Computing The Climate

This week's book is "Computing the Climateql" by Steve M. Easterbrook, a captivating journey into the synergy of climate science and computing, making it a must-read for anyone intrigued by the intersection of these fields. Easterbrook's engaging writing style effortlessly demystifies complex concepts, ensuring accessibility for readers with diverse backgrounds. The book's strength lies in its seamless blend of theoretical discussions with real-world examples, showcasing the instrumental role of computing in unraveling the intricacies of climate dynamics.

Easterbrook's balanced perspective sets this book apart, acknowledging the uncertainties in climate science while underscoring the transformative impact of technological advancements. By delving into interdisciplinary connections with policy, economics, and environmental science, Easterbrook provides a holistic understanding of the challenges associated with climate change. This comprehensive approach educates and empowers readers to recognize the pivotal role of computational progress in shaping our collective response to climate-related issues.

In essence, "Computing the Climate" stands as a persuasive testament to the indispensable role of computing in climate research. Easterbrook's skillful narrative not only informs but also inspires readers to grasp the significance of technological innovation in confronting the pressing challenges of our changing climate. This book is an essential addition to the literature, urging readers to actively engage in the ongoing dialogue surrounding the future of our planet.

 

Causal Factor Investing

This weeks book is "Causal Factor Investing" by Marcos M. López de Prado, his most recent work in quantitative finance. López de Prado introduces a unique approach by emphasizing causality, a factor often overlooked in traditional quantitative models. The book provides a refreshing perspective on financial markets, challenging conventional wisdom and offering a comprehensive guide to understanding and implementing causal factor investing.

What sets this book apart is its practicality. López de Prado not only delves into the theoretical underpinnings of causal factor investing but also provides real-world examples and case studies. This hands-on approach ensures that readers can not only grasp the concepts but also apply them in their own investment strategies. The author's writing style is both engaging and approachable, striking a balance between academic rigor and practical applicability.

In a landscape where financial strategies are constantly evolving, "Causal Factor Investing" stands out as a timely and relevant resource. López de Prado's expertise shines through, making this book essential for both seasoned professionals and those new to quantitative finance. It's a must-read for anyone serious about staying ahead in the dynamic world of investment, offering a thought-provoking and informative guide that will shape the future of quantitative finance.

 

SQL for Data Analysis

This week’s Data Science Book, "SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights" by Cathy Tanimura. This book is a must-have resource for anyone serious about data analysis and SQL. It equips you with the tools and knowledge to tackle complex data analysis tasks with confidence. With its clear explanations, real-world examples, and comprehensive coverage of advanced topics, this book will undoubtedly become an invaluable asset in your data analysis journey. Whether you're a data analyst, data scientist, or a SQL enthusiast, this book will help you take your skills to the next level and transform data into actionable insights.

 

What is ChatGPT Doing… and Why Does It Work?

This week’s Data Science Book, "What Is ChatGPT Doing ... and Why Does It Work?" by Stephen Wolfram. Wolfram, a prominent figure in mathematics and computation, offers a compelling exploration of ChatGPT's success and the broader implications of artificial intelligence while delving into the inner workings of ChatGPT and its historical context.

The book stands out for its accessibility, making complex concepts understandable to non-computer scientists and non-mathematicians alike. Wolfram provides a high-level overview of ChatGPT's components, such as embeddings and transformers, without getting bogged down in technical details.

While acknowledging ChatGPT's successes, Wolfram candidly addresses its limitations, especially in real-world computational tasks. He critically examines ChatGPT's reliance on neural network weightings and raises intriguing questions about intelligence, drawing parallels with biological evolution.

In conclusion, "What Is ChatGPT doing ... and Why Does It Work?" is a thought-provoking read that demystifies AI for a wide audience. Wolfram's expertise and candid exploration make it a must-read for those interested in AI's frontiers and the complexities of intelligence.

 

Learning Git

This week’s Data Science Book, "Learning Git", by Anna Skoulikari. This is a remarkable book that caters to both technical and non-technical individuals seeking to master Git. The book's rainbow project approach offers an effective and enjoyable way to understand the inner workings of Git, covering all the basics needed for practical use in an industry setting. From setting up local repositories to managing remote ones, the book excels in simplifying complex concepts with colorful diagrams and highlighted keywords. Highly recommended for anyone looking to grasp Git's fundamentals and achieve confident control over version control in their work.

 

Network Science with Python

This week’s Data Science Book, "Network Science with Python", by D. Knickerbocker is a highly recommended book for anyone interested in network analysis. It provides a comprehensive and accessible introduction to the topic. The book's linear progression and friendly tone make it highly engaging and easy to follow. The author's contagious enthusiasm and practical examples effectively communicate the power and importance of network analysis. The book covers various domains, including language and social media data mining, and explores the relationship between NLP and networks, an approach similar to our very own Graphs for Data Science substack. It emphasizes the value of actionable insights in the conversational AI domain and provides historical context and real-world use cases for NLP solutions. The book also introduces the Python packages used and dives into network science using the NetworkX library. It demonstrates how graphs can be used in machine learning and covers important concepts like betweenness centrality, page rank, and community detection with real-world applications. Overall, "Network Science with Python" is a well-written and comprehensive guide that offers practical insights and is suitable for readers of all levels.

 

Fluent Python

This week’s Data Science Book is "Fluent Python" by L. Ramalho, which is, in my opinion, the best book on Python programming available as it teaches readers to truly understand how Python works and how to utilize it effectively. This is a book for those who want a comprehensive and in-depth understanding of the language, covering advanced topics such as metaprogramming with "dunder()" methods without getting lost in the weeds. It covers exactly what you need to know, without overwhelming you with unnecessary information. "Fluent Python" is a must-have for any serious Python programmer as even after almost twenty years of working with Python, I continue to learn new things with each chapter. The second edition weighs in at over 1,000 pages for an in depth, comprehensive cover, of everything you may ever need to know about Python.

 
 

Automate the Boring Stuff with Python

This week’s Data Science Book is "Automate the Boring Stuff with Python " by A. Sweigart, an exceptional book that is perfect for anyone who wants to learn how to use Python for automating mundane tasks. The author has done an excellent job of explaining complex concepts in a simple and easy-to-understand manner.

The book is well-organized and starts with the basics of Python programming, and gradually progresses to more advanced topics such as web scraping, working with Excel spreadsheets, and sending automated emails. The real-life examples and practical exercises included in each chapter make it easy for readers to apply what they have learned and see the results.

One of the most remarkable aspects of this book is that it doesn't require any prior programming experience, making it perfect for beginners. However, it is also an excellent resource for experienced programmers who want to learn how to automate repetitive tasks.

Overall, this is a must-read for anyone who wants to learn how to use Python for automating everyday tasks. The book is well-written, easy to follow, and the author's sense of humor makes it an enjoyable read. I highly recommend this book to anyone who wants to learn Python programming and automate their boring tasks.

 

The Recursive Book of Recursion 

This week’s Data Science Book is "The Recursive Book of Recursion" by Al Sweigart, a highly recommended book for programmers of all levels. The book explains recursion in a clear and approachable way. It begins by laying important groundwork and explains functions and their operation and features. The author spends significant time explaining the call stack, what it does, how it is structured, and how it operates, leading to a discussion of ‘stack overflow,’ one of the risks of using recursion. He then devotes an entire chapter to comparing recursion and iteration, demonstrating that in the vast majority of cases, recursive functions are not necessary and in some cases perform worse than their iterative counterparts.

However, the book also shows where recursion is actually a good idea and where it is a good fit. Sweigart explores traversing tree structures and demonstrates how memoization can improve the efficiency of some recursive functions. The book ends with several projects that build on the concepts that come before, including the Droste Effect, a recursive art technique that generates a similar recursive image from any photograph or drawing utilizing images.

Overall, the Recursive Book of Recursion is a great read for beginners and intermediate programmers alike. The book teaches about recursion and stretches the reader to think differently while confidently showing that seemingly lofty concepts are within reach.

 

Introduction to Algorithms (4th Edition)

​This week’s Data Science Book is “Introduction to Algorithms (4th edition)” by T. M. Cormen, C. E. Leiderson, R. L. Rivest and C. Stein. This book is affectionately known as the bible of algorithms and over the years has proven to be an essential reading and, at over 1300 pages, a complete reference for anyone interested in gaining a broad understanding of algorithms. The content can at times be challenging but is presented in a fashion that is engaging and easily digestible. Exercises at the end of each chapter are expressly presented without the benefit of solutions but were carefully designed to help students to think algorithmically and thoroughly absorb the material presented.

 

Advanced Algorithms and Data Structures

This week’s Data Science Book is "Advanced Algorithms and Data Structures" by M. La Rocca. This book is a great resource for engineers who want to enhance their knowledge of algorithms and data structures without having to go back to traditional, academic-style textbooks. The author has a deep understanding of how to deliver high-quality code and each algorithm is thoroughly illustrated with pseudo-code and diagrams. The book is especially helpful for tackling contemporary problems such as multidimensional search, understanding caches better, classification, and graph theory.

The writing is friendly and approachable, making it relatively easy to understand, although some familiarity with math may be helpful. It is a book that can be read, skipped around, and returned to from time to time when needed. Overall, this is a strongly recommended book for engineers looking to enhance their algorithm and data structure knowledge.

 

AI and Machine Learning for Coders

This week’s Data Science Book is " AI and Machine Learning for Coders " by L. Moroney and with a foreword by none other than Andrew Ng. This is a book that exceeds expectations with excellent explanations on how to code machine learning using TensorFlow and different ML techniques. The book covers various topics, including computer vision, natural language processing, and time series forecasting, and even includes a section on text generation.

The book is aimed specifically at coders with Python experience and explains how neural networks work at a high level without overwhelming readers with too much math. The author does an excellent job of explaining convolution, maxpooling, interpretability, bias/fairness, and Google's AI principles.

Overall, anyone who wants to learn about deep learning using TensorFlow, will find here an excellent resource that provides a solid foundation in deep learning and is suitable for hands-on practitioners without overwhelming them with math.

 

Practical Linear Algebra for Data Science

This week’s Data Science Book is "Practical Linear Algebra for Data Science" by M. X. Cohen, a book written for self-studying learners who need to learn how to apply linear algebra in their work. The book is self-contained and can be used as a standalone resource, but it can also be used as a supplement to a lecture-based course. Whether you are trying to enhance your understanding of linear algebra or learn the subject from scratch, this is a valuable resource that provides a clear and practical approach to the subject.

The author is an excellent instructor that recognizes that traditional linear algebra textbooks can be frustrating for those looking to use the subject as a tool for understanding data, statistics, deep learning, image processing, and other technical fields. Instead of memorizing equations and abstract proofs, the author provides clear explanations and practical examples to help the reader understand how to think about matrices, vectors, and operations. The focus of the book is to help the reader develop a visual, geometric, intuition for linear algebra and how to implement these concepts in Python code, particularly for applications in machine learning and data science.

 

Data Science in Context

This week’s Data Science Book is "Data Science in Context" by A. Z. Spector, P. Norvig, C. Wiggins, and J. M. Wing. The book, whose authors require no introduction, provides a comprehensive overview of Data Science, covering the technical aspects of the field and the ethical considerations and challenges it presents. It is structured in a way that makes it easy for readers to understand and absorb the information and offers recommendations for addressing ethical concerns. The authors focus on real-life examples from their various fields, such as healthcare and finance, to illustrate data science applications and their potential impact. Aimed at a wide audience, it includes data science novices and experienced professionals, and is recommended for anyone with an interest in data science and its role in daily life and various industries.

 

Computer Age Statistical Inference

This week’s Data Science Book is " Computer Age Statistical Inference " by B. Effron and T. Hastie. This book provides a comprehensive overview of modern statistical methodology, covering a wide range of topics including Bayesian and frequentist approaches, survival analysis, logistic regression, empirical Bayes, random forests, neural networks, Markov chain Monte Carlo, and model selection. The authors are two well-known experts from Stanford University that are able to discuss the history of statistical analysis and its evolution with the introduction of electronic computation in the 1950s and offer a modern approach that integrates methodology and algorithms with statistical inference. This book is a valuable resource for understanding the flow of statistical thinking and for choosing the best approach to solve data analysis problems. It is well-written and well-produced, with good examples and a small amount of *gasp* R code.

 

How Data Happened

This weeks Data Science Book is " How Data Happened " by C. Wiggins and M. L. Jones. This book is an offshoot of the popular Columbia University course that Wiggins, a mathematician and Jones, an historian, teach together. They cover the history of Data, how algorithms developed and have been used over the years. They provide a special view on the development of the field of Data Science as we know it today and interesting perspectives on how it might develop in coming years. A unique source of insight and perspective that will help you become a leader in your Data Science career.

 

Code: The Hidden Language of Computer Hardware and Software

This weeks Data Science Book is " Code: The hidden language of Computer Hardware and Software " by C. Petzold.This book is a bit of detour from our usual fare here at Data For Science as it focuses more on Computer Science than on Data Science per se. It provides a step-by-step timeline of how computers came to be, in a clear and concise way. It takes you on a tour of what happens "behind" the pixels on your screen, from logical gates on up, without requiring a heavy technical background. Chapter, by chapter, introduces each concept and technology necessary to make modern computers work. By the end of it, you'll have a detailed an intuitive understanding how Computers really work and will be able to mode easily optimize the way in which you write your own software. It might also make you want to learn to program in Assembly! A book that should be required reading for anyone interested in Computer Science.

 

Essential Math for Data Science

This weeks Data Science Book is "Essential Math for Data Science" by T. Nield. Here at Data For Science, we pride ourselves in taking a deep approach to the algorithms and models we employ. For this, good mathematical foundations are paramount. In his recent book T. Nield introduces the reader to enough of the mathematics underlying Data Science and Machine Learning to give you a head start in grokking both classic and state of the art algorithms that your likely to encounter in your career as a Data Scientist. A highly recommended read to anyone who wants to look inside the black boxes we use everyday.

 

Fundamentals of Data Engineering

This weeks Data Science Book is "Fundamentals of Data Engineering" by J. Reis and M. Housley. While our focus is on Data Science, we can't downplay the major role played by Data Engineering in practical Data projects. In this recent tome, the authors guide us through the thought processes, concepts and principles necessary to establish a successful Data Engineering operation to help support all of your data driven projects. They recommend a cloud first approach that allows for quick experimentation, adaptation and scaling.

 

Analytical Skills for AI & Data Science

This weeks Data Science Book is "Analytical Skills for AI & Data Science" by D. Vaughan. This is an unusual book that takes a holistic approach to AI and Data Science from a Business perspective. Aimed at managers with limited Data Science experience, this book uses increasingly complex practical examples to introduce a wide range of concepts and analytical techniques. Even if you have no direct interest in AI and Data Science, this book will give you enough background knowledge to be able to successfully manage Data Scientists, break down complex problems into individual components and help guide your team towards the right solution to your business problems.

 

The Practitioner's Guide to Graph Data

This weeks Data Science Book is "The Practitioner's Guide to Graph Data" by D. K. Gosnell and M. Broecheler. Graph Thinking and Graph Data are topics near and dear to our hearts here at D4Sci (checkout G4Sci if you haven't yet) and this book does an excellent job of introducing both fundamental and advanced topics and techniques using practical real world datasets and state of the art graph databases. The book is exceptionally well written and easy to follow, with practical "rules of thumb" generously sprinkled throughout along with practical examples that you can use to grok as the various concepts are they are introduced. A must have for anyone interested in Graph Thinking and Graph Databases.

 

Bayes’ Rule: A tutorial Introduction to Bayesian Analysis

This weeks Data Science Book is "Bayes' Rule: A Tutorial Introduction to Bayesian Analysis" by J. V. Stone. Despite its simplicity Bayes Rule is one of the most power theorems in probability theory. This relatively short book (170 pages) provides a nice overview and hands on introduction to Bayesian analysis. While the main text provides a strong foundation to the main ideas and concepts, the companion Python code gives you a leg up in introducing these techniques into your daily work. Overall, an excellent first introduction to the topic and a good refresher reference if you haven't touched this area in some time.

 

Causality

This weeks Data Science Book is "Causality" by J. Pearl. Causal Inference is a lively and fast developing area in Data Science that we believe has the potential to be truly revolutionary in coming years (you can get a quick overview of the main ideas in our Causal Inference series over at Medium). Judea Pearl is one of the most prominent founding fathers of this field that he introduces masterfully in this textbook. While the approach Pearl chooses is mathematically rigorous, thanks to his rich use of toy examples, the key ideas and concepts are easily grasped and adapted to real world datasets. Causal Inference is a powerful arrow in any Data Scientist's quiver and this is the ideal starting point if you're interested in taking the first steps in this exciting area.

 

Interactive Dahsboard and Data Apps with Plotly and Dash

This weeks Data Science Book is "Interactive Dahsboard and Data Apps with Plotly and Dash" by E. Dabbas. In our lives as data scientists and machine learning engineers, we are often called upon to develop Dashboards and other Data Drive apps to communicate results or to monitor the performance of models deployed to production. Plotly and Dash are the current State of the Art libraries for interactive visualizations with a web frontend. This book does a remarkable job of getting you up to speed with both of these libraries taking you from basic to advanced level through practical building blocks that you can immediately customize for your own use.

 

Hands-On Data Visualization: Interactive Storytelling From Spreadsheets to Code

This weeks Data Science Book is "Hands-On Data Visualization: Interactive Storytelling From Spreadsheets to Code" by J. Dougherty, I. Ilyankou. Data visualization, and in particular, storytelling with Data is an often overlooked skill that can help you stand out among your peers. Being able to explain your results, highlight the key points and messages can help you maximize your professional impact. This book is one of the best introductions to Data Visualization and storytelling using a wide range of tools and techniques that are presented through hands-on, step by step, tutorials. The book focuses on generating interactive visualizations that can be embedded within your customer facing website or internal dashboard using a wide range of practical tools.

 

Google BigQuery: The Definitive Guide

This weeks Data Science Book is "Google BigQuery: The Definitive Guide" by V. Lakshmanan and J. Tigani. Google BigQuery is google's SQL based data warehousing and analysis solution that you can easily use in your own projects. Written by one of the early designers of BQ, this books provides a clear and in depth view of how to make the most out of this powerful system that is able to process terabytes of data in just a few seconds. The authors introduce each concept in a intuitive and didactic way, using simple examples that leverage public datasets you can easily run on your own. Overall an excellent first introduction and regular reference to one of the best data analysis systems available today.

 

Graph Representation Learning

This weeks Data Science Book is "Graph Representation Learning" by W. Hamilton. This short (141 pages) book gives a grounded, well-written, and to the point introduction to representation learning for graphs that helps you grasp the fundamental concepts as well understand when to and when not to. The extensive bibliography provides entry points for further study for the motivated reader. The algorithm descriptions are clear and intuitive in a way that will give you a leg up to implement pre-existing algorithm and even develop your own variations.

 

The Data Science Handbook

This weeks Data Science Book is "The Data Science Handbook" by F. Cady, data scientist at the Allen Institute for Artificial Intelligence. The proliferation of new toolkits, algorithms and approaches in Data Science make it easy to lose track of the latest developments. In this handbook, Cady presents us a comprehensive overview of the entire field. In this well written and easy to read reference the author uses practical examples and practical applications to bring the underlying theory to the real world. We feel that this book is at its most useful for when you need to quickly review a topic or to get a to the point overview of a new technique to decide whether its worth trying to apply it to your own work.

 

Fundamentals of Data Visualization

This weeks Data Science Book is "Fundamentals of Data visualization" by C. O. Wilke. Here at D4Sci we're firm believers that an image is worth 1000 words (check out V4Sci if you haven't already) and this book will quickly and easily super charge your visualization skills. Wilke brings to bear the perspective of a Data Scientist to a field often dominated by Designers. Throughout this book, Wilke teaches you how to craft powerful visualizations with your tool or programming language of choice by introducing only as much visualization theory and concepts as necessary and without getting lost in abstract ideas or concepts. The authors experience as a Data Science makes this one of the best applied books on Visualization, despite the fact that the book is entirely language agnostic.

 

Feynman Lectures on Computation

This weeks Data Science Book is the most excellent "Feynman's Lectures on Computation" by R. P. Feynman. You might be familiar with Feynman's Lectures on Physics, but his lectures on Computation (based on a class he taught and his work in 'Connection Machine') aren't any less amazing. Through this short book, Feynman guides us through the concept of computation and the van Neumann architecture in his unique style, from logic functions, to Turing machines, coding and even quantum computers. While not directly related to Data Science, it will give you a unique appreciation of the finer points in which computers are "Dumb as hell but go like mad" so that you can better squeeze every bit of performance out of your code.

 

Scientific Computing with Python

This weeks Data Science Book is the recently released second edition of "Scientific Computing with Python" by C. Führer, O. Verdier and J. E. Solem which fills an important gap in most Data Scientists bookshelves. High performance computing, specially scientific and numeric computing, is a fairly technical branch of computer science that most programmers aren't really familiar with. It requires a deep understanding of the finer points of the data structures being used and of their specific implementations. With this book the authors have managed to demystify a highly complex subject with clear explanations and analysis that will appeal to developers of all levels. Naturally, some of the subjects could gain from being further developed, but in every project of this kind a balance must be struck between breath and depth. Overall, a useful book that I'll refer to often.

 
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Think Bayes

This weeks Data Science Book is "Think Bayes (2nd Ed)" by Allen B. Downey. While Bayesian Statistics is a powerful tool in the toolbox of any Data Scientists, it is not the easiest of skills to learn if you are not mathematically inclined. In this book, Downey uses his down to earth, step by step style to make you proficient in the world of Bayesian Statistics by leveraging your pre-existing knowledge of Python instead of relying excessively on mathematical notation as most other books do. The book comes with a complete up-to-date GitHub repository so that you can more easily work your way through the example and cement your understanding of this important topic.

 
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The Hundred-Page Machine Learning Book

This weeks Data Science Book is "The Hundred-Page Machine Learning Book" by Andriy Burkov. In this short book (despite the title, it in at 160 pages, but we won’t hold it against it) Burkov provides a high level overview of a broad range of Machine Learning topics that will appeal to beginners and perspective students who wish to get a quick survey of the breadth of possibilities afforded by ML algorithms before diving more deeply into the subject. While the book won’t convert you into a ML expert overnight, it does provide enough information and references to get you started and armed with enough baggage to be able to choose your own path. Another audience that will find the book useful is that of managers and those collaborating with data scientists and who need a solid understanding on the basics of this class of algorithms.

 

Data Science on AWS

This weeks Data Science Book is "Data Science on AWS" by Chris Fregly and Antje Barth. We've all had the experience of running out of memory or compute power while performing our data analysis or training our models. In this extremely well written book, the authors introduce us to a huge slate of AWS services that we can use to train, develop and, eventually, deploy our Machine Learning models. Overall, the book strikes the right balance between technical depth and practical breadth and will put the power of AWS at our allowing you to put your data science capabilities on par with some of the leaders of the field.

 

Machine Learning for Time Series Forecasting with Python

This weeks Data Science Book is "Machine Learning for Time Series Forecasting with Python" by Francesca Lazzeri. This book take a modern approach to introducing time series forecasting using practical intuitive explanations of the fundamental concepts together with Python code that covers data preparation, deep learning and end-to-end model deployment in the cloud in a hands on manner and without getting bogged down with too many mathematical details.

 

Data Analysis: A Bayesian Tutorial

This weeks Data Science Book is "Data Analysis: A Bayesian Tutorial" by D. S. Sivia and J. Skilling. Bayesian analysis is a statistical approach with a long and rich history that allows us to use probability statements to quantify our uncertainty about specific parameters. This short book provides an excellent first introduction to this powerful family of techniques with practical examples. The book quickly guides us from the fundamental intuition behind Bayes theorem more advanced concepts and applications such as Model comparison, Inference and Non-Parametric Estimation.

 

Transformers for Natural Language Processing

This weeks Data Science Book is "Transformers for Natural Language Processing" by D. Rothman. Transformers are the latest generation deep learning architecture for NLP that gained prominence in recent years by surpassing the performance of RNNs, GRUs and LSTMs. This book provides a grounded introduction to Transformers from the perspective of cognitive science so that you can quickly master the fundamentals and grow to apply them to your own work.

 

Learning SQL

This weeks Data Science Book is "Learning SQL" by A. Beaulieu. Despite recent developments and data storage advancements, much of the worlds data is still being produced, stored, and manipulated in SQL databases. SQL, the Structured Query Language, provides us with a simple and performant way to run complex queries, generate customized reports, and retrieve subsets of data. Beaulieu quickly and easily guides us from beginners to fluent users of this powerful language with chapters dedicated to introducing each of the fundamental concepts through annotated examples and exercises.

 

Flask Web Development

This weeks Data Science Book is "Flask Web Development" by M. Grinberg. One of the often overlooked skills in Data Science is the ability to quickly generate APIs, dashboards and proof of concepts for machine learning model deployment. Flask is a Python-based micro-framework with a straightforward and concise approach that makes it extremely easy to go from idea to working product. This book is designed to take you from being someone who never wrote web code to developing a fully fledged web application. It also some with an up to date repo of code examples to make sure you learn the current best practices.

 

Data Science From Scratch

This weeks Data Science Book is "Data Science From Scratch" by J. Grus. In this book, Grus, a mathematician by training, guides you through the math, statistics, and ideas underlying the algorithms of modern Data Science while showing you how to implement them from scratch. As you work your way through the book you'll quickly become an expert in the finer points (and possible pitfalls) of each algorithm while at the same time significantly improving your Python proficiency.

 

A First Course in Network Science

This weeks Data Science Book is "A First Course in Network Science" by F. Menczer, S. Fortunato and C. A. Davis. The field of Network Science has developed quickly since its birth in the late 90s with the introduction of many different concepts and techniques originating from different fields. This recent textbook, written by some of the pioneers in this field, guides you through the theoretical and practical framework needed to understand the basic principles of network science and how you might apply it in your own work. The book is complemented by a GitHub repository packed full of Python examples to help you better understand the concepts as they are introduced.

 

Think Stats

This weeks Data Science Book is "Think Stats" by Allen B. Downey. Despite the natural trend to be star struck with latest models and algorithms, the truth is that most of the work of a Data Scientists is must more basic and fundamental. Having a solid basis in traditional statistics is paramount to a successful data science career. Think Stats takes an empirical approach that helps you completely and truly grok the concepts as they are being introduced. Using Python examples and detailed explanations Downey guides the reader through important conceptual tools like Probability Distributions, Hypothesis Testing, Regression and Survival Analysis that should be part of every practitioners toolkit,

 

Deep Learning with Python

Our very first Data Science Book is "Deep Learning with Python" by François Chollet. Deep Learning is conceptual framework responsible for most recent successes in Machine Learning and Artificial Intelligence. Unfortunately, it is not the easiest field to come to grips with. In this book, and in great part thanks to the modular structure of Keras, Chollet, a Google Engineer and brains behind Keras, manages to maintain the perfect balance between breath and depth. The book provides plenty of practical real world examples to explore the concepts as they are introduced and to provide the conceptual basis allowing you to quickly and successfully venture further in your own work. I hope you enjoy reading it as much as I did.

 

Data Science for Business

Our very first Data Science Book is "Data Science for Business" by Foster Provost and Tom Fawcett. This book has the distinction of having been the first book I ever read when I first started getting interested in Data Science back in the day. While it doesn't dive into the Data Science programming stack, Provost and Fawcett have a gift for putting the fundamental concepts and ideas of Data Science into a practical business context that makes it clear when each algorithm should be applied and where they might be found lacking.