I have masterâ€™s degrees in math and engineering and worked in the operations research field, but I have been retired for a number of years. This book sounded like an enjoyable way to learn about present-day techniques and tools and see how things have changed since I left the world of work. Despite my having a technical background better than I would expect to be common in its target audience, I found Data Science for Dummies very hard to understand and suspect most people would find it even harder,The topics covered seem like the right ones, and there are â€œcheat sheetsâ€ and updates online that could be very helpful in keeping the content current. The problem is that the book is so poorly written that it is not going to be useful unless you already know the material.The author takes pains to define the many technical terms used in data science, but most of the definitions are poorly done and almost seem circular. Time and again the definition includes the very terms that are being defined or uses the same word repeatedly and unclearly, e.g., â€œIf science is a systematic method by which people study and explain domain-specific phenomenon[SIC] that occur in the natural world, then you can think of data science as the scientific domain thatâ€™s dedicated to knowledge discovery via data analysis.â€Despite the many definitions provided, many explanations include technical terms that the average non-engineering person would not know. For example, there is a detailed description of the MapReduce programming paradigm that forms the basis for the Hadoop data-processing platform that is apparently used extensively in data science. The explanation uses terms like â€œkey-value pairsâ€ without defining them. When I gave up trying to understand the explanation I showed it to a recently retired computer science professor and a still-employed engineer who is familiar with Hadoop. Both found it very difficult to understand.  In this case the explanation MIGHT have been improved greatly by a simple example.In addition to the problems above, the writing style is awkward.  It is extremely repetitive. For example, data science and data engineering are defined and distinguished from each other three times in the first three chapters; in several cases, exact sentences are repeated.  The Dummies books are usually organized so that you can use them as a reference and would not have to read straight through, but the level of repetition here is unnecessary and does not make for an enjoyable read. Word usage is also poor, which can contribute to misunderstanding, e.g., â€œSince a data scientist must also have subject matter expertise in the particular area in which they work, this requirement generally precludes a data scientist from having expertise in data engineering.â€  â€œPrecludesâ€? I can see how such expertise might be fairly rare, but it certainly is not precluded. And sentences are poorly constructed, e.g., â€œMachine learning is the practice of applying algorithms to learn from and make automated predictions about data.â€  No, "machine learning" learns from data, but it does not make predictions about data.In sum, this book is likely to leave most of its intended audience as frustrated as I was. This is a topic of great interest and importance today, but I suggest you look elsewhere for an introduction.