Jalil Villalobos Alva - Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks

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Welcome to Beginning Mathematica and Wolfram for Data Science.

Why is data science important nowadays? Data science is an active topic that is evolving every day; new methods, new techniques, and new data is created every day. Data science is an interdisciplinary field that involves scientific methods, algorithms, and systematic procedures to extract data sets and thus have a better understanding of the data in its different structures. It is a continuation of some theoretical fields of data analysis such as statistics, data mining, machine learning, and pattern analysis. With a unique objective, data science extracts quantitative and qualitative information of value from the data that is being recollected from various sources, thus enabling one to objectively count an event, either for decision making, product development, pattern detection, or identification of new business areas.

Data science carries out a series of processes to solve a problem, including data acquisition, data processing, model construction, communication of results, and data monitoring or model improvement. The first step is to formalize an objective in the investigation. From the object of the investigation, we can proceed to the sources of the acquisition of our data. This step focuses on finding the right data sources. The product of this path is usually raw data, which must be processed before it can be handled. Data processing includes transforming the data from a raw form to a state in which it can be reproduced for the construction of a mathematical model. The construction of the model is a stage that is intended to obtain the information by making predictions in accordance with the conditions that were established in the early stages. Here the appropriate techniques and tools are used that are comprised of different disciplines. The objective is to obtain a model that provides the best results. The next step is to present the outcome of the study, which consists of reporting the results obtained and whether they are congruent with the established research objective. Finally, data monitoring has the intention to keep the data updated, because data can change constantly and in different ways.

Many techniques exist, and this list only shows a part of them. Since research on data science, machine learning, and artificial neural networks are constantly increasing.

This book is intended for readers who want to learn about Mathematica/Wolfram language and implement it on data science; focused on the basic principles of data science as well as programmers outside of software developmentthat is, people who write code for their academic and research projects, including students, researchers, teachers, and many others. The general audience is not expected to be familiar with Wolfram language or with the front-end program Mathematica, but little or any experience is welcome. Previous knowledge of the syntax would be an advantage to understand how the commands work in Mathematica. If this is not the case, the book provides the basic concepts of the Wolfram language syntax, the fundamental structure of expressions in the Wolfram language, and the basic handling and understanding of Mathematica notebooks.

Prior knowledge or some experience with programming, mathematical concepts such as trigonometric function, and basic statistics are useful; some understanding of mathematical modeling is also helpful but not compulsory.

Wolfram language is different from many other languages but very intuitive and easy to learn.

The book aims to teach the general structure of the Wolfram language, data structures, and objects, as well as rules for writing efficient code while also teaching data management techniques that allow readers to solve problems in a simple and effective way. We provide the reader with the basic tools of the Wolfram language, such as creating structured data, to support the construction of future practical projects.