Data Science and Engineering 2:

DSE 2: Modeling and Decision Making

This is the second foundational course in data science. It It follows AI 1 and DSE 1 and builds on them in multiple directions: (a) It develops your ability to use generative models and clustering (b) we introduce the support vector machine, tree based models, ensemble models, and boosting (c) It introduces you to bayesian models (d) and culminates with decision theory and model interrogation, and how to deal with imbalanced data situations.

  • Know This First (or testing out of it)
  • DSE 1
  • AI 1
  • generative models such as LDA and naive bayes
  • clustering and k-means
  • Basic bayesian modeling and hierarchical thinking
  • SVM, the hinge loss, and kernel models
  • decision trees
  • bootstrap and bagging, random forests
  • boosting
  • data imbalance and decision making in the context of the above models
  • model interrogation and visualization

After you finish this module, you will be ready to run complex data science analyses on all kinds of data on your own, all the way from fetching and cleaning the data, setting up pipelines for them, to finding the best model, to dealing with data imbalance, and finally being able to make decisions with the models.

Prerequisites

Topics

Learning Objectives

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Schedule

Students

Summer Cohort

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Conducted alongside elective

Winter Cohort

Intake 1:

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Conducted along with elective

Intake 2:

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Conducted along with elective

Working Professionals

Summer Cohort

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Conducted along with elective

Fall Cohort

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective

Winter Cohort

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective