Better business decisions built on data science are the promise at the center of this short audiobook, and one practical detail needs to come before that promise: the entire production, narration included, is in Spanish, not English, despite the English-language title and category tags on the listing itself. Herbert Jones has built a substantial catalog of compact, single-subject primers like this one, and Data Science condenses its topic into a single sitting instead of a multi-day commitment. In just under two hours, the audiobook moves through what data science actually is, how big data functions inside a modern company, exploratory data analysis, data mining, text extraction, a handful of machine learning algorithms, data modeling, and data visualization, closing with practical tips for applying all of it to a growing business.
Ernesto Tissot narrates in a clear, functional register well suited to an introductory technical script: no theatrics, correct handling of the English-derived technical vocabulary that inevitably surfaces inside Spanish-language tech writing, and a pace brisk enough that the short runtime never drags. What his narration cannot do is elevate material that is, by design, a broad survey rather than a deep dive. Data Science reads like a syllabus delivered efficiently rather than a sustained argument, which is a fair trade for a listener who wants orientation rather than a full course, and a genuine limitation for anyone hoping for more.
Herbert Jones's name turns up across a wide spread of similarly scoped guides covering adjacent technical subjects, the kind of prolific, single-topic primer catalog that has become common in Spanish-language self-publishing over the past decade. That context matters for calibrating expectations: this is not a flagship title from a major publisher with a dedicated production budget, it is one entry in a large, efficient library built to answer one specific question as directly as possible before moving to the next. Judged against that goal rather than against a university textbook, the format choice makes more sense.
The rating itself, a 3.9 average from 28 ratings, sits in reasonable company with these kinds of small-catalog technical primers: solid but not remarkable, reflecting a book that does its narrow job competently rather than a title trying to be everything to everyone. Twenty-eight ratings is a small enough sample that a single harsh or glowing review can swing the average more than it would on a bigger title, worth keeping in mind when weighing the specific reviews below against the number itself.
Built to Close the Gap for Total Beginners
The case for listening rests on scope, not depth. A listener who has heard the phrase data science used at work without a clear sense of what the field actually covers gets a fast, structured answer here: the distinction between data science and ordinary statistics, how large-scale data collection reshapes decision-making, and where machine learning actually fits into that picture. One reviewer, rating the book highly, praised how clearly it explained concepts he wished he had encountered earlier in his own work in geospatial statistics, a specific endorsement from someone already working adjacent to the field rather than encountering it cold.
A second reviewer called it a solid summary for understanding the foundations of data science, useful groundwork for anyone about to step into the field, and that matches how the book is actually built: definitions and framing before technique, technique before application. That order makes the short runtime feel intentional rather than rushed, since no attempt is made to cram in coding walkthroughs or statistical formulas a genuinely comprehensive data science course would need to include. The tradeoff is a book that answers what and why far more thoroughly than it answers how.
Data science as a discipline has moved from a niche technical specialty to something closer to a baseline literacy expected across marketing, finance, healthcare, and operations roles alike, which is exactly the gap this audiobook is built to close for a total beginner. It will not turn anyone into a data scientist in under two hours, no single format could, but it gives a working vocabulary: the terms colleagues in that role actually use, and roughly what those terms mean in practice.
Missing Worked Examples in an All-Spanish Recording
A third reviewer landed at the opposite end, describing the content as conceptual rather than practical and pointing out the absence of concrete, worked examples, a fair complaint for anyone hoping to walk away able to run an actual analysis afterward. This audiobook explains what exploratory data analysis or data modeling accomplish in principle; it does not walk a listener through building either one from scratch. Audio format compounds that gap: text mining and data visualization both benefit from seeing an example rendered on a page or screen, something a listen-only format cannot supply no matter how clearly Tissot reads the underlying material.
The bigger practical question is the language itself. Every review currently attached to this listing is written in Spanish, and the synopsis confirms the production is Spanish-language from start to finish. That is not a defect. Plenty of Spanish-speaking listeners are exactly the intended audience and are well served by a clear, efficient primer like this one, but an English-speaking shopper who lands on the listing expecting an English audiobook on the strength of the English title and category tags alone needs to know that before buying, not after pressing play on the first chapter.
Tissot's Steady Delivery Works Best for Spanish Speakers
Spanish-speaking listeners curious about data science as a career path or a practical business tool, with no appetite for a multi-hour technical course, get the most out of this one. Tissot's steady, unhurried delivery makes the material easy to absorb across a single commute or a gym session, and the closing chapter's suggestions for applying data science inside an actual business give the short runtime a practical landing point instead of ending on pure theory. Listeners already comfortable with basic statistics will move through the early chapters quickly and can treat the back half as the real payoff.
English-only listeners should look elsewhere first, since nothing about this listing points to an English-language edition existing. Anyone already working in data science, or hoping for hands-on coding walkthroughs and real datasets to practice against, will find the pace too introductory and the content too general to justify the credit. A beginner's overview, delivered efficiently and entirely in Spanish, is exactly what this audiobook is, and exactly what it should be judged against rather than against a longer, more technical course it was never trying to be.
Questions Listeners Ask
Is this data science audiobook in English or Spanish?
The entire production, narration included, is in Spanish. The listing's title and category tags are in English, but nothing in the actual audiobook or its attached reviews is, so English-only listeners should look for a different title.
Does this audiobook go deep enough for someone already working in data science?
No. One reviewer specifically calls it conceptual rather than practical, without concrete examples, and the book is built as a broad beginner's overview rather than a technical deep dive, so listeners with existing experience will likely find it too introductory.
How long is the Data Science audiobook?
Just under two hours, one hour and forty-eight minutes, which covers foundational concepts rather than hands-on technique.
Does the audiobook include practical exercises or coding examples?
No. It explains concepts like exploratory data analysis, data mining, and data visualization in principle rather than walking through worked examples, which one lower-rated review flags directly as a limitation.
