Supplemental Videos

Disclaimer: These videos were created by Ani Adhikari, John DeNero, and David Wagner for Data 8X, a massive open online course (MOOC). They may not match what is actually covered in the Spring 2021 offering of Data 8, and should not be used as the primary resource for the class.

Lecture 1: Introduction

• Why Data Science
• Programming
• Jupyter Notebook Intro

Lecture 2: Cause and Effect

• Questions
• Association
• Causation
• Confounding

Lecture 3: Tables

• Tables
• Select
• Sorting
• Bar Charts

Lecture 4: Data Types

• Names
• Call Expressions
• Arithmetic
  • Arithmetic Question
• Arrays
• Columns
• Strings
  • Strings Exercise
  • Exercise Answer

Lecture 5: Building Tables

• Creating Tables
• Take
• Where

Lecture 6: Census

• Census
• Column Arithmetic
• Accessing Values
• Males and Females

Lecture 7: Charts

• Line Graphs
  • Example One
• Scatter Plots
  • Example Two
• How to Choose
• Types of Data
• Distributions
  • Example Three

Lecture 8: Histograms

• Area Principle
• Binning
  • Example One
• Drawing Histograms
• Density
  • Example Two
  • Example Three

Lecture 9: Functions

• Defining Functions
• Defining Functions Discussion
• Apply
• Example Prediction

Lecture 10: Groups

• One Attribute
• Cross Classification
  • Example One
• Pivot Tables
  • Example Two
• Comparing Distributions

Lecture 11: Joins

• Joins
• Bikes
• Shortest Trips
• Maps

Lecture 12: Table Examples

• Table Method Review
• Discussion Questions
• Midterm Question
• Advanced Where

Lecture 13: Iteration

• Comparison
• Predicates
• Selection
• Random Selection Discussion
• Print
• Control Statements
• For Statements

Lecture 14: Chance

• Monty Hall Problem
• Probability
• Multiplication Rule
• Addition Rule
• Probability Example

Lecture 15: Sampling

• Probability and Sampling
• Sampling
• Distributions
• Large Random Samples
• Simulation
• Statistics

Lecture 16: Models

• Assessing Models
• A Model About Random Selection
• A Genetic Model
• Example

Lecture 17: Comparing Distributions

• Introduction
• Total Variation Distance
• Assessment
• Summary

Lecture 18: Decisions and Uncertainty

• Introduction and Terminology
• Performing a Test
• Statistical Significance
• An Error Probability
• Origin of the Conventions

Lecture 19 & 20: P-values & A/B Testing

• Introduction
• Hypotheses and Statistics
• Performing the Test
• Deflategate Example
• Deflategate Testing

Lecture 21: Examples

N/A

Lecture 22: Midterm Review

N/A

Lecture 23: Causality

• Introduction
• Hypotheses
• Test Statistic
• Performing a Test

Lecture 24: Confidence Intervals

• Percentiles
• Estimation
• Estimate Variability
• The Bootstrap

Lecture 25: Interpreting Confidence

• Applying the Bootstrap
• Confidence Interval Pitfalls
• Confidence Interval Tests

Lecture 26: Center and Spread

• Introduction
• Average and Median
• Standard Deviation
• Chebyshev’s Bounds

Lecture 27: The Normal Distribution

• Standard Units
• SD and Bell Curves
• Normal Distribution
• Central Limit Theorem

Lecture 28: Sample Means

N/A

Lecture 29: Designing Experiments

N/A

Lecture 30: Correlation

• Visualization
• Calculation
• Interpretation

Lecture 31: Linear Regression

• Prediction
• Linear Regression
• Regression to the Mean
• Regression Equation
• Interpreting the Slope