# Syllabus

Week | Lecture | Topic | Learning Goals | Python Libraries | Homework / Labs | Assessment |
---|

Week 1 | Lecture 1 | Introduction to Data Science | | - | H1 | |

Week 1 | Lecture 2 | Problem Understanding | | Anaconda and Jupyter | | Assignment 1 |

Week 2 | Lecture 3 | Obtaining Data | Obtain, Discuss | Requests + JSON | H2 | |

Week 2 | Lecture 4 | Data, Grammar and Engineering | Discuss, manipulate and Consolidate | Numpy, Scipy, Pandas | H3 | |

Week 3 | Lecture 5 | Exploratory Data Analysis | Interpret | Seaborn + Matplotlib | H4 | |

Week 3 | Lecture 6 | Geo-Visualisation | Describe, Analyse | Geopandas | | Assignment 2 |

Week 4 | Lecture 7 | How are things connected? - Networks and Spatial Weights | Describe, Analyze | Networkx + Osmnx | H5 | |

Week 4 | Lecture 8 | Supervised Learning - Regression | Apply | Scikit-learn | H6 | |

Week 5 | Lecture 9 | Regularisation and Dimensionality Reduction | Infer | | H7 | |

Week 5 | Lecture 10 | Bayesian vs Frequentist | Apply | | | Assignment 3 |

Week 6 | Lecture 11 | Unsupervised Learning - Clustering | Apply | | H8 | |

Week 6 | Lecture 12 | Classification | Apply | | H9 | |

Week 7 | Lecture 13 | Causal Inference | Infer | | | Assignment 4 |

Week 10 | | | | | | Final Project |