Advanced Data Science Automation Using Python

Instructor-led 4-days

Course Description
This course focuses on automating end to end data science workflows using Python. You build pipelines that handle data ingestion, cleaning, modeling, evaluation, and deployment with minimal manual work. Emphasis stays on orchestration, reproducibility, monitoring, and scaling using tools such as Airflow, Prefect, scikit-learn pipelines, and MLflow.

Course Objectives

• Automate full data science workflows 
• Build reusable and modular pipelines 
• Schedule and orchestrate data tasks 
• Track experiments and models 
• Deploy and monitor automated systems 
• Reduce manual intervention in data processes 

Key Takeaways

• You design automated pipelines from raw data to deployment 
• You reduce repetitive tasks using code and orchestration tools 
• You manage model lifecycle with tracking and versioning 
• You build systems that run on schedules and triggers 
• You monitor performance and handle failures 

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Module 1: Python Automation Foundations

• Scripting and task automation 
• File and process handling
  Exercises 

1. Automate file ingestion 
2. Schedule script execution 
3. Log task outputs 

Module 2: Data Pipeline Design

• Pipeline architecture 
• Modular workflow design
  Exercises 

4. Design pipeline structure 
5. Break workflow into reusable steps 
6. Build modular pipeline 

Module 3: Automated Data Ingestion

• APIs, databases, streaming
  Exercises 

7. Pull data from API 
8. Ingest database records 
9. Automate data refresh 

Module 4: Automated Data Cleaning

• Cleaning pipelines 
• Rule-based transformations
  Exercises 

10. Build cleaning pipeline 
11. Handle missing data automatically 
12. Standardize formats 

Module 5: Data Validation and Quality Checks

• Validation frameworks 
• Data integrity rules
  Exercises 

13. Create validation checks 
14. Detect anomalies 
15. Trigger alerts on failure 

Module 6: Automated EDA and Reporting

• Auto-generated reports 
• Profiling tools
  Exercises 

16. Generate EDA report 
17. Summarize dataset automatically 
18. Export insights 

Module 7: Feature Engineering Pipelines

• Automated transformations 
• Feature stores basics
  Exercises 

19. Build feature pipeline 
20. Automate feature scaling 
21. Store reusable features 

Module 8: Model Training Automation

• Training pipelines 
• Batch training
  Exercises 

22. Train model pipeline 
23. Automate retraining 
24. Save trained models 

Module 9: Model Evaluation Automation

• Metrics tracking 
• Validation workflows
  Exercises 

25. Evaluate model automatically 
26. Compare multiple models 
27. Generate evaluation reports 

Module 10: Hyperparameter Tuning Automation

• Grid search, random search 
• Optimization frameworks
  Exercises 

28. Automate tuning process 
29. Compare tuning strategies 
30. Save best model 

Module 11: Workflow Orchestration

• Airflow, Prefect 
• Task scheduling
  Exercises 

31. Build DAG pipeline 
32. Schedule workflow 
33. Handle task dependencies 

Module 12: Experiment Tracking and Versioning

• MLflow, logging 
• Model registry
  Exercises 

34. Track experiments 
35. Version models 
36. Log metrics and artifacts 

Module 13: Automated Deployment

• CI CD basics 
• API deployment
  Exercises 

37. Deploy model as API 
38. Automate deployment pipeline 
39. Update models without downtime 

Module 14: Monitoring and Alerting

• Performance monitoring 
• Drift detection
  Exercises 

40. Monitor model performance 
41. Detect data drift 
42. Trigger alerts 

Module 15: Scaling and Distributed Automation

• Parallel processing 
• Cloud integration
  Exercises 

43. Run parallel tasks 
44. Scale pipeline on cloud 
45. Optimize execution time 

Module 16: End to End Automation Project

• Full pipeline integration 
• Production readiness
  Exercises 

46. Build end to end pipeline 
47. Automate full workflow 
48. Deploy system 
49. Monitor pipeline 
50. Handle failures 
51. Final project presentation