Introduction to Data Science Daily Overview: Unit 4

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Unit 4

Daily Overview: Unit 4

Theme Day Lessons and Labs Campaign Topics Page
Predictions
and
Models
(15 days)
1 Lesson 1: Water Usage Data cycle, official data sets 315
2 Lesson 2: Exploring Water Usage Exploratory data analysis, campaign creation 319
3 Lesson 3: Evaluating and Implementinga Water Campaign Water Campaign—data Statistical questions, evaluate & mock implement campaign 321
4 Lesson 4: Refining the Water Campaign Water Campaign—data Revise and edit campaign, data collection 323
5 Lesson 5: Statistical Predictions Using One Variable Water Campaign—data One-variable predictions using a rule 325
6 Lesson 6: Statistical Predictions by Applying the Rule Water Campaign—data Predictions applying mean square deviation, mean absolute error 328
7 Lesson 7: Statistical Predictions Using Two Variables Water Campaign—data Two-variable statistical predictions, scatterplots 333
8 LAB 4A: If the Line Fits… Water Campaign—data Estimate line of best fit 335
9 LAB 4B: What’s the Score? Water Campaign—data Comparing predictions to real data 337
10 Lesson 8: What’s the Trend? Water Campaign—data Trend, associations, linear model 339
11 Lesson 9: Spaghetti Line Water Campaign—data Estimate line of best fit, single linear regression 343
12 LAB 4C: Cross-Validation Water Campaign—data Use training and testing data for predictions 346
13 Lesson 10: Predicting Values Water Campaign—data Predictions based on linear models 348
14 Lesson 11: How Strong Is It? Water Campaign—data Correlation coefficient, strength of trend 351
15 LAB 4D: Interpreting Correlations Water Campaign—data Use correlation coefficient to determine best model 353
Piecing it
Together
(6 days)
16 Lesson 12: More Variables to Make Better Predictions Water Campaign—data Multiple linear regression 358
17 Lesson 13: Combination of Variables Water Campaign—data Multiple linear regression 361
18 LAB 4E: This Model Is Big Enough for All of Us Water Campaign—data Multiple linear regression 364
19 Practicum: Predictions Water Campaign—data Linear regression 365
20 Lesson 14: Improving Your Model Water Campaign—data Non-linear regression 366
21 LAB 4F: Some Models Have Curves Water Campaign—data Non-linear regression 368
The
Growth of
Landfills
(5 days)
22 Lesson 15: The Growth of Landfills Water Campaign—data Modeling to answer realworld problems 372
23 Lesson 16: Exploring Trash via the Dashboard Water Campaign—data Analyze data to improve models 376
24 Lesson 17: Exploring Trash via RStudio Water Campaign—data Analyze data to improve models 377
25 Prepare Team Presentations Water Campaign—data Modeling with statistics -
26 Present Team Recommendations Water Campaign—data Modeling with statistics -
Decisions,
Decisions!
(3 days)
27 Lesson 18: Grow Your Own Decision Tree Water Campaign—data Multiple predictors, classifying into groups, decision trees 380
28 Lesson 19: Data Scientists or Doctors? Water Campaign—data Decision trees based on training and testing data 385
29 LAB 4G: Growing Trees Water Campaign—data Decision trees to classify observations 388
Ties that
Bind
(3 days)
30 Lesson 20: Where Do I Belong? Water Campaign—data Clustering, k-means 392
31 LAB 4H: Finding Clusters Water Campaign—data Clustering, k-means 397
32+ Lesson 21: Our Class Network Water Campaign—data Clustering, networks 399
End of Unit
Project
(7 days)
33-
40
End of Unit 3 and 4 Design Project andOral Presentations: Water Usage Water Campaign Synthesis of above 403

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