Data wrangling is a task of great importance in data analysis. Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be […]

# Exercises

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-3)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Volatility modelling in R exercises (Part-4)

This is the fourth part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will explore GARCH-M and E-GARCH models. We will also use these models to generate rolling window forecasts, bootstrap forecasts and perform simulations. Answers to the exercises are available here. […]

## Data visualization with googleVis exercises part 7

Table, Org Chart & Tree Map In the seventh part of our series we are going to learn about the features of some interesting types of charts. More specifically we will talk about Table, Org Chart and Tree Map. Read the examples below to understand the logic of what we are going to do and […]

## Hacking strings with stringr

This is first of the set of exercise on string manipulation with stringr Answers to the exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page. Exercise 1 use a stringr function to merge […]

## Parallel Computing Exercises: Foreach and DoParallel (Part-2)

In general, foreach is a statement for iterating over items in a collection without using any explicit counter. In R, it is also a way to run code in parallel, which may be more convenient and readable that the sfLapply function (considered in the previous set of exercises of this series) or other apply-alike functions. […]

## Data Visualization with googleVis exercises part 6

Geographical Charts In part 6 of this series we are going to see some amazing geographical charts that googleVis provides. Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin! Answers to the exercises are […]

## Volatility modelling in R exercises (Part-3)

This is the third part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use GARCH models to forecast volatility. Answers to the exercises are available here. Exercise 1 Load the rugarch and the FinTS packages. Next, load the m.ibmspln dataset from […]

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-2)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Parallel Computing Exercises: Snowfall (Part-1)

R has a lot of tools to speed up computations making use of multiple CPU cores either on one computer, or on multiple machines. This series of exercises aims to introduce the basic techniques for implementing parallel computations using multiple CPU cores on one machine. The initial step in preparation for parallelizing computations is to […]