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 datasets	304
	2	Lesson 2: Drought		Exploratory data analysis, campaign creation	307
	3	Lesson 3: Community Connection		Community topic research, campaign creation	309
	4	Lesson 4: Evaluate and Implement the Campaign		Evaluate & mock implement campaign	312
	5^	Lesson 5: Refine and Create the Campaign	Team Campaign—data	Revise and edit campaign, data collection	314
Predictions and Models (14 days)	6	Lesson 6: Statistical Predictions Using One Variable	Team Campaign—data	One-variable predictions using a rule	317
	7	Lesson 7: Statistical Predictions by Applying the Rule	Team Campaign—data	Predictions applying mean square error, mean absolute error	319
	8	Lesson 8: Statistical Predictions Using Two Variables	Team Campaign—data	Two-variable statistical predictions, scatterplots	323
	9	Lesson 9: The Spaghetti Line	Team Campaign—data	Estimate line of best fit, single linear regression	326
	10	LAB 4A: If the Line Fits…	Team Campaign—data	Estimate line of best fit	328
	11	Lesson 10: What’s the Best Line?	Team Campaign—data	Predictions based on linear models	330
	12	LAB 4B: What’s the Score?	Team Campaign—data	Comparing predictions to real data	333
	13	LAB 4C: Cross-Validation	Team Campaign—data	Use training and test data for predictions	335
	14	Lesson 11: What’s the Trend?	Team Campaign—data	Trend, associations, linear model	338
	15	Lesson 12: How Strong Is It?	Team Campaign—data	Correlation coefficient, strength of trend	342
	16	LAB 4D: Interpreting Correlations	Team Campaign—data	Use correlation coefficient to determine best model	345
	17	Lesson 13: Improving Your Model	Team Campaign—data	Non-linear regression	348
	18	LAB 4E: Some Models Have Curves	Team Campaign—data	Non-linear regression	350
	19	Practicum: Predictions	Team Campaign—data	Linear regression	352
Piecing it Together (3 days)	20	Lesson 14: More Variables to Make Better Predictions	Team Campaign—data	Multiple linear regression	354
	21	Lesson 15: Combination of Variables	Team Campaign—data	Multiple linear regression	357
	22	LAB 4F: This Model Is Big Enough for All of Us	Team Campaign—data	Multiple linear regression	360
Decisions, Decisions! (3 days)	23	Lesson 16: Footbal or Futbol?	Team Campaign—data	Multiple predictors, classifying into groups, decision trees	362
	24	Lesson 17: Grow Your Own Decision Tree	Team Campaign—data	Decision trees based on training and test data	368
	25	LAB 4G: Growing Trees	Team Campaign—data	Decision trees to classify observations	372
Ties that Bind (3 days)	26	Lesson 18: Where Do I Belong?	Team Campaign—data	Clustering, k-means	376
	27	LAB 4H: Finding Clusters	Team Campaign—data	Clustering, k-means	382
	28+	Lesson 19: Our Class Network	Team Campaign—data	Clustering, networks	384
End of Unit Project (7 days)	29- 36	End of Unit 4 Modeling Activity Project	Team Campaign	Synthesis of above	387

^=Data collection window begins.

+=Data collection window ends.