UC Berkeley: 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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Data Science An overview of data science
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Wed Jan 20 1 Why Data Science? Lab 01
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Fri Jan 22 2 Cause and Effect Homework 01
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Tables Using Python to manipulate information
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Mon Jan 25 1 Expressions
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Wed Jan 27 2 Sequences Lab 02
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Fri Jan 29 3 Data Sets Homework 02
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Mon Feb 01 4 Tables
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Wed Feb 03 5 Functions Lab 03
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Fri Feb 05 6 Categories Homework 03
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Visualization Interpreting and exploring data through visualizations
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Mon Feb 08 1 Charts
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Wed Feb 10 2 Histograms Lab 04
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Sampling Understanding the behavior of random selection
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Fri Feb 12 1 Sampling Homework 04
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Wed Feb 17 2 Iteration Lab 05
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Fri Feb 19 3 Estimation and Means Homework 05
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Mon Feb 22 4 Variability
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Prediction Making predictions from data
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Wed Feb 24 1 Correlation Project 1
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Fri Feb 26 2 Explorations: Privacy
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Mon Feb 29 3 Regression
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Wed Mar 02 4 Prediction
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Fri Mar 04 5 Explorations: Design and Critique
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Mon Mar 07 6 Errors
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Wed Mar 09 7 Multiple Regression Lab 08
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Fri Mar 11 8 Classification Homework 06
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Mon Mar 14 9 Explorations: Machine Learning
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Wed Mar 16 10 Midterm Lab 09
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Fri Mar 18 11 Feature Selection
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Inference Reasoning about populations by computing over samples
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Mon Mar 28 1 Confidence Intervals Project 2
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Wed Mar 30 2 Percentiles Lab 10
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Fri Apr 01 3 Distance Between Distributions Homework 07
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Mon Apr 04 4 Hypothesis Testing
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Wed Apr 06 5 Hypothesis Testing II
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Fri Apr 08 6 Permutation Tests
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Mon Apr 11 7 Implementing Permutation Tests
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Wed Apr 13 8 A/B Testing Lab 12
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Fri Apr 15 9 Regression Inference Homework 08
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Mon Apr 18 10 Slope Inference Project 3
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Wed Apr 20 11 Regression Diagnostics Lab 13
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Probability Making assumptions and exploring their consequences
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Fri Apr 22 1 Probability
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Mon Apr 25 2 Conditional Probability
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Wed Apr 27 1 Statistics Lab 14
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Fri Apr 29 2 Conclusion
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