Coursera

AI-Driven Financial Planning, Forecasting, and Automation

Coursera

AI-Driven Financial Planning, Forecasting, and Automation

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Build multi-year forecasts and stress-test financial plans

  • Apply predictive models to forecast business metrics and value drivers

  • Design AI-powered pipelines to automate financial analysis and reporting

要了解的详细信息

可分享的证书

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最近已更新!

March 2026

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累 Finance 领域的专业知识

本课程是 Financial Analyst: AI, Excel, and Power BI Skills 专业证书 专项课程的一部分
在注册此课程时,您还会同时注册此专业证书。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 通过 Coursera 获得可共享的职业证书

该课程共有11个模块

You will apply zero-based budgeting principles to build a departmental budget from the ground up. You’ll structure and code expenses clearly, allocate costs accurately, and design a template that supports disciplined cost management and transparency.

涵盖的内容

3个视频1篇阅读材料1个作业

You will analyze budget-to-actual variances to determine their root causes and assess their business impact. You’ll interpret deviations, identify operational drivers, and prepare clear explanations that support corrective action and decision-making.

涵盖的内容

2个视频1篇阅读材料2个作业

You will create a multi-year P&L projection by integrating top-down market assumptions with bottom-up sales and cost plans. You’ll model revenue and expense drivers across multiple years and ensure assumptions are logically connected.

涵盖的内容

3个视频1篇阅读材料1个作业

You will evaluate the resilience of a financial plan by stress-testing it against adverse scenarios. You’ll run downside cases, assess margin pressure, and propose adjustments that preserve financial stability.

涵盖的内容

2个视频1篇阅读材料2个作业

You will analyze transaction-level data to isolate key drivers of revenue, cost, and margin. You’ll build pivot-based reports, interpret performance patterns, and identify the operational factors shaping financial outcomes.

涵盖的内容

3个视频1篇阅读材料2个作业

You will evaluate data completeness and reconcile discrepancies between source systems such as ERP, General Ledger, and data warehouse platforms. You’ll document findings and ensure financial accuracy across reporting environments.

涵盖的内容

3个视频1篇阅读材料3个作业

You will apply supervised-learning algorithms to forecast key business metrics using structured datasets. You’ll build and tune predictive models and evaluate forecast accuracy using appropriate performance metrics.

涵盖的内容

2个视频2篇阅读材料2个作业

You will analyze feature importance using explainable AI techniques such as SHAP and feature importance scores. You’ll interpret model outputs to identify the variables that most strongly influence business performance

涵盖的内容

2个视频2篇阅读材料3个作业

You will evaluate competing AI models for credit-risk classification using financial datasets. You’ll compare model performance using metrics such as F1 score and AUROC to determine which approach best supports risk assessment.

涵盖的内容

3个视频2篇阅读材料2个作业

You will create an automated pipeline that retrieves financial data from SEC filings, retrains models, and updates earnings forecasts. You’ll design a workflow that keeps financial insights accurate, efficient, and continuously updated.

涵盖的内容

3个视频2篇阅读材料3个作业

In this project, you will build a 12-month financial forecast using structured revenue and cost assumptions. You will develop base, optimistic, and downside scenarios to evaluate profitability under different business conditions. You will perform variance analysis by comparing forecasted results to recent performance and assess financial sensitivity to key value drivers. Finally, you will prepare a professional recommendation brief summarizing risk exposure and strategic actions. This project simulates a real FP&A assignment and demonstrates your ability to translate financial assumptions into structured analysis and executive-ready insights.

涵盖的内容

2篇阅读材料1个作业

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位教师

Professionals from the Industry
268 门课程 39,794 名学生

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常见问题

¹ 本课程的部分作业采用 AI 评分。对于这些作业,将根据 Coursera 隐私声明使用您的数据。