Readership: Undergraduate students in economics finance and statistics.The book is a well‐narrated introduction to mining of financial data for undergraduate students. In fact, the appropriate readership seems to be second or third year students in business schools, economics departments with a course or two on statistical methods under their sleeves. An exposure to some algorithmic computational methods would also be a desirable background of a student attending a course based on this book. Such a course would be also of interest for those undergraduate students in mathematics and computer science that are interested in seeing solution of economics and financial problems through ‘big data’ approach.

• Financial Analytics Using R

Finally, the book could serve as a first guide to machine learning and data mining methods for a self‐taught financial analyst‐to‐be.The focus of the text is on presenting and developing modern computational tools for performing financial analytics in the form of ‘a laptop laboratory for data sciences’. This so‐called laptop laboratory is built based on the statistical language R and is supported by numerous add‐in packages facilitating some sophisticated and problem‐specific computational tools.

Additionally, in the chapter on data exploration, the RSQLite Package that bridges the R and SQL database exploration software has been utilised. Data science is a relatively new field, and the book delivers a convincing account of practical financial analytics problems that can be approached from data mining perspective and through algorithmic analysis. The latter differentiates it from a typical financial statistics textbook. The hands‐on data approach is promoted throughout the book with a considerable success. It is evident that the content was previously tested in a classroom and thus it should be relatively easy to use the book as a foundation for a course on financial and business analytics.As opposed to other attempts in delivering data analytics to beginners, the book balances well between the elements of programming and the theoretical foundations that underpin the methodology without trivialising either of them. It starts with a gentle introduction to R that is followed by a chapter on fundamentals of financial statistics.

In the appendices, the reader will find complementary material covering statistical theory and basic probability distributions. As a result, the book is, to a great extent, self‐contained, allowing a reader who has gaps in assumed prerequisite knowledge (which, as mentioned above, ideally would include introduction to basic algorithmic programming and a first course in statistics) to fill them without a need for additional texts or supplementary material. From the fourth chapter onwards, the book focuses on important topics for financial and business practitioners. Among them, we find models for financial securities, elements of risk analysis, time series analysis, investment efficiency, portfolio theory, predicting stock valuation, gauging the market sentiment, trading strategy, option pricing and some others. The accurate selection of data sets and examples motivates and illustrates a fairly wide range of methods that are essential for financial and business strategists. However, the text aiming at beginners must limit its methodological contents either in the range or in the depth.

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Seemingly, the authors have decided to follow the former path by giving a quite wide range of topics that are explained in a compact fashion and are well supported by algorithmic methods. In some parts of the text, the discussion of the code and algorithm dominates which may somewhat limit insight to analytical aspects of the problem at hand. Nevertheless, it provides important insight into how complex computational tools are built to address context specific problems. This is an important part of education in analytical thinking with the use of software engineering and thus do not affect value of the book for a new researcher adept in the field of financial analytics.From a general point of view, the material covered by book seems to be very adequate for the first course on big data and data mining methods for financial applications. The chosen topics are diverse and represent well the subject along with its practical implications. Each chapter is followed by a list of exercises, which occasionally appear to be somewhat short.

Sometimes, the focus of exercises is more on some minor technical details in programs or methods rather than on the ‘general’ and universal aspects of the methodology. However, considering that the book discusses many examples and leads a reader through data analyses in a rather detailed way, this does not impact dramatically the usefulness of the book. Nevertheless, for an instructor using the book in a classroom, there will be a necessity to extend the lists of exercises. The book could also benefit from a list of projects that would test a student's acquired abilities of utilising the lessons learned in financial practice. There seem to be no internet resources for the book, which is slightly disappointing because it would give an opportunity to update the book both through upgraded computational tools and relevant practical data sets. For example, a file with the R codes used throughout the book would save the typing effort that currently is needed if a reader intends to access the computational tools.

Financial Analytics Using R

This is a simple example of enhancement that certainly would be appreciated by students if available on a dedicated website.Overall, the book is a valuable addition to the growing introductory educational literature on data analytics methods in finance. It can be utilised as an in‐classroom textbook and is highly recommended for this purpose.