10:38

Reimagining Cities with Prof. David Hsu

07:10

Introduction to GIS, Part I: Key Concepts

17:06

Introduction to R, Part III: Linear and Mixed Models in R

32:33

Introduction to R, Part I: Interface and Data Structures

19:19

Introduction to GIS, Part V: Extract Information from Maps using Spatial Data Points

21:51

Introduction to R, Part IV: Loops and Functions

15:16

Introduction to GIS, Part II: Vectorial Maps, Raster Maps, and Time Series

04:43

Introduction to GIS, Part III: Projection

10:43

Introduction to GIS, Part IV: Stack, Brick, Crop, and Mask

10:20

Introduction to GIS, Part VI: Plotting Maps with ggplot2

19:12

Introduction to R, Part II: Playing with the Data

01:15:46

Lecture 12: List Comprehension, Functions as Objects, Testing, and Debugging (FIXED)

44:20

The Kitchen Cloud Chamber with Prof. Anne White

01:14:27

Lecture 6: Bisection Search (FIXED)

01:15:13

Lecture 10: Lists and Mutability (FIXED)

01:18:06

Lecture 14: Dictionaries

46:42

Lecture 3: Iteration

01:17:53

Lecture 16: Recursion on Non-numerics

32:12

Lecture 21: Timing Programs and Counting Operations

01:17:54

Lecture 25: Plotting

01:21:18

Lecture 23: Complexity Classes Examples

01:03:30

Lecture 1: Introduction to CS and Programming Using Python

01:13:16

Lecture 4: Loops over Strings, Guess-and-Check, and Binary

01:20:36

Lecture 22: Big Oh and Theta

01:16:55

Lecture 19: Inheritance

46:17

Lecture 11: Aliasing and Cloning

47:48

Lecture 17: Python Classes

45:26

Lecture 9: Lambda Functions, Tuples, and Lists

47:11

Lecture 5: Floats and Approximation Methods

45:19

Lecture 15: Recursion

47:39

Lecture 24: Sorting Algorithms

01:12:32

Lecture 26: List Access, Hashing, Simulations, and Wrap-Up

01:17:26

Lecture 18: More Python Class Methods

01:13:00

Lecture 6: Bisection Search (BROKEN)

01:17:46

Lecture 8: Functions as Objects

01:19:04

Lecture 20: Fitness Tracker Object-Oriented Programming Example

01:14:04

Lecture 10: Lists and Mutability (BROKEN)

45:54

Lecture 7: Decomposition, Abstraction, and Functions

42:56

Lecture 13: Exceptions and Assertions

01:18:58

Lecture 2: Strings, Input/Output, and Branching

02:44

5 Million Subscribers – An OCW Odyssey

08:51

Navigating Open Licensing: Strategies for Access and Reuse

01:22:02

Lecture 23: Visualizing Data

01:22:58

Lecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions

01:06:54

Lecture 12: Assessing and Deriving Estimators

01:13:44

Lecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation

01:15:58

Lecture 14: Causality

01:20:15

Lecture 07: Functions of Random Variables

01:20:03

Lecture 19: Practical Issues in Running Regressions

01:09:22

Lecture 21: Endogeneity and Instrument Variables

01:07:55

Lecture 02: Fundamentals of Probability

01:19:26

Lecture 15: Analyzing Randomized Experiments

01:00:43

Lecture 01: Introduction to 14.310x Data Analysis for Social Scientists

01:08:46

Lecture 05: Summarizing and Describing Data

01:23:54

Lecture 04: Gathering and Collecting Data

01:12:09

Lecture 03: Random Variables, Distributions, and Joint Distributions

01:16:16

Lecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations

01:20:25

Lecture 20: Omitted Variable Bias

01:20:25

Lecture 17: The Linear Model

01:18:52

Lecture 08: Moments of Distribution