Master Python, Prompt Engineering, and AI Code Generation to become a job-ready Data Mining professional.
Data Mining roles now expect more than just SQL and basic analytics—they demand professionals who can use Python, generative AI, and prompts to move faster from raw data to actionable insights. This course trains students to use Python, Prompt Engineering, and AI Code Generation to clean data, explore patterns, automate analysis, and communicate results for real business problems.
Learners start by strengthening Python fundamentals, then layer on generative AI tools that write and optimize code for data tasks, and finally master prompts that turn natural language questions into high-quality analysis workflows. By the end, students can apply these combined skills to Data Mining use cases that map directly to analyst, data scientist, and GenAI-enabled data roles in the job market.
Why this course is career-focused
• Teaches in-demand stack: Python + Data Mining + Generative AI + Prompt Engineering, now highlighted in many AI and data job descriptions.
• Oriented to hiring needs: Projects mimic day-to-day tasks like cleaning messy datasets, generating analysis code from prompts, and producing automated reports and dashboards.
• Job-ready portfolio: Capstone and mini-projects can be showcased for roles such as Data Analyst, Junior Data Scientist, GenAI Data Engineer, and AI-enabled Business Analyst.
**Modules are 3hr long spread over multiple days.
Module 1: Python for Data Mining Essentials
Covers core Python programming, data structures, and libraries like pandas and NumPy for data loading, cleaning, and basic exploration—setting the foundation for efficient data handling in mining tasks.
Module 2: Generative AI and Prompt Engineering Fundamentals
Introduces large language models (LLMs), prompt design principles (structure, context, roles), and techniques like chain-of-thought prompting to generate accurate text, code, and insights from AI systems.
Module 3: AI Code Generation for Data Tasks
Teaches integration of tools like GitHub Copilot and OpenAI APIs to automate Python code for data pipelines, ETL processes, and analysis scripts, with best practices for validation and optimization.
Module 4: Advanced Data Mining Techniques with AI
Explores pattern discovery, clustering, classification, and visualization using scikit-learn enhanced by AI-generated code and prompts—focusing on real datasets for predictive mining.
Module 5: Prompt Optimization and AI Workflow Automation
Focuses on iterative prompt refinement, evaluation metrics, multi-step reasoning, and building automated workflows that combine data mining with generative AI for scalable analysis.
Module 6: Capstone Projects and Job-Ready Applications
Students apply the full skill stack to end-to-end Data Mining projects, such as building AI-assisted dashboards and reports, with portfolio development for data analyst and mining roles.
