Planned programme areas 规划中的课程方向
Six areas we are building out.
正在搭建的六个方向。
Each area below is in development. The descriptions show the intended scope so you can judge whether it fits your goals. 以下每个方向均处于筹备阶段，说明旨在呈现各课程的预期范围，方便你判断是否契合自己的目标。
Exam support
CFA study support across the curriculum覆盖全考纲的 CFA 备考辅导
Structured help across all three levels: study planning, topic review and practice-question walkthroughs spanning quantitative methods, economics, financial statement analysis, ethics, fixed income, equity and portfolio management. The aim is to clarify difficult material and build steady study habits, not to predict questions or promise a result.
围绕 CFA 三个级别的考纲提供系统辅导：包括学习计划制定、知识点串讲，以及涵盖定量方法、经济学、财务报表分析、职业伦理、固定收益、权益投资与投资组合管理等领域的真题讲解。重点在于帮助你理清难点、养成稳定的学习节奏，而非押题或承诺考试结果。
Quantitative core
Quantitative methods & financial econometrics定量方法与金融计量经济学
Foundations and applied practice in the methods finance relies on: probability and statistics, regression and inference, time-series analysis, and model specification, estimation and validation. For those who want to understand the methods properly rather than apply them as a black box.
聚焦金融领域所依赖方法的基础与应用：包括概率与统计、回归与推断、时间序列分析，以及模型设定、估计与检验。适合那些希望真正理解方法原理、而非将其当作黑箱套用的学生与从业者。
Tools
R for finance面向金融的 R 语言
Applied R for financial data work: importing and cleaning data, computing returns and volatility, statistical and econometric modelling, visualisation, and reproducible research workflows. Suited to coursework, dissertations and empirical finance projects where results need to be transparent and repeatable.
面向金融数据工作的 R 语言实操：涵盖数据导入与清洗、收益率与波动率计算、统计与计量建模、可视化，以及可复现的研究工作流。适用于课程作业、毕业论文及对结果透明性与可复现性有要求的实证金融项目。
Tools
Python for finance & data面向金融与数据的 Python
Practical Python for finance and data analysis: working with pandas and NumPy, cleaning and structuring datasets, returns and risk calculations, basic forecasting, and clear charts and reporting. Aimed at building working skills that transfer to internships, research and analyst roles.
面向金融与数据分析的实用 Python：包括 pandas 与 NumPy 的使用、数据清洗与结构化、收益与风险计算、基础预测，以及清晰的图表与报告制作。目标是培养可迁移至实习、研究及分析师岗位的实战技能。
Applied insight
ESG & sustainable-finance analyticsESG 与可持续金融分析
An introduction to working with ESG and sustainable-finance data: what the main data sources and ratings measure and where they disagree, how to handle the data carefully, and how to read ESG analytics critically rather than at face value. Framed around method and evidence, with no view taken on any specific product or investment.
介绍 ESG 与可持续金融数据的处理方法：包括主流数据来源与评级各自衡量的内容及其分歧所在、如何审慎处理这类数据，以及如何批判性地解读 ESG 分析结果。课程立足于方法与证据，不对任何具体产品或投资标的发表意见。
Applied insight
Risk & volatility modelling风险与波动率建模
Workshops on the modelling behind risk measurement: volatility models, value-at-risk and related measures, monitoring methods, and the assumptions and limitations behind each approach. The emphasis is on understanding what a model can and cannot tell you, and on validating it honestly.
围绕风险度量背后建模方法的专题课程：涵盖波动率模型、风险价值（VaR）及相关度量、监测方法，以及各类方法所依赖的假设与局限。重点在于理解模型能说明什么、不能说明什么，并对其进行诚实的检验与验证。