AI for Finance & Analytics Mastery Bundle

Get an overview of Agentic AI, what you will build in this course, and how AI agents are used in real finance and business workflows.

Learn how to set up your development environment, required tools, and libraries needed to build and test agent-based AI systems.

Understand how to configure the OpenAI API, manage API keys securely, and connect your environment to large language models.

Build your first AI agent and understand how agents receive instructions, generate responses, and perform basic reasoning tasks.

Learn how to change model parameters, experiment with prompts, and test agent behavior for accuracy and consistency.

Create a stateless AI agent that processes each request independently, ideal for simple queries and one-time tasks.

Develop a stateful AI agent that maintains memory across interactions, enabling context-aware and multi-step reasoning.

Explore common tools used in financial AI agents and understand how tools extend agent capabilities beyond text generation.

Learn how to integrate real financial data sources so your agents can access market data, ratios, news, and filings.

Build custom data-fetching functions that allow AI agents to retrieve financial metrics, news, and SEC filings reliably.

Test your AI agents using real finance questions and evaluate how they fetch data, analyze results, and explain insights.

Understand why multi-agent systems matter in finance and how different AI agents collaborate like real analyst teams.

Set up advanced configurations and tools required to support multi-agent workflows and collaborative AI systems.

Learn how to define specialized agent roles such as researcher, analyst, and writer, and assign responsibilities clearly.

Understand safety principles, validation techniques, and best practices for using AI responsibly in financial workflows.

Implement guardrails and validation checks to prevent hallucinated numbers and ensure financial data accuracy.

Learn the principles of responsible AI, governance, compliance, and ethical considerations when deploying AI in finance.

Learn the fundamentals of artificial intelligence and how ChatGPT can assist Excel users. This video explains how AI can automate tasks, analyze data, and improve productivity in Excel.

Explore Excel’s built-in AI tool Flash Fill for quick data transformation. Learn how Excel detects patterns and automatically extracts or modifies data values.

Continue exploring Flash Fill and see how it can transform and manipulate data efficiently. You will also learn how to correct results when pattern detection needs adjustment.

Learn how to interact with ChatGPT using natural language questions. This module demonstrates how ChatGPT can answer Excel-related queries and provide useful insights.

Understand how to access and use ChatGPT effectively. This session shows how to log in, navigate the interface, and start interacting with the AI tool.

Learn how to ask ChatGPT to generate Excel formulas. You will see how natural language prompts can quickly produce accurate formulas for spreadsheet calculations.

Explore how ChatGPT can help refine formulas and solve spreadsheet problems. The video also demonstrates how ChatGPT suggests alternative solutions for dynamic calculations.

Learn how ChatGPT can transform and organize Excel data automatically. This video demonstrates formatting data and restructuring columns using AI assistance.

Discover how ChatGPT can convert unstructured information into structured Excel tables. This session shows how data from sources like PDFs can be reorganized for analysis.

Learn how ChatGPT can analyze Excel datasets and provide useful insights. You will see how AI identifies patterns and summarizes data automatically.

Explore how ChatGPT can compare multiple datasets and identify overlapping or duplicate entries. This helps streamline analysis across multiple lists or tables.

Understand how ChatGPT can generate descriptive analysis and insights from data. This video shows how AI interprets datasets and provides analytical summaries.

Learn how ChatGPT can perform sentiment analysis on datasets and visualize results. This video demonstrates how AI can categorize data and present insights using charts.

Get an overview of how generative AI is applied in financial reporting, analysis, and executive communication across modern finance teams.

Learn how to set up Python, libraries, datasets, and AI APIs required to run all finance-focused AI workflows in this course.

Understand why finance teams are integrating generative AI to improve reporting speed, insight quality, and decision support.

Learn how large language models and AI agents work, and how GPT models are used in real finance and FP&A use cases.

Learn how to generate clear ratio analysis commentary using AI, focusing on performance interpretation rather than raw calculations.

Understand how to design prompts specifically for finance tasks to ensure accurate, structured, and business-ready outputs.

Build prompts that help AI interpret income statements, balance sheets, and cash flows like a finance professional.

Use AI to automatically generate earnings summaries that highlight key performance drivers and financial outcomes.

Learn how to generate leadership-style financial narratives aligned with how CEOs and CFOs communicate performance.

Understand how to connect Power BI data to GPT using Python for automated insight generation.

Automatically convert charts and numeric trends into concise, executive-ready financial commentary using AI.

Create a live AI insights panel that updates commentary automatically when dashboard data changes.

Automate structured commentary for KPIs, ratios, and forecast variances across recurring finance reports.

Learn the standard structure of high-quality financial reports expected by CFOs, investors, and leadership teams.

Use AI to generate accurate, leadership-ready executive summaries from financial data and business updates.

Build AI-assisted checks to identify numeric mismatches, unusual variances, and reporting inconsistencies.

Understand how AI acts as a control layer to validate financial reports and improve accuracy before leadership review.

Understand the complete quant workflow from data to execution. Learn how AI and ChatGPT integrate into modern quantitative research systems.

Set up your Python environment using Anaconda and Jupyter Notebook. Learn how to configure API keys and required tools for the course.

Load financial price and sentiment datasets into Python. Understand how to inspect, visualize, and prepare data for analysis.

Learn how to fetch market data using APIs like yfinance. Build automated pipelines to download and store financial data.

Clean and structure financial data for accurate analysis. Handle missing values, dates, and outliers in time series datasets.

Create financial features like returns, volatility, and moving averages. Learn how raw data is transformed into meaningful trading signals.

Load and process sentiment datasets from news and media. Prepare structured inputs for sentiment-based trading analysis.

Use ChatGPT to generate sentiment scores from text data. Learn how AI converts qualitative information into quantitative signals.

Build a vectorized backtesting system for trading strategies. Compare strategy performance against a benchmark using historical data.

Calculate key metrics like CAGR, Sharpe ratio, and drawdown. Understand how to evaluate risk and return in trading strategies.

Use ChatGPT to explain backtest results in simple language. Generate professional insights and improvement suggestions automatically.

Build portfolios, measure risk, and optimize allocation strategies. Learn how modern quant systems manage risk and returns.

This video summarizes the complete learning journey and next steps. Understand how to continue building advanced AI-powered quant systems.