Data Analysis portfolio in R, Python and Excel consists of 6 projects:
Is a research project that takes a closer look at newly introduced Solana (SOL) futures on Deribit. As the futures contracts were introduced less than 2 months ago at the time of this research there is very limited data available, however some contracts are reaching their maturity therefore providing valuable information on how efficient the market is. The project has a vey fast and efficient way of collecting derrivative data from Deribit API.
This folder consists of 1 data analysis example in excel. The project analyses beer data and goes in to very extent analysis utilising pivot tables, pivot charts, slicers, and data mapping. The files can not be previewed in excel, therefore feel free to download and go through the analysis that way.
This project is written in R code and by impoting stock return data and data on the 5 factors from Kenneth French's website, the model automatically runs the regressions and collects all of the necessary data, i.e. Alphas, Betas, p-values and t-stats.
This project is written in R code and by importing stock return data and data on the 3-factors from Kenneth French's website, the model automatically runs the regressions, plots results: showing the approximation of the stocks to the Capital Market Line. It also collects all of the necessary outputs.
In this project I analyse inflation expectations data from the European Commission, the analysis is done in R code and also a report is written explaining all of the models used and conclusions achieved. In this Project I employ such models as: Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), Autocorrelation-functions (ACF), Augmented Dickey Fuller Tests (ADF), Bayesian Information Criterion tests (BIC), Autoregressive Distributed Lag model (ADL), Granger Causality tests and etc.
This project analyses survey data on consumer preferences for shopping online. The analysis is conducted in R. For the analysis I employed: Analysis of Variance ANOVA, OLS regressions, correlation analysis and supervised machine learning methods like clustering and binomial trees.