Advanced Data Science Using Python

Instructor-led 4-days

Course Description
This course builds advanced data science capability using Python. You work with real datasets, build statistical and machine learning models, and deploy results. Focus stays on performance, scalability, and reproducibility using modern Python tools such as pandas, NumPy, scikit-learn, and PySpark.

Course Objectives

• Build scalable data pipelines in Python 
• Apply statistical modeling and inference 
• Train and evaluate machine learning models 
• Work with large and unstructured datasets 
• Visualize and communicate insights 
• Deploy production-ready workflows 

Key Takeaways

• You write efficient Python code for data science 
• You manage large datasets and optimize performance 
• You build and validate predictive models 
• You automate workflows and pipelines 
• You present results clearly for decision making 

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Module 1: Advanced Python for Data Science

• Iterators, generators, decorators 
• Performance optimization
  Exercises 

1. Convert loops to generators 
2. Build decorators for logging 
3. Benchmark code performance 

Module 2: Data Wrangling at Scale

• pandas advanced operations 
• Efficient data structures
  Exercises 

4. Clean large dataset 
5. Perform complex joins 
6. Optimize memory usage 
7. Vectorize operations 

Module 3: Data Cleaning and Validation

• Handling missing and inconsistent data 
• Validation pipelines
  Exercises 

8. Detect anomalies 
9. Impute missing values 
10. Build validation checks 

Module 4: Exploratory Data Analysis

• Pattern detection 
• Statistical summaries
  Exercises 

11. Generate summary reports 
12. Identify correlations 
13. Detect outliers 

Module 5: Advanced Data Visualization

• matplotlib, seaborn, plotly 
• Interactive dashboards
  Exercises 

14. Build layered plots 
15. Create interactive visuals 
16. Design dashboard 

Module 6: Statistical Inference

• Hypothesis testing 
• Confidence intervals
  Exercises 

17. Run statistical tests 
18. Build confidence intervals 
19. Interpret results 

Module 7: Regression Modeling

• Linear and logistic regression 
• Diagnostics
  Exercises 

20. Fit regression models 
21. Check assumptions 
22. Improve model accuracy 

Module 8: Feature Engineering

• Encoding and transformations 
• Dimensionality reduction
  Exercises 

23. Create new features 
24. Apply PCA 
25. Evaluate feature importance 

Module 9: Machine Learning Foundations

• Supervised learning workflows 
• Model evaluation
  Exercises 

26. Train classification model 
27. Evaluate metrics 
28. Compare models 

Module 10: Advanced Machine Learning

• Random forest, gradient boosting, XGBoost 
• Hyperparameter tuning
  Exercises 

29. Train ensemble models 
30. Tune hyperparameters 
31. Compare performance 

Module 11: Unsupervised Learning

• Clustering 
• Association rules
  Exercises 

32. Perform k-means clustering 
33. Apply hierarchical clustering 
34. Generate rules 

Module 12: Time Series Analysis

• Forecasting 
• Seasonal decomposition
  Exercises 

35. Build ARIMA model 
36. Forecast trends 
37. Evaluate predictions 

Module 13: Text Mining and NLP

• Text preprocessing 
• Sentiment analysis
  Exercises 

38. Clean text data 
39. Perform sentiment analysis 
40. Build NLP pipeline 

Module 14: Big Data with Python

• PySpark 
• Parallel processing
  Exercises 

41. Process large dataset with Spark 
42. Run distributed tasks 
43. Optimize execution 

Module 15: Reproducible Workflows

• Jupyter, pipelines 
• Version control
  Exercises 

44. Build reproducible notebook 
45. Automate workflow 
46. Integrate version control 

Module 16: Deployment and Production

• APIs, Flask, FastAPI 
• Model deployment
  Exercises 

47. Build API for model 
48. Deploy model 
49. Monitor performance 
50. Package application 
51. End-to-end project deployment