Data Science Study

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. 

Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently bingeable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Data Science for Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits.

So many people dream of becoming their own boss or succeeding in their chosen profession, and with the resources available today, more entrepreneurs and professionals are achieving great success! However, success should be defined for the long term. As opportunities start to grow, so does the competition. Getting your business up and running or starting on your career path is one thing, but having a sustainable business or career is another. Many people make the mistake of making plans, but having no follow-through. This is where analytics comes in.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

With the world going remote, and data science following along, you can start and pursue a career in data right here, right now. How do I start? What do I need to know? What is data analytics? I'm sure one, if not all, of these questions have run through your mind. Well, the answer is data literacy: the ability to read, understand, and communicate data into information. Building a solid foundation of practical knowledge is the first step to being successful in data. From there, you can finally decide what direction you should go in and start gaining the required knowledge.

There are two remarkable phenomena that are unfolding almost simultaneously. The first is the emergence of a data-first world, where data has become a central driving force, shaping industries and fueling innovation. The second is the dawn of the AI age, propelled by the advent of Generative AI...

There's no better time to become a data engineer. And acing the AWS Certified Data Engineer Associate (DEA-C01) exam will help you tackle the demands of modern data engineering and secure your place in the technology-driven future. Authors Sakti Mishra, Dylan Qu, and Anusha Challa equip you with the knowledge and sought-after skills necessary to effectively manage data and excel in your career.

This new edition - more than 50 percent new and revised - is a significant update from the previous one and reveals how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics, such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, this book presents ninety-seven concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data.

Colonialism has not disappeared; it has taken a new form. In the new world order where data is the new oil, big Tech companies are grabbing our most basic natural resources - our data - exploiting our labour and connections, and repackaging our information to control our views, track our movements, record our conversations and discriminate against us. In this searing, cutting-edge guide, two leading global researchers and founders of the concept of data colonialism reveal how history can help us understand the emerging future - and how we can fight back.

Unsere Umwelt wurde von Männern für Männer gestaltet. In Das Patriarchat der Dinge öffnet Rebekka Endler uns die Augen für das am Mann ausgerichtete Design, das uns überall umgibt. Und sie zeigt, welche mitunter lebensgefährlichen Folgen es für Frauen hat. Unsere westliche Medizin ist beispielsweise – mit Ausnahme der Gynäkologie – auf den Mann geeicht: von Diagnoseverfahren und medizinischen Geräten bis hin zur Dosierung von Medikamenten. Aber auch die Dummys für Crashtests haben den männlichen Körper zum Vorbild – und damit das ganze Auto samt Airbags und Sicherheitsgurten.

From facial recognition—capable of checking us onto flights or identifying undocumented residents—to automated decision systems that inform everything from who gets loans to who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search.

Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments - also known as randomized controlled trials - designed to test the impact of different online experiences. In this book, Michael Luca and Max Bazerman explain the importance of experiments for decision making in a data-driven world.

Using information in its everyday, nonspecialized sense, Michael Buckland explores the influence of information on what we know, the role of communication and recorded information in our daily lives, and the difficulty (or ease) of finding information. He shows that all this involves human perception, social behavior, changing technologies, and issues of trust.

When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls - information about the caller, the recipient, the time, the duration, the location - and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems?

I segreti più profondi della Cia e dell'Nsa sono stati hackerati e resi pubblici, più di una volta. Ma stai certo che da nessuna parte, nemmeno nel dark web, troverai una copia dell'algoritmo di ricerca di Google o dell'algoritmo del feed di Facebook. Oggi questi algoritmi determinano l'andamento della vita umana sul nostro pianeta. Jaron Lanier, informatico della Silicon Valley, pioniere della realtà virtuale, non ha nessun account social. In "Dieci ragioni per cancellare subito i tuoi account social" spiega perché non dovresti averne nemmeno tu.

"Big Data" is on the covers of Science, Nature, The Economist, and Wired magazines, on the front pages of the Wall Street Journal and The New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure.