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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf Study, Virtual ClassroomVideo and Text Based

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesMIT 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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