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Pandas & Time-Series for Finance

Data Manipulation

Harness pandas to load, clean, and transform financial time-series. Compute rolling statistics, log returns, and volatility surfaces.

Recommended first: Python Foundations: variables, loops, functions

8 lessons ~2h total 0 completed+800 XP available
Start Track

Curriculum

📊

Module 1: DataFrames

0/1

Load and explore financial data with pandas.

1

Pandas DataFrame Basics: describe(), head() & dtypes

Intermediate12 min
📈

Module 2: Returns & Rolling Stats

0/2

Compute log returns and rolling volatility.

1

Log Returns & 20-Day Volatility

Intermediate15 min
2

Resample Daily Prices to Monthly Returns

Intermediate10 min
🔗

Module 3: Multi-Asset Analysis

0/2

Correlations, merge operations, and pivot tables.

1

Correlation Matrix of Stock Returns in pandas

Intermediate15 min
2

GroupBy: Split-Apply-Combine Stats per Ticker

Intermediate10 min
📉

Module 4: Technical Indicators

0/1

Implement SMA, EMA, RSI, and Bollinger Bands.

1

SMA, EMA & RSI in Python

Intermediate18 min
🧹

Module 5: Data Cleaning

0/1

Handle missing values, duplicates, and outliers.

1

Cleaning Messy Financial Data

Intermediate12 min
🔀

Module 6: Merging Datasets

0/1

Join price data with fundamental data.

1

Merging Price & Fundamentals Data

Intermediate15 min

Certificate of Completion

Finish all 8 lessons to earn a Data Manipulation certificate you can add to your LinkedIn and CV.

8 lessons remaining

What You'll Be Able To Do

  • Clean and transform large financial datasets
  • Merge price, fundamentals & macro data
  • Engineer features for quant models

Difficulty Breakdown

intermediate8 lessons