Quite a fascinating read! Incredible that an entire book can be written about 'Zero'. The book begins with the history of Zero (it took a long time foQuite a fascinating read! Incredible that an entire book can be written about 'Zero'. The book begins with the history of Zero (it took a long time for societies to accept the concept of Zero and include it in their number line). The history is connected up until present day, with the relevance of Zero in calculus and algebra, and eventually how it lies core to our understandings of physics and particularly astrophysics. I enjoyed the first half of the book more than the second half, which became slow at times and quite complex - but the author makes a good effort in trying to simplify concepts via his words and visuals....more
Written based on author Edosa Odaro's years of experience as an international data leader, you can learn how to make data work properly for your businWritten based on author Edosa Odaro's years of experience as an international data leader, you can learn how to make data work properly for your business. Edosa recounts his personal lessons learned working as an AI and data transformation leader in various sectors.
For example, what did he take out of working directly in the chaos of the Lehman Brothers crash? At a large UK banking institution, could data help them get out of (or at least minimize) the impending disaster, with repercussions felt all around the world? From this particular experience and more, Edosa shares "what to do to 'make data work' for ourselves and for our organizations." Great book with many anecdotes to learn from!...more
4.5/5 stars for me, I highly recommend it for both practitioners and non-practitioners. Accessible book for a wide audience covering AI Ethics. Both s4.5/5 stars for me, I highly recommend it for both practitioners and non-practitioners. Accessible book for a wide audience covering AI Ethics. Both sides of the coin are observed, from the dream/nightmare superintelligence scenarios to concerns closer rooted in today's actual challenges. I found the introduction chapters incredibly interesting, especially chapter 2 which connects our past to what drives us to think the way we do today about AI. Although I enjoyed it, I would say it raises a lot more questions than answers (understandably so given how complex the topic is). But I was hoping for more suggestions on 'how' to aim for more ethical AI....more
No one can argue that today's business landscape is DIGITAL, you've got to stay ahead of the curve or you'll quickly fall behind. It's Digital or... DNo one can argue that today's business landscape is DIGITAL, you've got to stay ahead of the curve or you'll quickly fall behind. It's Digital or... Disappear. Decisively Digital includes 24 interviews with leaders of the digital revolution covering: Digital Culture & Modern Work, Data Democracy & Analytics, Big Data Processing & Cloud Computing, Artificial Intelligence and Process Automation, Blockchain & IoT. Plus some tips & tricks for digital strategy from author and digital leader himself Alexander Loth! The interviews each come with a different perspective on how you can unlock the potential of new digital technologies. Although the interviews can vary depending on the topic covered, some questions are repeated & my favorite question of all that is always addressed: "How should business models evolve to survive and thrive in an increasingly digital world?" Great read for all digital and data enthusiasts, as well as business professionals trying to understand how new-age technology can benefit their bottom line....more
Author Andriy Burkov has done what was previously thought impossible. This book condenses the ins & outs of machine learning, both theory & practice, Author Andriy Burkov has done what was previously thought impossible. This book condenses the ins & outs of machine learning, both theory & practice, into a little over 100 pages. The theoretical / mathematical formulas are shared along with some Python code to show how this can easily be executed on. The book is comprised of 11 chapters that start with the basics (intro, notation and definitions) to fundamental algorithms, with a lot of the book spent on supervised learning in particular.
Definitely not a book for the faint of heart, wonderful for data practitioners BUT I recommend this only for those with at least some light background / experience in the subject. It is also greatly helpful that you're comfortable with math / stats if you want to follow along with everything in the book. Not 100% necessary if you are okay brushing over these pieces to get general ideas....more
Given that analytics & data science have become such hot topics within business, WHY do so many companies still fail at making it work? "Most data sciGiven that analytics & data science have become such hot topics within business, WHY do so many companies still fail at making it work? "Most data science leaders, focusing mainly on the technical aspects of their craft, have struggled to find successes in organizations & to unlock real business value." So this book looks to fill that gap! It is based around 3 key pillars: Strategy, Process and People. The book goes into detail on how to properly build & lead data science and analytics teams within any org. This is not a technical book by any means, but rather very deeply business and managerial focused. I think it would be more helpful for experienced business and analytics leader, in order to understand many of the points made. It can also be read by those just starting out who should come back and reference it as they grow in their career. I really enjoyed the last bonus chapter that covered what's next, the future of business analytics: no-code and low-code, edge AI, bias and ethics, data for good and data for bad, etc...more
We all talk about how to use data to drive business decisions, but have you ever wondered how we could use data to make better life choices? Author SeWe all talk about how to use data to drive business decisions, but have you ever wondered how we could use data to make better life choices? Author Seth Stephens-Davidowitz did just that! Using some of his own research mixed with third-party studies, he uses data to break down some of our biggest life choices and how we can optimize for better results: Marriage, Kids, Athletics, Money, Work, Looks and Happiness.
If you haven't read his first book Everybody Lies, you must check it out. It is one of my all-time faves! In comparison, Don't Trust Your Gut is more US-centric and I found it has less 'wow' factor than his first book. Both worth a read though!...more
A guide for leaders that looks at how to develop the right mindset & strategies to generate value from data & SCALE. "It favors pragmatism over perfecA guide for leaders that looks at how to develop the right mindset & strategies to generate value from data & SCALE. "It favors pragmatism over perfection, collaboration & cooperation over siloed thinking & business, and new ways of working over outdated data strategies & initiatives." Both authors are CDOs (Chief Data Officers) by profession and share from their learned experiences what the keys to success are for a data leader. Scaling a successful data strategy is incredibly complex which is why data has to be embedded into your entire operations. And everyone agrees that CULTURE is central to your success. I did a lot of highlighting, lots of nuggets of knowledge throughout that I'm sure I will reference throughout my career. Great read for those in the field, regardless of whether you are a data leader now or strive to be in the future....more
Written by the Godfather of data literacy himself, I thoroughly enjoyed this book. After reviewing pre-published pieces of the book for Jordan, I've bWritten by the Godfather of data literacy himself, I thoroughly enjoyed this book. After reviewing pre-published pieces of the book for Jordan, I've been SO looking forward to reading through the final product. I think this is a must-read for anyone wanting to implement a data strategy. It is filled with advice from an organizational sense but also what needs to be done at the individual level (the 3 C´s: Curiosity, Creativity, Critical Thinking). It is filled with nuggets of knowledge like the 4 levels of analytics, how to define data literacy and differentiate it from digital fluency, and how to avoid the most common roadblock - an unfit company culture. The key message is that "not everyone in the world needs to be a data scientist, but EVERYONE needs to be data literate," so you can use this book to guide you on your journey, especially for those just starting out....more
The Age of AI is written by an interesting group of leaders from different disciplines: (1) from the GOVERNMENT side - Henry A. Kissinger - American dThe Age of AI is written by an interesting group of leaders from different disciplines: (1) from the GOVERNMENT side - Henry A. Kissinger - American diplomat and former US Secretary of State, (2) from the BUSINESS and TECH side - Eric Schmidt - Business executive and former CEO of Google, (3) from the SCIENCE side - Daniel Huttenlocher - Computer scientist and inaugural dean at Massachusetts Institute of Technology. I had high expectations given the authors behind this, but it did not quite live up to them. The book is not what I expected it to be, but still very much worth the read. The co-authors share their thoughts on what AI means for us in a broad sense - think civilization level. Don't expect a book that explains in much detail what AI is or applications for business. It's more like a thought experiment covering what the 'age' of AI means for humanity and where it may take us. Some parts seem to ramble on and I feel like they could have been covered more succinctly. Regardless, a timely read with the current AI hype we're living through. Recommended if you want to be challenged on what AI really means for humanity....more
A simple, straightforward read on managing data teams. Manager or not, it definitely covers a lot of useful content for readers working in the data spA simple, straightforward read on managing data teams. Manager or not, it definitely covers a lot of useful content for readers working in the data space. As author Taylor puts it, "this book will tell you the 20% of your job that solves 80% of the problems you'll encounter as a data team manager." The book focuses on the operational excellence you need for success and covers topics such as: How to measure success as a data team manager, Why data quality issues happen, How to proactively fix data quality issues, The importance of requirement gathering (and winning over stakeholders) before diving into your analytics project, Hiring and then subsequently managing, motivating & retaining data workers and more....more
If you want to learn more about data storytelling, THIS is the book for you. The book goes way beyond typical data visualization content and covers hoIf you want to learn more about data storytelling, THIS is the book for you. The book goes way beyond typical data visualization content and covers how to communicate your insights more effectively to others and drive change. Storytelling is one of the aspects that makes us stand out as a species & support our ability to hand down generations of history & knowledge. The author does a great job of pairing theory with real-world examples so you can understand exactly how the lessons are put into practice.
"We hear statistics, but we feel stories. A narrative offers an opportunity to engage the minds of your audience at a deeper & more encompassing level than facts ever can." ...more
The BEST book I have read on data / AI ethics thus far. Written so clearly and well thought out with each proposed theory or methodology paired with eThe BEST book I have read on data / AI ethics thus far. Written so clearly and well thought out with each proposed theory or methodology paired with examples and use cases. Highly recommended for all data practitioners, and even for non-practitioners it is mostly accessible. Data ethics falls at the intersection of technology, society, ethics and algorithm design. The book dedicates a chapter each to the algorithmic problems and proposed ways to solve for: privacy, fairness, game theory, p-hacking, and finally a combined touch on more difficult to resolve interpretability, morality and the risks of a singularity. Many incredibly relevant notions are covered including but not limited to: differential privacy (centralized vs local), Pareto curves balancing predictive accuracy and fairness, fairness gerrymandering, Maxwell equations, the echo chamber equilibrium, the Bonferroni correction and the reproducibility crisis. On a side note, I love how the authors relate the famous data science phrase 'garbage in, garbage out' to their own 'bias in, bias out.' I know I'll be referencing this book for years to come, great work!...more
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