical Exercises: Set up PostgreSQL and MySQL; create and manage tables.
- Explanation: Fundamentals of database functionality and design principles.
- LeetCode/HackerRank Practice: Basic SQL operations.
- Best Practices: Naming conventions, data integrity.
- Real-World Project: Inventory Management System.
Day 2: Advanced SQL Queries and Database Features
- Topics Covered: Joins, Unions, Subqueries; advanced features in PostgreSQL and MySQL.
- Practical Exercises: Writing complex queries and exploring advanced features.
- Explanation: Advanced data retrieval and analysis.
- LeetCode/HackerRank Practice: Advanced query challenges.
- Best Practices: Query optimization techniques.
- Real-World Project: Sales Reporting System.
Day 3: Data Modeling and Integrity
- Topics Covered: Normalization, constraints (Primary keys, Foreign keys), relationships.
- Practical Exercises: Designing normalized schemas and enforcing constraints.
- Explanation: Efficient data modeling for reliability.
- LeetCode/HackerRank Practice: Data modeling exercises.
- Best Practices: Avoiding redundancy, maintaining consistency.
- Real-World Project: CRM System.
Day 4: Data Manipulation and Transactions
- Topics Covered: Data manipulation operations; Transactions and ACID properties.
- Practical Exercises: Implementing transactions and performing data manipulation.
- Explanation: Data consistency during complex operations.
- LeetCode/HackerRank Practice: Transaction management exercises.
- Best Practices: Effective transaction management.
- Real-World Project: Banking Transaction System.
Day 5: Performance Optimization
- Topics Covered: Indexing, Query optimization, Performance analysis.
- Practical Exercises: Applying indexing and optimization techniques.
- Explanation: Enhancing database performance for large datasets.
- LeetCode/HackerRank Practice: Query optimization challenges.
- Best Practices: Performance tuning.
- Real-World Project: E-Commerce Product Catalog.
Day 6: SQL Integration with Machine Learning
- Topics Covered: Feature engineering, dataset preparation, SQL in ML pipelines.
- Practical Exercises: Preparing data for ML models; developing basic ML models.
- Explanation: Improving ML model accuracy through effective data preparation.
- LeetCode/HackerRank Practice: Data preparation exercises.
- Best Practices: Clean, relevant data for ML models.
- Real-World Project: Customer Churn Prediction Model.
Day 7: Exploratory Data Analysis (EDA)
- Topics Covered: Data exploration with SQL and Python; Visualization techniques.
- Practical Exercises: Conducting EDA; creating visualizations.
- Explanation: Uncovering data patterns and insights.
- LeetCode/HackerRank Practice: EDA exercises.
- Best Practices: Visualization tools for comprehensive analysis.
- Real-World Project: Customer Behavior Analysis.
Day 8: Advanced SQL Techniques and Data Warehousing
- Topics Covered: Window functions, CTEs, Data warehousing concepts.
- Practical Exercises: Utilizing advanced SQL features; exploring data warehousing.
- Explanation: Streamlining complex data analysis and reporting.
- LeetCode/HackerRank Practice: Advanced SQL features exercises.
- Best Practices: Data warehousing for large-scale data.
- Real-World Project: Financial Forecasting System.
Day 9: Capstone Project – SQL for AI & ML Use Cases
- Topics Covered: Integration of SQL with AI/ML; Capstone project objectives.
- Practical Exercises: Completing a comprehensive project integrating SQL and Python.
- Explanation: Demonstrating practical application of skills.
- LeetCode/HackerRank Practice: Capstone project challenges.
- Best Practices: Impactful integration and solution effectiveness.
- Real-World Project: AI-Powered Customer Segmentation System.
Day 10: Review and Advanced Topics
- Topics Covered: Best practices, advanced features, review.
- Practical Exercises: Consolidating learning, reviewing key techniques.
- Explanation: Preparing for advanced roles and projects.
- LeetCode/HackerRank Practice: Review exercises.
- Best Practices: Staying updated with industry trends.
- Real-World Project: Unified Analytics Dashboard.
Days 11-15: SQLite Integration and Advanced Topics
Day 11: Introduction to SQLite
- Topics Covered: Overview, basic commands, SQLite features.
- Practical Exercises: Setting up SQLite; creating and managing databases.
- Explanation: SQLite for lightweight, embedded applications.
- LeetCode/HackerRank Practice: SQLite basics exercises.
- Best Practices: Effective use of SQLite.
- Real-World Project: Local Data Storage Application.
Day 12: Advanced SQLite Features
- Topics Covered: Indexing, optimization, transactions, complex queries.
- Practical Exercises: Optimizing SQLite databases and queries.
- Explanation: Enhancing SQLite performance and efficiency.
- LeetCode/HackerRank Practice: Advanced SQLite challenges.
- Best Practices: Performance optimization.
- Real-World Project: Mobile App Data Management System.
Day 13: Integrating SQLite with Python
- Topics Covered: SQLite with Python; CRUD operations; Exception handling.
- Practical Exercises: Developing Python applications with SQLite.
- Explanation: Supporting lightweight data management in Python applications.
- LeetCode/HackerRank Practice: Python-SQLite integration exercises.
- Best Practices: Exception handling and data management.
- Real-World Project: Python Data Reporting Tool.
Day 14: SQLite for Machine Learning and AI
- Topics Covered: Using SQLite data for ML; Feature engineering and model training.
- Practical Exercises: Developing ML models using SQLite data.
- Explanation: Leveraging SQLite data in ML workflows.
- LeetCode/HackerRank Practice: ML with SQLite exercises.
- Best Practices: Ensuring data quality for ML applications.
- Real-World Project: Predictive Maintenance System.
Day 15: Capstone Project and Review
- Topics Covered: Final capstone project, review best practices.
- Practical Exercises: Completing a project integrating all databases and Python.
- Explanation: Showcasing practical skills and knowledge.
- LeetCode/HackerRank Practice: Capstone project exercises.
- Best Practices: Adhering to industry standards.
- Real-World Project: Unified Solution Integrating SQL, PostgreSQL, MySQL, and SQLite for a Python-Based AI/ML Application.
Job Description (JD) and Career Planning for Python AI & ML Engineers
Job Description: SQL and Database Developer for AI & ML
Role Overview: SQL and Database Developers for AI & ML design and manage databases to support machine learning and AI initiatives. Responsibilities include integrating databases with Python, optimizing database performance, and ensuring data quality.
Key Responsibilities:
- Database Management: Design, manage, and optimize SQL, PostgreSQL, MySQL, and SQLite databases.
- Query Optimization: Develop and refine complex queries to meet AI/ML requirements.
- Data Integration: Collaborate with data scientists to integrate databases with Python and machine learning models.
- Performance Tuning: Implement indexing and optimization techniques to enhance database performance.
- Feature Engineering: Prepare data for machine learning models, ensuring data integrity and relevance.
- Collaboration: Work with cross-functional teams to support data-driven projects.
Required Skills:
- Database Expertise: Proficiency in SQL, PostgreSQL, MySQL, and SQLite.
- Python Programming: Strong skills in Python for data manipulation and database integration.
- Machine Learning Knowledge: Understanding of ML concepts and experience with data preparation.
- Optimization Skills: Experience in database performance tuning.
- Communication: Effective collaboration and presentation skills.
Preferred Qualifications:
- Experience: Relevant experience in database management for AI/ML applications.
- Certifications: Certifications in SQL and database technologies.
- Projects: Experience with real-world AI/ML and database integration projects.
How This Workshop Helps in Job Planning
- Comprehensive Skill Development:
- Advanced Database Skills: Acquire in-depth knowledge of SQL, PostgreSQL, MySQL, and SQLite, essential for data management roles.
- Python Integration: Learn to integrate databases with Python, a critical skill for AI/ML positions.
- Real-World Project Experience:
- Hands-On Projects: Work on practical projects that align with industry requirements, enhancing your resume and portfolio.
- Career Preparation:
- Job Descriptions Insight: Understand the specific skills and responsibilities for SQL and AI/ML roles, helping you target your job search effectively.
- Interview Preparation: Gain experience through mock interviews and technical exercises tailored to industry expectations.
- Networking and Support:
- Professional Connections: Engage with industry experts and peers, expanding your professional network.
- Career Assistance: Receive personalized feedback on your resume, LinkedIn profile, and job search strategy.
- Tailored Guidance:
- Job Planning: Get customized advice on job planning and career advancement, ensuring you are well-prepared for the next step in your career.
This workshop is designed to provide you with the advanced skills, practical experience, and career support necessary to excel as a Python AI & ML Engineer. Join us to take your career to the next level and stand out in the competitive job market.