Introduction to Data Science Daily Overview: Unit 4

Unit 4

Daily Overview: Unit 4

Theme	Day	Lessons and Labs	Campaign	Topics	Page
Campaigns and Community (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
Predictions 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 Together (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
Decisions, Decisions! (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 Bind (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 Project (7 days)	29- 36	End of Unit 4 Modeling Activity Project and Presentation	Team Campaign	Synthesis of above	388

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