Quick Take
- Narration: Virtual Voice narrates, and the reviews suggest the print edition itself has significant production issues including missing page numbers, cut-off content, and unclear images. The audio is a secondary format problem for a title already struggling with primary format execution.
- Themes: Excel data tools, Power BI dashboards, Python for data analysis
- Mood: Overpromising and technically incomplete
- Verdict: The low rating and consistent review complaints about production quality and elementary-level content make this difficult to recommend over better-documented alternatives.
There is a category of technical book that attempts to teach three distinct professional tools in a single volume and ends up adequately covering none of them. Data Analytics with Excel, Power BI and Python sits in that category. The ambition is understandable: Excel, Power BI, and Python are the three most commonly requested tools in entry-level data analyst job postings, and a book that bridges all three would genuinely serve the market. The execution here does not fulfill that ambition.
I want to be fair to what the book is trying to do. Author Punit Prabhu has clearly designed this as a self-study resource, complete with a companion blog, a YouTube channel, and an email address for queries. That support infrastructure suggests good faith investment in the learner’s success. The problem is what the reviews consistently describe: the content is elementary to the point that reviewers with basic technical backgrounds found nothing applicable, and the print production has documented issues with missing page numbers and truncated text that make even the intended reference function difficult.
The Tool Coverage Problem
Covering Excel, Power BI, and Python in four and a half hours of audio requires a level of compression that makes the coverage feel like a glossary rather than a guide. The book’s stated outcomes are ambitious: in-depth knowledge in both Excel and Power BI, the ability to create interactive dashboards, and enough Python proficiency for data analytics work. That scope is a year of part-time study for a complete beginner, not four hours of listening. The gap between what the introduction promises and what the runtime can deliver is substantial.
Reviewer Jonathan Enchia, who expected IT-professional-level content, describes feeling cheated. That reaction reflects a mismatch between the book’s positioning and its actual depth. The content that exists is described as appropriate for students and professionals with no prior background, which may be accurate. But the book markets itself to a broader audience without signaling clearly enough where it sits on the proficiency spectrum.
Production and Format Concerns
The print edition reviews raise flags that affect the audio edition by extension. When reviewers report that the table of contents references page numbers that don’t exist in the body of the book, and that content is cut off mid-thought and doesn’t continue on the following page, these are problems that suggest the underlying manuscript needs attention regardless of format. A Virtual Voice narration of a text with these structural problems compounds the issue: what reads as abrupt in print becomes confusing in audio, with no visual context to bridge gaps.
The accompanying practice files are offered via an external link, which creates a dependency that listeners cannot access mid-session. For a book whose core value proposition is hands-on skill development, the inability to follow along interactively while listening is a meaningful limitation.
Listener Guidance
If you are a complete beginner to all three tools and want a high-level orientation before committing to a more detailed course, this might serve as a preview rather than a learning resource. But the reviews, the runtime, and the production issues point toward alternatives as the better investment. Greg Deckler’s Learn Power BI, available elsewhere in this data science library, covers the Power BI component alone with considerably more depth and a stronger track record with beginner audiences. For Python and Excel, the landscape of well-reviewed beginner resources is extensive enough that this title doesn’t distinguish itself.
Frequently Asked Questions
The synopsis mentions free practice files. Are these accessible from the Audible edition?
The practice files are offered via an external link referenced in the book content. In the audio edition, you would need to access these separately, which requires pausing playback and navigating to the companion site. This is a meaningful limitation for a book designed around hands-on skill practice.
How elementary is the Excel and Power BI content? Is it genuinely entry-level or does it assume some prior knowledge?
Reviewer feedback suggests the content is genuinely entry-level to the point that reviewers with any prior technical background found it below their level. Someone who has never opened Power BI or written a formula in Excel should be the primary audience. IT professionals and people with intermediate spreadsheet skills will likely find the coverage too basic.
Is there a better audiobook option for learning Power BI as a beginner?
Greg Deckler’s Learn Power BI is a commonly cited alternative that focuses entirely on the Power BI tool and goes into considerably more depth. It has a stronger review record and covers the full range of Power BI functionality from ingestion through dashboard publication. For a focused single-tool approach, it is the more reliable investment.
Can this book actually teach Python for data analytics in the space available?
No. Given that the total runtime covers three major tools in under five hours, the Python coverage is necessarily introductory at best. Learning Python for data analytics at a functional professional level requires substantially more time and practice than any audio-only format can provide. This book can introduce the concept of Python in a data context, not teach you to use it.
