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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | MIT Cambridge |
The Syllabus
- Module 1: Introduction to Data Science
- Module 2: Thinking about Risk and Uncertainty through Probability and Distributions
- Module 3: Correlation
- Module 4: Clustering
- Module 5: Linear Regression Part 1
- Module 6: Linear Regression Part 2
- Module 7: Logistic Regression
- Module 8: Collaborative Filtering
- Module 9: Optimization Part 1
- Module 10: Optimization Part 2
- Module 11: Optimization Part 3
- Module 12: Optimization Part 4
- Module 13: Optimization Part 5
- Module 14: Regression and Classification
- Module 15: Ensemble Models
- Module 16: Fairness and Bias Issues in Data-Driven Predictions
- Module 17: Neural Networks Part 1
- Module 18: Neural Networks Part 2
- Module 19: Neural Networks Part 3
- Module 20: Natural Language Processing (NLP) Part 1
- Module 21: Natural Language Processing (NLP) Part 2
- Module 22: Interpretability and Causality in Models
- Module 23: Data, Models, and Decisions
- Module 24: Leading Digital Transformations
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