Python

Python for Finance – Full Course Curriculum

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Welcome to the Python learning track of QuantML — where finance professionals stop dragging formulas and start building tools. Below is the full roadmap for mastering Python for finance — from zero to dangerous (to your coworkers’ job security).

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✅ Module 0 – Environment Setup

Goal: Set up your workspace and get ready to write real code

Outcome: You’ll run your first Python script before you even cry.

Lessons:

• Installing Python (Anaconda / plain install)

• Setting up Google Colab and Jupyter Notebooks

• GitHub 101: Repos, cloning, pushing, pretending you understand Git

• Python packages for finance: pandas, numpy, matplotlib, yfinance, openpyxl, streamlit

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✅ Module 1 – Python Basics for Finance

Goal: Learn Python syntax with a finance lens

Outcome: You’ll replace basic Excel tasks with Python scripts.

Lessons:

• Data types, variables, conditionals, loops

• Functions: writing and using them like an adult

• Reading CSV/Excel files with pandas

• Creating basic reports: revenue summaries, monthly variance

• String manipulation for cleaning messy finance data

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✅ Module 2 – Financial Data Analysis

Goal: Transform raw data into real insights

Outcome: You’ll build dashboards faster than Karen can ask for “last month’s version.”

Lessons:

• Aggregating data with groupby, pivot_table, and resample

• Calculating financial KPIs (margin %, growth %, CAGR)

• Automating financial statements: P&L, balance sheet, cash flow

• Common analysis: YoY, MoM, Budget vs Actual

• Error-proofing with exception handling and validation

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✅ Module 3 – Visualization & Reporting

Goal: Make beautiful, clear reports (that don’t involve copying charts from Excel)

Outcome: You’ll create dashboards that don’t look like corporate crime scenes.

Lessons:

• Visualizing data with matplotlib and seaborn

• Interactive dashboards with Streamlit

• Exporting results to Excel with formatting

• Automating PowerPoint with python-pptx

• Building a full KPI dashboard from scratch

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✅ Module 4 – Forecasting and Time Series

Goal: Predict the future (without a crystal ball)

Outcome: You’ll build forecasting models and stop guessing revenue like an intern.

Lessons:

• Preparing time series data

• Forecasting with linear regression

• ARIMA modeling for financial data

• Using Prophet for quick forecasting (yes, it works)

• Automating monthly forecast updates

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✅ Module 5 – Stock Market Data & Analysis

Goal: Learn to analyze equity markets using Python

Outcome: You’ll build your own stock screener and analyze returns like a quant… kind of.

Lessons:

• Fetching NSE/BSE data using APIs and yfinance

• Analyzing price, volume, moving averages

• Calculating returns, volatility, Sharpe Ratio

• Screening based on fundamentals and trends

• Visualizing price trends and comparing tickers

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✅ Module 6 – Automation Projects for FP&A

Goal: Build tools that do the boring stuff for you

Outcome: You’ll automate half of your job and maybe get promoted (or replaced).

Lessons:

• Automating Excel reports from raw ERP dumps

• Building a revenue tracker from scratch

• Emailing daily/weekly reports using Python

• PowerPoint automation for review decks

• Real-world FP&A tools: Variance analyzer, Rolling Forecast builder

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✅ Module 7 – GPT & LLMs in Finance

Goal: Bring generative AI into your workflow

Outcome: You’ll build tools that explain numbers, not just calculate them.

Lessons:

• Connecting to GPT using OpenAI API

• Automating commentary generation for dashboards

• GPT for variance analysis explanation

• Natural language querying of data (SQL + GPT)

• Creating a “Chat With Your P&L” app

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✅ Module 8 – Real-Time Dashboards & APIs

Goal: Build dashboards that update themselves

Outcome: You’ll never say “Please refresh this report” again.

Lessons:

• APIs 101: REST, endpoints, tokens, your new friends

• Pulling data from external APIs (Exchange rates, market data, etc.)

• Streamlit apps with refresh timers

• Database integration with sqlite3 or SQLAlchemy

• Building a real-time financial data tracker

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✅ Module 9 – Advanced Use Cases & Projects

Goal: Combine what you’ve learned into real projects

Outcome: You’ll build a finance portfolio that slaps.

Projects:

• Executive Dashboard with Streamlit + Excel automation

• Automated Rolling Forecast Tool with ML

• GPT-powered FP&A commentary generator

• Indian stock market screeners

• Financial Risk Analyzer (Value at Risk, Beta, stress test)

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✅ Module 10 – Resume Projects & Portfolio

Goal: Build proof-of-work that isn’t a fake Udemy certificate

Outcome: You’ll walk into interviews with a dashboard, not a story.

What You’ll Do:

• Package your projects into a portfolio

• Deploy dashboards publicly (Heroku, Streamlit Cloud)

• Add GitHub projects with clean README files

• Use your projects to ace case studies and technical interviews

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🎓 Certification (Optional / Coming Soon)

When you complete the track (yes, actually finish it, not just read the titles), you can earn a QuantML Python for Finance Certificate — complete with a showcase page and portfolio badge.

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Or don’t. Stay in Excel. I’m sure that VLOOKUP will love you back someday.