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Introduction to Data Science Daily Overview: Unit 4


Unit 4

Daily Overview: Unit 4

Theme Day Lessons and Labs Campaign Topics Page
(5 days)
1 Lesson 1: Trash Modeling to answer real world problems, official data sets 315
2 Lesson 2: Drought Exploratory data analysis, campaign creation 319
3 Lesson 3: Community Connection Team Campaign—data Community topic research, campaign creation 321
4 Lesson 4: Evaluate and Implement the Campaign Team Campaign—data Statistical questions, evaluate & mock implement campaign 323
5 Lesson 5: Refine and Create the Campaign Team Campaign—data Revise and edit campaign, data collection 325
and Models
(13 days)
6 Lesson 6: Statistical Predictions Using One Variable Team Campaign—data One-variable predictions using a rule 328
7 Lesson 7: Statistical Predictions by Applying the Rule Team Campaign—data Predictions applying mean square deviation, mean absolute error 333
8 Lesson 8: Statistical Predictions Using Two Variables Team Campaign—data Two-variable statistical predictions, scatterplots 335
9 Lesson 9: The Spaghetti Line Team Campaign—data Estimate line of best fit, single linear regression 337
10 LAB 4A: If the Line Fits… Team Campaign—data Estimate line of best fit 339
11 Lesson 10: What’s the Best Line? Team Campaign—data Predictions based on linear models 343
12 LAB 4B: What’s the Score? Team Campaign—data Comparing predictions to real data 345
13 LAB 4C: Cross-Validation Team Campaign—data Use training and test data for predictions 346
14 Lesson 11: What’s the Trend? Team Campaign—data Trend, associations, linear model 348
15 Lesson 12: How Strong Is It? Team Campaign—data Correlation coefficient, strength of trend 351
16 LAB 4D: Interpreting Correlations Team Campaign—data Use correlation coefficient to determine best model 353
17 Lesson 13: Improving Your Model Team Campaign—data Non-linear regression 355
18 LAB 4E: Some Models Have Curves Team Campaign—data Non-linear regression 357
Piecing it
(4 days)
19 Lesson 14: More Variables to Make Better Predictions Team Campaign—data Multiple linear regression 359
20 Lesson 15: Combination of Variables Team Campaign—data Multiple linear regression 361
21 LAB 4F: This Model Is Big Enough for All of Us Team Campaign—data Multiple linear regression 364
22 Practicum: Predictions Team Campaign—data Linear regression 365
(3 days)
23 Lesson 16: Footbal or Futbol? Team Campaign—data Multiple predictors, classifying into groups, decision trees 369
24 Lesson 17: Grow Your Own Decision Tree Team Campaign—data Decision trees based on training and test data 371
25 LAB 4G: Growing Trees Team Campaign—data Decision trees to classify observations 374
Ties that
(3 days)
26 Lesson 18: Where Do I Belong? Team Campaign—data Clustering, k-means 379
27 LAB 4H: Finding Clusters Team Campaign—data Clustering, k-means 382
28 Lesson 19: Our Class Network Team Campaign—data Clustering, networks 385
End of Unit
(7 days)
End of Unit 4 Modeling Activity Project and Presentation Team Campaign Synthesis of above 388

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