Python for Finance: Portfolio Statistical Data Analysis

Offered By
Coursera Project Network
In this Guided Project, you will:

Perform exploratory data analysis and visualization of financial data

Portfolio allocation and calculate portfolio statistical metrics

Perform interactive data visualization using Plotly Express

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project, we will use the power of python to perform portfolio allocation and statistically analyze the performance of portfolio using metrics such as cumulative return, average daily returns and Sharpe ratio. We will analyze the performance of following companies: Facebook, Netflix and Twitter over the past 7 years. This project is crucial for investors who want to properly manage their portfolios, visualize datasets, find useful patterns, and gain valuable insights such as stock daily returns and risks. This project could be practically used for analyzing company stocks, indices or currencies and performance of portfolio. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Data ManipulationFinancial AnalysisPython ProgrammingData Visualization (DataViz)Finance

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the problem statement and business case

  2. Import datasets and libraries

  3. Perform random asset allocation and calculate portfolio daily return

  4. Perform random asset allocation and calculate portfolio daily return

  5. Perform portfolio data visulaization

  6. U​nderstand and calculate portfolio statistical metrics

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.