Preface

Mastodon https://fosstodon.org/@rforanalytics

This is compilation of notes for R for Data Analytics. These notes are used as learning material in R for Research, R for Financial Analytics and R for Data Analytics workshops.

Please contact using the Contact form or for queries related to the workshops.

There are four parts

Part-I covers the following topics

  • Getting Started
  • R Data Types & Data Structures
  • A Short Introduction to R Programming
  • Data Exploration: Preprocessing, Transformation
  • Graphics in R

Part-II covers topics in Financial Modelling

  • Linear Regression
  • Panel Regression
  • Technical Analysis
  • VaR Forecasting and GARCH models

Part-III covers topics in Machine Learning

  • Introduction to Sampling and Resampling methods
  • Introduction to Logistic Regression and K-Nearest Neighbour
  • Machine Learning: CART (Classification & Regression Trees)
  • Text Mining example

Part-IV covers selected topics on using R for Research

  • Bibliometric analysis using R

The data files used in these notes are available here https://github.com/singh1985/rforanalytics/tree/master/data

About the author

Abhay Singh is an Associate Professor in applied finance, his primary area of research is in Financial Risk Modelling, including Econometrics and Multivariate Analysis, Investment Analysis and Asset Pricing. He is leading research projects in data science with a strong focus on interdisciplinary research in data analytics and financial analytics. His ongoing projects include applications of Machine Learning in FinTech and to improve business processes, Multivariate Analysis using Vine Copulas, Financial Risk Modelling and forecasting using Econometric methods, Social Media Analytics and Text Mining. He has published my research in numerous international research outlets including high quality ABDC ranked A/A* journals.

He has over a decade of experience in data analytics and quantitative finance research using statistical software. He is a big believer in open-source software and have been using R for data analytics and statistical computing including quantitative research methods for several years. In 2017, he published his first book, “R in Finance & Economics: A Beginners Guide” which focusses on quantitative research methods in Finance/Econometrics using R. He is recognised as an expert in telling stories using data analytics and visualisation and consults on academic and industry research projects.

He is usually tweeting about R, economics, finance and politics