🐼

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.

7 lessons ~2h total 0 completed+700 XP available

Start Track

Curriculum

📊

Module 1: DataFrames

0/1

Load and explore financial data with pandas.

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

Intermediate12 minpandasDataFrame

📈

Module 2: Returns & Rolling Stats

0/1

Compute log returns and rolling volatility.

Log Returns & 20-Day Volatility

Intermediate15 minlog returnspct_change

🔗

Module 3: Multi-Asset Analysis

0/2

Correlations, merge operations, and pivot tables.

Correlation Matrix of Stock Returns in pandas

Intermediate15 mincorr()correlation matrix

GroupBy: Split-Apply-Combine Stats per Ticker

Intermediate10 mingroupbysplit-apply-combine

📉

Module 4: Technical Indicators

0/1

Implement SMA, EMA, RSI, and Bollinger Bands.

SMA, EMA & RSI in Python

Intermediate18 minSMAEMA

🧹

Module 5: Data Cleaning

0/1

Handle missing values, duplicates, and outliers.

Cleaning Messy Financial Data

Intermediate12 mindropnafillna

🔀

Module 6: Merging Datasets

0/1

Join price data with fundamental data.

Merging Price & Fundamentals Data

Intermediate15 minpd.mergemerge

Certificate of Completion

Finish all 7 lessons to earn a Data Manipulation certificate you can add to your LinkedIn and CV.

7 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

intermediate7 lessons

Back to Dashboard

🐼