Luiz Paulo Favero - Data Science, Analytics and Machine Learning with R

Data science; Analytics; Machine learning; Applications; Decision making process

This chapter provides a brief introduction to data science, analytics, and machine learning, which will serve as a foundation for understanding the concepts and techniques covered throughout the book.

In this new millennium, in which it is estimated that more than 5 quintillion pieces of data are generated daily from social networks, the internet of things, digital photos, consumer monitoring, and other sources, the understanding of the importance of data science in its various aspects is of fundamental importance for scientific and technological advancement, economic and social development, environmental preservation, business success, the discovery and exploration of new areas of knowledge, understanding of historical events, and even the protection of life on our planet!

Data science is therefore naturally multidisciplinary. We found examples of data science applications in engineering, physics, medicine, biology, education, psychology, pedagogy, law, politics, public security, economics, sociology, business, marketing, astronomy, anthropology, human resources, meteorology, geography, and history. We will hardly be able to find a field of study in which it is not possible to investigate phenomena through the techniques and procedures of data science.

There are many aspects that data science encompasses. Many are the professions associated with these aspects because every day we witness the emergence of new terminologies and positions in the market and in the academic world. Examples include data scientist, data engineer, data architect, data analyst, business intelligence analyst, machine learning engineer, database administrator, computer engineer, information technology facilitator, edge computing master, cybercity analyst, personal data broker, machine manager, digital tailor, augmented reality (AR) journey builder, user experience (UX) writer, DevOps (developers and IT operation professionals), among many other professions. And these professionals work, as we mentioned, in the most diverse sectors! We find data engineers in the food and beverage industry as well as AR journey builders in the gaming industry.

provides an overview of the relationship among data science, analytics, and machine learning.

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Through , it is possible to verify, therefore, that data science encompasses knowledge about data analysis (analytics) as well as knowledge about methods, algorithms, Big Data, and decision-making processes.

The Analytics pillar involves knowledge and fundamentals about measurement scales of variables, mathematics, statistics, calculus, linear algebra, operations research, geometry, and trigonometry. It is not possible to find a data scientist who does not present some solidity of knowledge in these fields; however, if you find one who identifies this way, this person will be, at most, a pusher of codes and buttons!

The pillar referring to methods, algorithms, and Big Data refers to the knowledge for implementing routines and codes from specific languages such as R, Python, Stata, Julia, SQL, Java, C/C ++, Scala, SAS, Matlab, SPSS, among many others. Note that the implementation of routines necessarily involves knowledge about the fundamentals of Analytics so mistakes are not made when writing the codes. It is very common to find programmers who do not know the statistical foundations of a particular modeling technique and end up writing code that does not reflect, for example, the nature of the variables under study. The outputs obtained in this case will be, to say the least, inaccurate and sometimes completely wrong!

In this pillar, we can still find the fundamentals of Big Data, which correspond to the simultaneous occurrence of five characteristics, or dimensions of the data: volume, speed, variety, variability, and complexity of the data.

The exacerbated volume of data arises, among other reasons, from the increase in computational capacity and the increase in the monitoring of the most diverse phenomena. The speed with which data becomes available for treatment and analysis, due to new forms of collection that use, for example, electronic tags and radiofrequency systems, is also visible and vital for the decision-making processes. The