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 this course, 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