# 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. |
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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 |