| Table of Contents |
| Content |
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Page |
| Overview and Philosophy |
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8 |
| Scope and Sequence |
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14 |
UNIT 1
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Campaign |
Topics |
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| Daily Overview |
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19 |
| Essential Concepts |
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20 |
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Section 1: Data are all Around |
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22 |
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Lesson 1: Data Trails |
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Defining data, consumer privacy |
24 |
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Lesson 2: Stick Figures |
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Organizing & collecting data |
26 |
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Lesson 3: Data Structures |
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Organizing data, rows & columns, variables |
28 |
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Lesson 4: The Data Cycle |
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Data cycle, statistical questions |
30 |
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Lesson 5: So Many Questions |
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Statistical questions, variability |
35 |
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Lesson 6: What Do I Eat? |
Food Habits |
Data cycle, collecting data |
39 |
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Lesson 7: Setting the Stage |
Food Habits – data |
Participatory Sensing |
42 |
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Section 2: Visualizing Data |
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47 |
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Lesson 8: Tangible Plots |
Food Habits – data |
Dotplots, minimum/maximum, frequency |
48 |
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Lesson 9: What Is Typical? |
Food Habits – data |
Typical value, center |
52 |
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Lesson 10: Making Histograms |
Food Habits – data |
Histograms, bin widths |
54 |
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Lesson 11: What Shape Are You In? |
Food Habits – data |
Shape, center, spread |
57 |
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Lesson 12: Exploring Food Habits |
Food Habits – data |
Single & multi-variable plots |
59 |
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Lesson 13: RStudio Basics |
Food Habits – data |
Intro to RStudio |
61 |
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Lab 1A: Data, Code & RStudio |
Food Habits – data |
RStudio basics |
64 |
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Lab 1B: Get the Picture? |
Food Habits – data |
Variable types, bar graphs, histograms |
68 |
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Lab 1C: Export, Upload, Import |
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Importing data |
71 |
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Lesson 14: Variables, Variables, Variables |
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Multi-variable plots |
76 |
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Lab 1D: Zooming Through Data |
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Subsetting |
80 |
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Lab 1E: What’s the Relationship? |
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Multi-variable plots |
84 |
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Practicum: The Data Cycle & My Food Habits |
Food Habits |
Data cycle, variability |
87 |
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Section 3: Would You Look at the Time |
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89 |
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Lesson 15: Americans’ Time on Task |
Time Use – data |
Evaluating claims |
90 |
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Lab 1F: A Diamond In the Rough |
Time Use – data |
Cleaning names, categories, and strings |
95 |
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Lesson 16: Categorical Associations |
Time Use – data |
Joint relative frequencies in 2- way tables |
100 |
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Lesson 17: Interpreting Two-Way Tables |
Time Use – data |
Marginal & conditional relative frequencies |
102 |
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Lab 1G: What’s the FREQ? |
Time Use – data |
2-way tables, tally |
107 |
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Practicum: Teen Depression |
Time Use |
Statistical questions, interpreting plots |
110 |
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Lab 1H: Our Time |
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Data cycle, synthesis |
112 |
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End of Unit 1 Project: Evaluating Claims from the Media |
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Data cycle |
113 |
| UNIT 2 |
Campaign |
Topics |
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| Daily Overview |
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115 |
| Essential Concepts |
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116 |
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Section 1: What is Your True Color? |
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118 |
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Lesson 1: What Is Your True Color? |
Personality Color - data |
Subsets, relative frequency |
120 |
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Lesson 2: What Does Mean Mean? |
Personality Color |
Measures of center – mean |
123 |
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Lesson 3: Median In the Middle |
Personality Color |
Measures of center – median |
127 |
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Lesson 4: How Far Is It from Typical? |
Personality Color |
Measures of spread – MAD |
130 |
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Lab 2A: All About Distributions |
Personality Color |
Measures of center & spread |
134 |
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Lesson 5: Human Boxplots |
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Boxplots, IQR |
136 |
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Lesson 6: Face Off |
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Comparing distributions |
139 |
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Lesson 7: Plot Match |
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Comparing distributions |
142 |
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Lab 2B: Oh, the Summaries… |
Personality Color |
Numerical summaries, custom functions |
144 |
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Practicum: The Summaries |
Food Habits or Time Use |
Data cycle, comparing distributions |
147 |
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Section 2: How Likely is it? |
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149 |
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Lesson 8: How Likely is It? |
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Probability, simulations |
150 |
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Lesson 9: Dice Detective |
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Simulations to detect unfairness |
153 |
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Lesson 10: Marbles, Marbles |
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Probability, with replacement |
157 |
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Lab 2C: Which Song Plays Next? |
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Probability of simple events, do loops, set.seed() |
159 |
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Lesson 11: This AND/OR That |
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Compound probabilities |
162 |
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Lab 2D: Queue It Up! |
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Probability with/without replacement, sample() |
166 |
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Practicum: Win, Win, Win |
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Probability estimation through repeated simulations |
169 |
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Section 3: Are You Stressing or Chilling? |
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170 |
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Lesson 12: Don’t Take My Stress Away |
Stress/Chill – data |
Introduction to campaign |
172 |
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Lesson 13: The Horror Movie Shuffle |
Stress/Chill – data |
Chance differences – categorical |
176 |
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Lab 2E: The Horror Movie Shuffle |
Stress/Chill – data |
Inference for categorical variables |
180 |
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Lesson 14: The Titanic Shuffle |
Stress/Chill – data |
Chance differences - numerical |
183 |
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Lab 2F: The Titanic Shuffle |
Stress/Chill – data |
Inference for numerical variables |
187 |
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Lesson 15: Tangible Data Merging |
Stress/Chill – data |
Merging datasets |
189 |
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Lab 2G: Getting It Together |
Stress/Chill & Personality Color |
Stacking vs. joining datasets |
191 |
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Practicum:What Stresses Us? |
Stress/Chill & Personality Color |
Analyzing merged data |
193 |
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Section 4: What’s Normal? |
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194 |
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Lesson 16: What Is Normal? |
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Introduction to normal curve |
195 |
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Lesson 17: A Normal Measure of Spread |
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Measures of spread - SD |
198 |
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Lesson 18: What’s Your Z-Score? |
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z-scores, shuffling |
201 |
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Lab 2H: Eyeballing Normal |
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Normal curves overlaid on distributions & simulated data |
205 |
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Lab 2I: R’s Normal Distribution Alphabet |
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Normal probability, rnorm(), pnorm(), qnorm() |
207 |
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End of Unit 2 Project: Comparing Groups Using Our Own Data |
Stress/Chill, Personality Color, FoodHabits, or Time Use |
Synthesis of Unit 2 |
209 |
| UNIT 3 |
Campaign |
Topics |
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| Daily Overview |
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211 |
| Essential Concepts |
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212 |
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Section 1: Testing, Testing…1, 2, 3… |
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214 |
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Lesson 1: Anecdotes vs. Data |
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Reading articles critically, data |
216 |
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Lesson 2: What is an Experiment? |
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Experiments, causation |
219 |
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Lesson 3: Let’s Try an Experiment! |
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Random assignments, confounding factors |
222 |
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Lesson 4: Predictions, Predictions |
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Visualizations, predictions |
224 |
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Lesson 5: Time Perception Experiment |
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Elements of an experiment |
226 |
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Lab 3A: The Results Are In! |
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Analyzing experiment data |
228 |
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Practicum: TB or Not TB |
Time Perception |
Simulation using experiment data |
229 |
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Section 2: Would You Look at That? |
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231 |
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Lesson 6: Observational Studies |
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Observational study |
233 |
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Lesson 7: Observational Studies vs. Experiments |
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Observational study, experiment |
235 |
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Lesson 8: Monsters that Hide in Observational Studies |
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Observational study, confounding factors |
237 |
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Lab 3B: Confound it all! |
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Confounding factors |
241 |
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Section 3: Are You Asking Me? |
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243 |
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Lesson 9: Survey Says… |
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Survey |
244 |
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Lesson 10: We’re So Random |
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Data collection, random samples |
247 |
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Lesson 11: The Gettysburg Address |
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Sampling bias |
251 |
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Lab 3C: Random Sampling |
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Random sampling |
256 |
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Lesson 12: Bias in Survey Sampling |
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Bias in survey sampling |
258 |
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Lesson 13: The Confidence Game |
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Confidence intervals |
261 |
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Lesson 14: How Confident Are You? |
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Confidence intervals, margin of error |
264 |
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Lab 3D: Are You Sure about That? |
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Bootstrapping |
266 |
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Practicum: Let’s Build a Survey! |
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Survey design with non-leading questions |
269 |
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Section 4: What’s the Trigger? |
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270 |
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Lesson 15 Ready, Sense, Go! |
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Sensors, data collection |
271 |
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Lesson 16: Does it have a Trigger? |
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Survey questions, sensor questions |
274 |
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Lesson 17: Creating Our Own PS Campaign |
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Participatory Sensing campaign creation |
276 |
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Lesson 18: Evaluating Our Own PS Campaign |
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Statistical questions, evaluate campaign |
279 |
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Lesson 19: Implementing Our Own PS Campaign |
Class Campaign—data |
Mock-implement & create campaign |
281 |
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Section 5: Webpages |
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283 |
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Lesson 20: Online Data-ing |
Class Campaign—data |
Data on the internet |
284 |
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Lab 3E: Scraping Web Data |
Class Campaign—data |
Scraping data from the Internet |
287 |
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Lab 3F: Maps |
Class Campaign—data |
Making maps with data from the Internet |
289 |
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Lesson 21: Learning to Love XML |
Class Campaign—data |
Data storage, XML |
291 |
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Lesson 22: Changing Format |
Class Campaign—data |
Converting XML files |
296 |
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End of Unit 3 Project: Analyzing Our Own Campaign Data |
Class Campaign |
Statistical question, our data |
299 |
| UNIT 4 |
Campaign |
Topics |
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| Daily Overview |
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|
301 |
| Essential Concepts |
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302 |
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Section 1: Campaigns and Community |
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304 |
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Lesson 1: Trash |
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Modeling to answer real world problems, official datasets |
306 |
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Lesson 2: Drought |
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Exploratory data analysis, campaign creation |
309 |
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Lesson 3: Community Connection |
Team Campaign—data |
Community topic research, campaign creation |
311 |
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Lesson 4: Evaluate and Implement the Campaign |
Team Campaign—data |
Evaluate & mock-implement campaign |
314 |
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Lesson 5: Refine and Create the Campaign |
Team Campaign—data |
Revise and edit campaign, data collection |
316 |
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Section 2: Predictions and Models |
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317 |
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Lesson 6: Statistical Predictions Using One Variable |
Team Campaign—data |
One variable predictions using a rule |
319 |
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Lesson 7: Statistical Predictions Applying the Rule |
Team Campaign—data |
Predictions applying MSE, MAE |
321 |
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Lesson 8: Statistical Predictions Using Two Variables |
Team Campaign—data |
Two-variable statistical predictions |
325 |
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Lesson 9: The Spaghetti Line |
Team Campaign—data |
Estimate line of best fit, linear regression |
328 |
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LAB 4A: If the Line Fits… |
Team Campaign—data |
Estimate line of best fit |
330 |
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Lesson 10: What’s the Best Line? |
Team Campaign—data |
Predictions based on linear models |
332 |
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LAB 4B: What’s the Score? |
Team Campaign—data |
Comparing predictions to real data |
335 |
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LAB 4C: Cross-Validation |
Team Campaign—data |
Use training and test data for predictions |
337 |
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Lesson 11: What’s the Trend? |
Team Campaign—data |
Trend, associations, linear model |
340 |
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Lesson 12: How Strong Is It? |
Team Campaign—data |
Correlation coefficient, strength of trend |
344 |
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LAB 4D: Interpreting Correlations |
Team Campaign—data |
Correlation coefficient, best model |
347 |
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Lesson 13: Improving Your Model |
Team Campaign—data |
Non-linear regression |
350 |
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LAB 4E: Some Models Have Curves |
Team Campaign—data |
Non-linear regression |
352 |
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Practicum: Predictions |
Team Campaign—data |
Linear regression |
354 |
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Section 3: Piecing it Together |
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355 |
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Lesson 14: More Variables to Make Better Predictions |
Team Campaign—data |
Multiple linear regression |
357 |
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Lesson 15: Combination of Variables |
Team Campaign—data |
Multiple linear regression |
360 |
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LAB 4F: This Model Is Big Enough for All of Us |
Team Campaign—data |
Multiple linear regression |
363 |
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Section 4: Decisions, Decisions! |
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364 |
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Lesson 16: Footbal or Futbol? |
Team Campaign—data |
Multiple predictors, classifying into groups |
365 |
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Lesson 17: Grow Your Own Decision Tree |
Team Campaign—data |
Decision trees based on training/test data |
371 |
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LAB 4G: Growing Trees |
Team Campaign—data |
Decision trees to classify observations |
375 |
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Section 5: Ties That Bind |
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378 |
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Lesson 18: Where Do I Belong? |
Team Campaign—data |
Clustering, k-means |
379 |
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LAB 4H: Finding Clusters |
Team Campaign—data |
Clustering, k-means |
385 |
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Lesson 19: Our Class Network |
Team Campaign—data |
Clustering, networks |
387 |
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End of Unit 4 Project: Modeling a Community Issue
|
Team Campaign |
Synthesis of Unit 4 |
390 |