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Foundations of Data Science

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Foundations of Data Science — Spring 2016

Instructor: John DeNero
Co-instructors: Ani Adhikari, Michael I. Jordan, Tapan Parikh, and David Wagner
MWF 10-11 in 155 Dwinelle Hall

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Syllabus

  • Data Science An overview of data science

    • 1 Why Data Science?

    • 2 Cause and Effect

    • 3 New Lesson

  • Tables Using Python to manipulate information

    • 1 Expressions

    • 2 Sequences

    • 3 Data Sets

    • 4 Tables

    • 5 Functions

    • 6 Categories

  • Visualization Interpreting and exploring data through visualizations

    • 1 Charts

    • 2 Histograms

  • Sampling Understanding the behavior of random selection

    • 1 Sampling

    • 2 Iteration

    • 3 Estimation and Means

    • 4 Variability

  • Prediction Making predictions from data

    • 1 Correlation

    • 2 Explorations: Privacy

    • 3 Regression

    • 4 Prediction

    • 5 Explorations: Design and Critique

    • 6 Errors

    • 7 Multiple Regression

    • 8 Classification

    • 9 Explorations: Machine Learning

    • 10 Midterm

    • 11 Feature Selection

  • Inference Reasoning about populations by computing over samples

    • 1 Confidence Intervals

    • 2 Percentiles

    • 3 Distance Between Distributions

    • 4 Hypothesis Testing

    • 5 Hypothesis Testing II

    • 6 Permutation Tests

    • 7 Implementing Permutation Tests

    • 8 A/B Testing

    • 9 Regression Inference

    • 10 Slope Inference

    • 11 Regression Diagnostics

  • Probability Making assumptions and exploring their consequences

    • 1 Probability

    • 2 Conditional Probability

  • Conclusion

    • 1 Statistics

    • 2 Conclusion

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