Fall, 2020

Weeks with a ✔ are ready for your consumption. Weeks with a “x” are not.

Date | Week | Topic | Assignment | |
---|---|---|---|---|

Sep 15 | 0 | iPython setup & basic use | - | ✔ |

Sep 22 | 1 | Basic data types, operators & expressions | A1 | ✔ |

Sep 29 | 2 | Control flow | A2 | ✔ |

Oct 6 | 3 | Functions | A3 | ✔ |

Oct 13 | 4 | Input & Output | A4 | ✔ |

Oct 20 | 5 | NumPy 1 | A5 | ✔ |

Oct 27 | 6 | NumPy 2 | A6 | ✔ |

——– | — | ——————————————————- | ————- | —— |

Nov 3 | - | fall reading week |
- | - |

Nov 10 | 7 | pandas 1 | A7 | x |

Nov 17 | 8 | pandas 2 | A8 | x |

Nov 24 | 9 | Graphical displays of data | A9 | x |

Dec 1 | 10 | Data analysis project | A10 | x |

The Big List of Programming Challenges

- Here is a link to the course syllabus. Here is a pdf version: syllabus.pdf
- Our textbook will be Python for Data Analysis (2nd Ed.) by Wes McKinney (O’Reilly 2018).
- We will also use material from Think Python (2nd Ed.) by Allen B. Downey (Green Tea Press 2015)
- We will be using the Anaconda Python (v3) distribution.
- Our TA for the course is Geetika Gupta (ggupta23@uwo.ca)

- The Python Tutorial
- NumPy Documentation including several tutorials
- SciPy.org, an ecosystem of packages for scientific computing
- NumPy for MATLAB users
- pandas
- Python Data Science Handbook by Jake VanderPlas
- Spyder Scientific Python Developer Environment
- Pycharm, another IDE