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Introduction to Data Science Curriculum v_5.0 IDS_Curriculum
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  • Table of Contents
  • Overview & Philosophy
  • Scope and Sequence
    • Daily Overview
    • Essential Concepts
      • Data Are All Around
      • Lesson 1: Data Trails
      • Lesson 2: Stick Figures
      • Lesson 3: Data Structures
      • Lesson 4: The Data Cycle
      • Lesson 5: So Many Questions
      • Lesson 6: What Do I Eat?
      • Lesson 7: Setting the Stage
      • Campaign Guidelines – Food Habits
      • Visualizing Data
      • Lesson 8: Tangible Plots
      • Lesson 9: What is Typical?
      • Lesson 10: Making Histograms
      • Lesson 11: What Shape Are You In?
      • Lesson 12: Exploring Food Habits
      • Lesson 13: RStudio Basics
      • Lab 1A - Data, Code & RStudio
      • Lab 1B: Get the Picture?
      • Lab 1C: Export, Upload, Import
      • Lesson 14: Variables, Variables, Variables
      • Lab 1D: Zooming Through Data
      • Lab 1E: What’s the Relationship?
      • Practicum: The Data Cycle & My Food Habits
      • Would You Look at the Time!
      • Lesson 15: Americans’ Time on Task
      • Campaign Guidelines – Time Use
      • Lab 1F: A Diamond in the Rough
      • Lesson 16: Categorical Associations
      • Lesson 17: Interpreting Two-Way Tables
      • Lab 1G: What’s the FREQ?
      • Practicum: Teen Depression
      • Lab 1H: Our Time
      • End of Unit Project and Oral Presentation: Analyzing Data to Evaluate Claims
    • Daily Overview
    • Essential Concepts
      • What is Your True Color?
      • Lesson 1: What is Your True Color?
      • Lesson 2: What Does Mean Mean?
      • Lesson 3: Median in the Middle
      • Lesson 4: How Far is it from Typical?
      • Lab 2A - All About Distributions
      • Lesson 5: Human Boxplots
      • Lesson 6: Face Off
      • Lesson 7: Plot Match
      • Lab 2B - Oh the Summaries ...
      • Practicum: The Summaries
      • How Likely is it?
      • Lesson 8: How Likely Is It?
      • Lesson 9: Bias Detective
      • Lesson 10: Marbles, Marbles…
      • Lab 2C - Which Song Plays Next?
      • Lesson 11: This AND/OR That
      • Lab 2D - Queue It Up!
      • Practicum: Win, Win, Win
      • What Are the Chances That You Are Stressing or Chilling?
      • Lesson 12: Don’t Take My Stress Away!
      • Campaign Guidelines – Stress/Chill
      • Lesson 13: The Horror Movie Shuffle
      • Lab 2E - The Horror Movie Shuffle
      • Lesson 14: The Titanic Shuffle
      • Lab 2F - The Titanic Shuffle
      • Lesson 15: Tangible Data Merging
      • Lab 2G - Getting It Together
      • Practicum: What Stresses Us?
      • What’s Normal?
      • Lesson 16: What Is Normal?
      • Lesson 17: A Normal Measure of Spread
      • Lesson 18: What’s Your Z-Score?
      • Lab 2H - Eyeballing Normal
      • Lab 2I - R’s Normal Distribution Alphabet
      • End of Unit Project: Asking and Answering Statistical Questions of Our Own Data
    • Daily Overview
    • Essential Concepts
      • Testing, Testing…1, 2, 3…
      • Lesson 1: Anecdotes vs. Data
      • Lesson 2: What Is an Experiment?
      • Lesson 3:Let’s Try an Experiment!
      • Lesson 4: Predictions, Predictions
      • Lesson 5: Time Perception Experiment
      • Lab 3A: The Results Are In!
      • Practicum: Music to my Ears
      • Would You Look at That?
      • Lesson 6: Observational Studies
      • Lesson 7: Observational Studies vs. Experiments
      • Lesson 8: Monsters That Hide in Observational Studies
      • Lab 3B: Confound It All!
      • Are You Asking Me?
      • Lesson 9: Survey Says…
      • Lesson 10: We’re So Random
      • Lesson 11: The Gettysburg Address
      • Lab 3C: Random Sampling
      • Lesson 12: Bias in Survey Sampling
      • Lesson 13: The Confidence Game
      • Lesson 14: How Confident Are You?
      • Lab 3D: Are You Sure about That?
      • Practicum: Let’s Build a Survey!
      • What’s the Trigger?
      • Lesson 15: Ready, Sense, Go!
      • Lesson 16: Does It Have a Trigger?
      • Lesson 17: Creating Our Own Participatory Sensing Campaign
      • Lesson 18: Evaluating Our Own Participatory Sensing Campaign
      • Lesson 19: Implementing Our Own Participatory Sensing Campaign
      • Webpages
      • Lesson 20: Online Data-ing
      • Lab 3E: Scraping Web Data
      • Lab 3F: Maps
      • Lesson 21: Learning to Love XML
      • Lesson 22: Changing Orientation
      • Practicum: What Does Our Campaign Data Say?
      • End of Unit Project: TB or Not TB
    • Daily Overview
    • Essential Concepts
      • Predictions and Models
      • Lesson 1: Water Usage
      • Lesson 2: Exploring Water Usage
      • Lesson 3: Evaluating and Implementing a Water Campaign
      • Lesson 4: Refining the Water Campaign
      • Lesson 5: Statistical Predictions Using One Variable
      • Lesson 6: Statistical Predictions by Applying the Rule
      • Lesson 7: Statistical Predictions Using Two Variables
      • LAB 4A: If the Line Fits…
      • LAB 4B: What’s the Score?
      • Lesson 8: What’s the Trend?
      • Lesson 9: Spaghetti Line
      • LAB 4C: Cross-Validation
      • Lesson 10: Predicting Values
      • Lesson 11: How Strong Is It?
      • LAB 4D: Interpreting Correlations
      • Piecing It Together
      • Lesson 12: More Variables to Make Better Predictions
      • Lesson 13: Combination of Variables
      • LAB 4E: This Model Is Big Enough for All of Us
      • Practicum: Predictions
      • Lesson 14: Improving your Model
      • LAB 4F: Some Models Have Curves
      • The Growth of Landfills
      • Lesson 15: The Growth of Landfills
      • Lesson 16: Exploring Trash Via the Dashboard
      • Lesson 17: Exploring Trash Via RStudio
      • Decisions, Decisions!
      • Lesson 18: Grow Your Own Decision Tree
      • Lesson 19: Data Scientists or Doctors?
      • LAB 4G: Growing Trees
      • Ties That Bind
      • Lesson 20: Where Do I Belong?
      • LAB 4H: Finding Clusters
      • Lesson 21: Our Class Network
      • End of Unit 3 and 4 Design Project and Oral Presentation: Water Usage
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Introduction to Data Science_Curriculum_v_5.0.pdf


[Introduction to Data Science_Curriculum_v_5.0.pdf](../IDS_Curriculum_v_5.0/1_IDS_Curriculum_v_5.0/Introduction to Data Science_Curriculum_v_5.0.pdf)
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