商业分析课程

第1周主题：Module 1.1: Introduction to ML and Python An introduction to Machine Learning concepts and Python as a tool for ML. Live session: Staff and student introductions, course objectives, assessments overview, and discussion of ML fundamentals. Self-directed learning: Complete introductory materials before the session. | Module 1.1 Coding Bootcamp Introduction to Python programming and Jupyter Notebook setup. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 1.1: Introduction to ML and Python An introduction to Machine Learning concepts and Python as a tool for ML. Live session: Staff and student introductions, course objectives, assessments overview, and discussion of ML fundamentals. Self-directed learning: Complete introductory materials before the session. | Module 1.1 Coding Bootcamp Introduction to Python programming and Jupyter Notebook setup.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第2周主题：Module 1.2: Python coding for ML Hands-on session covering Python data structures, libraries (pandas, numpy), and basics of coding for ML. Live session: Guided coding demonstrations and exercises. Self-directed learning: Review Python examples and practice exercises. | Module 1.2: Coding Bootcamp Practicing Data Types, Loops, and Conditionals in Python 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 1.2: Python coding for ML Hands-on session covering Python data structures, libraries (pandas, numpy), and basics of coding for ML. Live session: Guided coding demonstrations and exercises. Self-directed learning: Review Python examples and practice exercises. | Module 1.2: Coding Bootcamp Practicing Data Types, Loops, and Conditionals in Python”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第3周主题：Module 2.1: Cluster Analysis Exploration of unsupervised learning through clustering methods. Live session: Theory and examples of k-means, hierarchical clustering, and evaluation metrics. Self-directed learning: Study clustering case studies. | Module 2.1: Coding Bootcamp Clustering datasets and visualizing results. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 2.1: Cluster Analysis Exploration of unsupervised learning through clustering methods. Live session: Theory and examples of k-means, hierarchical clustering, and evaluation metrics. Self-directed learning: Study clustering case studies. | Module 2.1: Coding Bootcamp Clustering datasets and visualizing results.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第4周主题：Module 2.2: Classification Introduction to supervised classification methods (e.g., decision trees and Random Forest). Live session: Classification theory, use cases, and demonstrations. Self-directed learning: Readings and exercises on classification. | Module 2.2: Coding Bootcamp Building Decision Tree classifiers in Python. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 2.2: Classification Introduction to supervised classification methods (e.g., decision trees and Random Forest). Live session: Classification theory, use cases, and demonstrations. Self-directed learning: Readings and exercises on classification. | Module 2.2: Coding Bootcamp Building Decision Tree classifiers in Python.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第5周主题：Module 2.3: Classification Evaluation Focus on evaluating classification models. Live session: Confusion matrix, accuracy, precision, recall, ROC, and AUC. Self-directed learning: Review examples of evaluation techniques. | Module 2.3: Coding Bootcamp Calculating and interpreting evaluation metrics. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 2.3: Classification Evaluation Focus on evaluating classification models. Live session: Confusion matrix, accuracy, precision, recall, ROC, and AUC. Self-directed learning: Review examples of evaluation techniques. | Module 2.3: Coding Bootcamp Calculating and interpreting evaluation metrics.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第6周主题：Module 2.4: Other Classification Techniques Exploring additional classifiers like SVM, k-NN, and Naive Bayes. Live session: Discussion of alternative approaches and their pros & cons. Self-directed learning: Study supplementary materials on advanced classifiers. | Module 2.4: Coding Bootcamp Implementing and comparing advanced classification methods. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 2.4: Other Classification Techniques Exploring additional classifiers like SVM, k-NN, and Naive Bayes. Live session: Discussion of alternative approaches and their pros & cons. Self-directed learning: Study supplementary materials on advanced classifiers. | Module 2.4: Coding Bootcamp Implementing and comparing advanced classification methods.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第7周主题：Module 3.1: Text Preparation for ML Working with unstructured text data for ML. Live session: Text cleaning, tokenization, and feature extraction techniques. Self-directed learning: Readings and exercises on text preprocessing. | Module 3.1: Coding Bootcamp Text preprocessing in Python. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 3.1: Text Preparation for ML Working with unstructured text data for ML. Live session: Text cleaning, tokenization, and feature extraction techniques. Self-directed learning: Readings and exercises on text preprocessing. | Module 3.1: Coding Bootcamp Text preprocessing in Python.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第8周主题：Module 3.2: Text-Driven ML Applying ML techniques to text data. Live session: Sentiment analysis, text classification, and business use cases. Self-directed learning: Review case studies in Text-Driven ML. | Module 3.2: Coding Bootcamp Building models for sentiment and text classification. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 3.2: Text-Driven ML Applying ML techniques to text data. Live session: Sentiment analysis, text classification, and business use cases. Self-directed learning: Review case studies in Text-Driven ML. | Module 3.2: Coding Bootcamp Building models for sentiment and text classification.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第9周主题：Module 3.3: Recommender Systems Designing and implementing recommendation algorithms. Live session: Collaborative and content-based filtering, hybrid approaches. Self-directed learning: Study examples of recommender systems in practice. | Module 3.3: Coding Bootcamp Students work on their ML project and may seek support during this session. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 3.3: Recommender Systems Designing and implementing recommendation algorithms. Live session: Collaborative and content-based filtering, hybrid approaches. Self-directed learning: Study examples of recommender systems in practice. | Module 3.3: Coding Bootcamp Students work on their ML project and may seek support during this session.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第10周主题：Module 4.1: Artificial Neural Networks (ANN) Understanding the fundamentals of ANNs and their components. Live session: Neurons, layers, activation functions, and backpropagation. Self-directed learning: Readings on ANN theory and applications. | Module 4.1: Coding Bootcamp Implementing simple neural networks with Python. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 4.1: Artificial Neural Networks (ANN) Understanding the fundamentals of ANNs and their components. Live session: Neurons, layers, activation functions, and backpropagation. Self-directed learning: Readings on ANN theory and applications. | Module 4.1: Coding Bootcamp Implementing simple neural networks with Python.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第11周主题：Module 4.2: Deep Learning (DL) Understanding advanced deep learning architectures, including CNNs and RNNs, and their applications to image and sequence data. Live session: Oral presentations + Introduction to CNNs and RNNs in DL. Self-directed learning: Incorporate feedback and explore DL architectures. | Module 4.2: Oral Examination Students present progress on their final ML projects and receive feedback. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 4.2: Deep Learning (DL) Understanding advanced deep learning architectures, including CNNs and RNNs, and their applications to image and sequence data. Live session: Oral presentations + Introduction to CNNs and RNNs in DL. Self-directed learning: Incorporate feedback and explore DL architectures. | Module 4.2: Oral Examination Students present progress on their final ML projects and receive feedback.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第12周主题：Module 4.3: Introduction to LLMs Exploring Large Language Models and their foundations. Live session: Transformers, pre-training/fine-tuning, attention mechanisms. Self-directed learning: Review LLM resources and examples. | Module 4.3: Assignment support Students receive help finalizing their ML projects. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 4.3: Introduction to LLMs Exploring Large Language Models and their foundations. Live session: Transformers, pre-training/fine-tuning, attention mechanisms. Self-directed learning: Review LLM resources and examples. | Module 4.3: Assignment support Students receive help finalizing their ML projects.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。

第13周主题：Module 4.4: LLMs’ Applications & Course Wrap-Up Examining real-world applications of LLMs and reflecting on course learning. Live session: Student Q&A, final thoughts, and feedback session. Self-directed learning: Submit final project and review key learnings. | Module 4.4: Assignment support Final drop-in session to assist with project submission. 本周先完成 Lecture/Reading 的概念梳理，再用 tutorial 或题目验证理解，重点是把概念转成可解释的步骤。 学习重点：围绕“Module 4.4: LLMs’ Applications & Course Wrap-Up Examining real-world applications of LLMs and reflecting on course learning. Live session: Student Q&A, final thoughts, and feedback session. Self-directed learning: Submit final project and review key learnings. | Module 4.4: Assignment support Final drop-in session to assist with project submission.”识别关键术语、方法边界和常见误区，输出一页结构化笔记（定义、方法、例题、易错点）。 实操建议：至少完成 2-3 个与本周主题直接相关的练习，并记录每题的假设与推导过程，避免只记结论。 交付与复盘：对照 BSAN7212 的 assessment 要求检查本周产出，保留可复用模板用于后续周和考前复盘。