Here are 37 books that Jumpstart Snowflake fans have personally recommended if you like
Jumpstart Snowflake.
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I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.
'A statistical national treasure' Jeremy Vine, BBC Radio 2
'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science
Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
Even if you are ultimately going to be working with terabytes of data, you’ll start out doing exploratory data analysis. The tool that you’ll use for that is most likely going to be Pandas. One of the best investments that you can make when becoming a data scientist is to become a Pandas expert, and there is no better book than Harrison’s to help you get there. Plus, many of the interview questions you will face during the hiring process will probably involve Pandas. Blow your interviewers out of the water by showing them corners of the Pandas library they didn’t even know!
Best practices for manipulating data with Pandas. This book will arm you with years of knowledge and experience that are condensed into an easy to follow format. Rather than taking months reading blogs and websites and searching mailing lists and groups, this book will teach you how to write good Pandas code.
It covers:
Series manipulation
Creating columns
Summary statistics
Grouping, pivoting, and cross-tabulation
Time series data
Visualization
Chaining
Debugging code
and more...
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
As a data scientist in the industry, it is very helpful to understand the business context behind the problems that you are solving. In many cases, you are trying to predict behavior—who is likely to buy an item, who is likely to click on a link, who is likely to repay a loan, etc.
This book by Eric Siegel is a great introduction to predictive analytics as used in real-life. It will help you frame data science problems in standard ways. For example, suppose you are asked to score sales leads so that salespeople can prioritize their efforts. How would you do it? The common way to frame this problem is to predict the customer lifetime value (LTV) of every sales lead. Before you can do prediction, you have to be able to do analysis though.
The way you estimate the LTV is to break the problem into three sub-problems:…
"The Freakonomics of big data." -Stein Kretsinger, founding executive of Advertising.com
Award-winning | Used by over 30 universities | Translated into 9 languages
An introduction for everyone. In this rich, fascinating - surprisingly accessible - introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.
Prediction is booming. It reinvents industries and runs the world. Companies, governments, law…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
It is not enough for a data scientist to be able to analyze data and build ML models. You have to be able to communicate the insights to decision-makers concisely and accurately. This book shows you bad and good visualizations — you’ll be surprised by how often you would have defaulted to the bad way without the guidance provided by this book!
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.
This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke…
I’m a cognitive scientist, and I love reading, thinking, and researching about the nature of the human – and especially the young – mind, and what it is capable of. Even while I was still doing my PhD in experimental psychology at Oxford in the early 1970s, I was gripped by the new possibilities for thinking about education that were being opened up by science. In particular, the assumption of a close association between intelligence and intellect was being profoundly challenged, and I could see that there was so much more that education could be, and increasing needed to be, than filling kids’ heads with pockets of dusty knowledge and the ability to knock out small essays and routine calculations. In particular, we now know that learning itself is not a simple reflection of IQ, but is a complex craft that draws on a number of acquired habits that are capable of being systematically cultivated in school – if we have a mind to do it.
You may know of Sugata’s work, even if the name does not ring a bell. He is the Indian professor who decided to cement an online computer into a wall in a slum in Delhi, set up a hidden camera, and waited to see how the local children would react. This was before everyone had a laptop or a mobile phone. The kids quickly gathered round and quickly figured out how to do all kinds of interesting things, without any teaching. Indeed, he found that when teachers tried to ‘help’, the children stopped being resourceful, stopped collaborating as independent learners, and expected to be taught. The School in the Cloud documents the growth of Sugata’s work and global influence since that first experiment, and reminds us forcibly of just how much all children can learn under their own steam – if we will just get out of the way.
Discover the results of Sugata Mitra's latest research around self-organized learning environments (SOLE) and building "Schools in the Cloud" all over the world.
I am an Anglo Irish writer who is as filled with a wide-eyed wonder of the magic of Christmas in my middle age as I was as a small child. Alongside my lifelong love of Christmas and its traditions, I have enjoyed an equally long love of ghost stories. Combining these two passions, I am the editor of theGhost Stories For Christmasanthologies of classic Christmas ghost stories, the first of which was published in 2022. I am also the writer of Ghostly Tales of Japan, a collection of original stories set throughout Japanese history.
This anthology holds a special place in my heart. I received a copy of it as a Christmas present from my dear grandmother in 1979. Just holding it in my hands brings back so many happy memories of that long-departed lady. The book contains just eleven stories, but it is “a collection of deliciously scary fare.” Among the choice delicacies contained within its covers is the shortened version ofA Christmas Carolmade by Dickens for his public readings of the story, which I read in his imagined voice. Alongside anthology favourites by Hugh Walpole, Algenon Blackwood, and Jerome K. Jerome are less familiar, but equally rewarding ghostly tales by Marjorie Bowen, Oliver Onions, Margery Allingham, and others. I don’t know if it is still in print, but anyone who takes the trouble to find a copy will be richly rewarded.
My passion for developing production-ready, cooperating microservices began in 2008 when I first started assisting customers in creating distributed systems—long before the term “microservices” was coined. During that time, I faced significant challenges, including grappling with the “Eight Fallacies of Distributed Computing”. Since then, I’ve dedicated most of my career to deepening my understanding of these complexities and finding ways to address them through robust architecture, design patterns, and the right tools.
Understanding how requests and messages traverse a large microservice landscape is notoriously challenging. The CNCF OpenTelemetry framework standardizes how to collect and observe telemetry data (metrics, logs, and traces).
This book was invaluable in helping me grasp OpenTelemetry’s core concepts and architecture, including collectors and exporters, and how to instrument applications effectively. It also helped me understand how distributed tracing data is structured into traces and spans and how to propagate context information between cooperating microservices.
OpenTelemetry is a revolution in observability data. Instead of running multiple uncoordinated pipelines, OpenTelemetry provides users with a single integrated stream of data, providing multiple sources of high-quality telemetry data: tracing, metrics, logs, RUM, eBPF, and more. This practical guide shows you how to set up, operate, and troubleshoot the OpenTelemetry observability system.
Authors Austin Parker, head of developer relations at Lightstep and OpenTelemetry Community Maintainer, and Ted Young, cofounder of the OpenTelemetry project, cover every OpenTelemetry component, as well as observability best practices for many popular cloud, platform, and data services such as Kubernetes and AWS Lambda. You'll learn…
I’m the Head of Trend and Innovation Scouting for Nokia, and I’ve been with the company since the glory days of Nokia mobile phone world dominance. I know first-hand what happens when a company focuses exclusively on the technology, not the humans that use it, and how quickly that can lead to disaster. One of the lessons that I see repeated continuously in the field of innovation is that a huge amount of attention gets paid to the new technology, and not nearly enough on how the technology will interact with our existing systems, beliefs, attitudes, and culture. Learning from the mistakes is the best way to make sure that the future doesn’t repeat them!
While the term the “Metaverse” usually makes people think of a fully digital, immersive world, my own feeling is that technologies that bring digital information and entertainment into our physical world is a much more powerful and important arena. This leads us to the transformative and still-developing world of Augmented Reality.
David Rose of the MIT Media Lab has been working with Augmented Reality for more than a decade, and Supersight is an overview of what he's seen and what he’s learned in this time.
What I love about Supersight is that while David is clearly as excited about this topic as I am, he’s also a realist, and openly discusses issues and challenges with Augmented Reality. Perhaps most valuable are the 14 Augmented Reality Design Principles that he outlines – super realistic, super useful.
After reading this, you’ll have a very grounded idea of the capabilities and potential of…
For thousands of years, human vision has been largely unchanged by evolution.
We’re about to get a software update.
Today, Apple, Google, Microsoft, Facebook, Snap, Samsung, and a host of startups are racing to radically change the way we see. The building blocks are already falling into place: cloud computing and 5G networks, AI computer vision algorithms, smart glasses and VR headsets, and mixed reality games like Pokémon GO. But what’s coming next is a fundamental shift in how we experience the world and interact…
With over a decade of experience in web development using Clojure and active involvement in the Clojure open source community, I have gathered invaluable insights into effective use of the language. I am eager to share some of the experience and knowledge I have acquired with those new to the language.
This book contains many practical examples of solving common programming tasks using Clojure, and it's an excellent choice for a practical Clojure reference.
Developers who are new to the functional programming style will find a lot of useful patterns for solving problems using idiomatic Clojure style. The book is an essential reference for Clojure developers.
With more than 150 detailed recipes, this cookbook shows experienced Clojure developers how to solve a variety of programming tasks with this JVM language. The solutions cover everything from building dynamic websites and working with databases to network communication, cloud computing, and advanced testing strategies. And more than 60 of the world's best Clojurians contributed recipes. Each recipe includes code that you can use right away, along with a discussion on how and why the solution works, so you can adapt these patterns, approaches, and techniques to situations not specifically covered in this cookbook. Master built-in primitive and composite data…
I’ve spent more than a decade working on infrastructure, from my early days at LinkedIn, where we had to do a massive DevOps transformation to save the company, to co-founding Gruntwork, where I had the opportunity to work with hundreds of companies on their software delivery practices. From all of this, I can say the following with certainty: the DevOps best practices that a handful of the top tech companies have figured out are not filtering down to the rest of the industry. This is making the entire software industry slower, less effective, and less secure—and I see it as my mission to fix that.
This is a book for practitioners, by a practitioner, full of practical learnings that I was able to start using in my work immediately.
I especially appreciated the parts teaching the core principles of infrastructure as code (e.g., systems are disposable, consistent, can easily be reproduced, etc.), core practices of infrastructure as code (e.g., use definition files, self-documented systems and processes, version all the things, etc.), and the idea of antifragile systems (rather than just systems that you prevent from breaking) and autonomic systems (rather than just automated systems).
Six years ago, Infrastructure as Code was a new concept. Today, as even banks and other conservative organizations plan moves to the cloud, development teams for companies worldwide are attempting to build large infrastructure codebases. With this practical book, Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered by DevOps teams to manage cloud-age infrastructure.
Ideal for system administrators, infrastructure engineers, software developers, team leads, and architects, this updated edition demonstrates how you can exploit cloud and automation technology to make changes easily, safely, quickly, and responsibly. You'll learn how to define everything as…
