# Index of Topics and Sources

## Goal of these Notes

My goal for the notes section of my website is to establish (and then elaborate over time) the skills and knowledge required in order to be successful in data science. I prefer to store and access this knowledge digitally instead of memorizing it or writing it down. And, distilling it onto this website helps me to learn it. I hope this information may be of use to someone else.

The following is a very high-level overview of various data science topics and which sources cover each one.

## Topics and Sources

The following table of indexes is included to keep the topic table from growing very wide.

Index |
Source |
---|---|

1 | Udacity Data Analyst Nanodegree |

2 | A Whirlwind Tour of Python |

3 | Python for Data Analysis |

4 | Python Data Science Handbook |

5 | Data Science from Scratch |

✓’s in the following table indicate which sources discuss each given topic. The topics tabulated below should roughly correspond with the topics in the navigation to the left.

Topic |
1 |
2 |
3 |
4 |
5 |
---|---|---|---|---|---|

Command Line | ✓ | ||||

SQL | ✓ | ✓ | |||

MapReduce | ✓ | ||||

Python | ✓ | ✓ | ✓ | ✓ | |

Anaconda | ✓ | ||||

Jupyter Notebooks | ✓ | ✓ | |||

Ipython interpreter | ✓ | ✓ | ✓ | ||

Numpy | ✓ | ✓ | ✓ | ||

Pandas | ✓ | ✓ | ✓ | ||

Matplotlib | ✓ | ✓ | ✓ | ✓ | |

Statsmodels | ✓ | ||||

Scikit-learn | ✓ | ||||

Data Analysis Process | ✓ | ✓ | ✓ | ||

Descriptive Statistics | ✓ | ✓ | ✓ | ||

Probability | ✓ | ✓ | |||

Inferential Statistics | ✓ | ✓ | |||

Linear Algebra | ✓ | ||||

Machine Learning | ✓ | ✓ |