Lecture 2 – Data Sampling and Probability
Presented by Isaac Schmidt, Suraj Rampure
Content by Fernando Perez, Suraj Rampure, Ani Adhikari, and Joseph Gonzalez
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 2.0 Introduction to lecture format. |
||
| 2.1.1 Data science lifecycle, case study on squirrels. |
2.1.1 | |
| 2.1.2 Censuses and surveys. Issues with the US Census. |
2.1.2 | |
| 2.2 Samples. Drawbacks to convenience and quota samples. |
2.2 | |
| 2.3 A case study in sampling bias (1936 election). |
2.3 | |
| 2.4 Sources of bias, and a formal definition of sampling frames. |
2.4 | |
| 2.5 Probability samples, and why we need them. |
2.5 | |
| 2.6 Introducing binomial and multinomial probability calculations. |
2.6 | |
| 2.7 Generalizing binomial and trinomial probability calculations. |
2.7 | |
| 2.8 (Extra) Using permutations and combinations to derive the binomial coefficient. |
2.8 | |
| 2.9 (Extra) Example usages of the binomial coefficient. |
2.9 |