Data 8: Foundations of Data Science
Fall 2016
Instructor:
Ani Adhikari
Announcements:
- Final Review Information can be found here: Dead Week Review
Week | Date |
Lecture | Reading | Discussion/Lab | Homework | Project |
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Introduction | Wed 8/24 |
Introduction (slides) (video) | 1.1 1.2 1.3 |
Intro to Notebooks/Python (data8) (ds8) (due whenever) |
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Fri 8/26 |
Cause and Effect (slides) (video) | Chapter 2 | HW01 (data8) (ds8) (due 9/1) |
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Python Basics |
Mon 8/29 |
Expressions (slides) (video) | Chapter 3 | |||
Wed 8/31 |
Sequences (slides) (notebook) (video) | Chapter 4 | Data Types and Sequences (data 8) (ds8) | |||
Visualization: Graphs and Tables |
Fri 9/2 |
Tables Part 1 (slides) (video) | 5 5.1 |
HW02 (data8) (ds8) (due 9/8) |
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Mon 9/5 |
No Class: Labor day | |||||
Wed 9/7 |
Tables Part 2 (slides) (video) | 5.2 5.3 5.4 |
Arrays and Tables (data8) (ds8) | |||
Fri 9/9 |
Graphs (slides) (video) | 6 6.1 |
HW03 (data8) (ds8) (due 9/15) |
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Mon 9/12 |
Graphs Part II (slides) (video) | 6.2 6.3 |
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Wed 9/14 |
Functions and Tables (slides) (video) | 7 7.1 |
Functions and Histograms (data8) (ds8) | |||
Fri 9/16 |
Functions and Tables Part 2 (slides) (video) | 7.2 7.3 |
HW04 (data8) (ds8) (due 9/22) |
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Mon 9/19 |
Functions and Tables Part 3 (slides) (video) | 7.4 7.5 |
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Distributions and Random Sampling |
Wed 9/21 |
Iteration (slides) (video) | 8 8.1 |
Project 1 | Project 1 (due 10/4) | |
Fri 9/23 |
Randomness (slides) (video) | 8.2 8.3 |
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Mon 9/26 |
Sampling (slides) (video) | 8.4 8.5 |
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Wed 9/28 |
Empirical Distribution of a Sample (slides) (video) | 9 9.1 9.2 |
Project 1 Checkpoint (due 9/27) | |||
Fri 9/30 |
Empirical Distribution of a Statistic (slides) (video) | 9.3 | HW05 (data8) (ds8) (due 10/6) |
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Testing Statistical Hypothesis |
Mon 10/3 |
Total Variation (slides) (video) | 10.1 | |||
Wed 10/5 |
Hypothesis Testing (slides) (video) | 10.2 | Statistics in Sampling (data8) (ds8) | |||
Fri 10/7 |
Error Probabilities (slides) (video) | 10.3 | HW06 (data8) (ds8) (due 10/13) | |||
Mon 10/10 |
Examples (slides) (video) | 10.4 | ||||
Midterm | Wed 10/12 |
Midterm Review (slides) (video) | Midterm Review | |||
Fri 10/14 |
Midterm | |||||
Estimation | Mon 10/17 |
Bootstrap Confidence Intervals (slides) (video) | 11 11.1 11.2 |
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Wed 10/19 |
Interpreting CIs (slides) (video) | 11.3 | The Bootstrap (data8)(ds8) | |||
Fri 10/21 |
CI Testing (slides) (video) | 11.4 | HW07 (data8) (ds8) (due 10/27) | |||
Why the Mean Matters | Mon 10/24 |
Averages and Standard Deviation (slides) (video) | 12 12.1 12.2 |
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Wed 10/26 |
SD and Normal Curve (slides) (video) | 12.3 12.4 |
Project 2 (data8) (ds8) | Project 2 (due 11/8 at 7pm, Checkpoint due 11/1 at 7pm) | ||
Fri 10/28 |
Variability of the Sample Mean (slides) (video) | 12.5 12.6 |
HW08 (data8) (ds8) (due 11/3) | |||
Prediction | Mon 10/31 |
Scatter Plots and Correlation (slides) (video) | 13 13.1 |
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Wed 11/2 |
Regression (slides) (video) | 13.2 | Introduction to Regression (ds8) | Project 2 Checkpoint (due 11/1 at 7pm) | ||
Fri 11/4 |
Least Squares (slides) (video) | 13.3 | HW09 (data8) (ds8) (due 11/10) | |||
Assessing Predictions and Models |
Mon 11/7 |
Regression Inference I (slides) (video) | 13.4 13.5 13.6 14 14.1 |
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Wed 11/9 |
Regression Inference II (slides) (video) | 14.2 14.3 |
Regression Inference (ds8) | |||
Fri 11/11 |
Holiday: No School | HW10 (data8) (ds8) (due 11/17) | ||||
Predicting Categories |
Mon 11/14 |
Classification I (slides) (video) | 15 15.1 15.2 |
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Wed 11/16 |
Classification II (slides) (video) | 15.3 15.4 15.5 |
Classification and Project 3 (data8) (ds8) | Project 3 (due 11/29 at 7pm, checkpoint due 11/22 at 7pm) | ||
Comparison, Causality and Decisions |
Fri 11/18 |
Comparing Samples I (slides) (video) | 16 16.1 |
HW11 (data8) (ds8) (due 11/28) | ||
Mon 11/21 |
Comparing Samples II (slides) (video) | 16.2 | ||||
Wed 11/23 |
Thanksgiving: No School | |||||
Fri 11/25 |
Thanksgiving: No School | |||||
Mon 11/28 |
Causality (slides) (video) | 16.3 | ||||
Wed 11/30 |
Bayes and decisions (slides) (video) | 17 17.1 17.2 |
Bayes Lab | |||
Fri 12/2 |
Conclusion (slides) (video) | HW12 (optional practice problems) |