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Statistical projects with data

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.
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Suggestions or feedback? Previous image Next image. Each year, the world generates more data than the previous year. But just because data are proliferating doesn't mean everyone can actually use them. Companies and institutions, rightfully concerned with their users' privacy, often restrict access to datasets — sometimes within their own teams. And now that the Covid pandemic has shut down labs and offices, preventing people from visiting centralized data stores, sharing information safely is even more difficult. Without access to data, it's hard to make tools that actually work.
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Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
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The purpose of these notes is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models. The notes originated in a handout introducing S-Plus, but have been substantially rewritten to target R using R Studio. Each of the subjects discussed here includes a sample application. Introducing R was last updated November
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