Showing titles in Data Science

Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

Artificial intelligence is transforming lives and revolutionizing industries. This accessible guide to generative AI and machine learning is for tech enthusiasts and forward-thinkers alike—whether you're in IT or a professional from another field looking to stay ahead and curious about AI’s impact on the future, this book provides the essential insights you need to stay competitive and informed in the Age of AI.

Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can.

One key responsibility of product designers and UX practitioners is to conduct formal and informal research to clarify design decisions and business needs. But there’s often mystery around product research, with the feeling that you need to be a research Zen master to gather anything useful. But anyone can conduct product research. With this quick reference guide, you’ll learn a common language and set of tools to help you carry out research in an informed and productive manner.

From technology, media, and retail to sport teams, government agencies, and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety, and so much more. Organized for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved, and the processes put in place to make it practical, as well as the technical details, challenges, and lessons learned from each unique scenario.

We are living in the age of information and there’s no denying it—those who are best at organizing, understanding, and then using data to their advantage will be the ones on top. The skills themselves are simple and easy to learn and will give you a tremendous advantage in the highly competitive marketplace of today’s workforce. The only thing left is for you to give a little commitment and launch yourself into a whole new reality of success.

If you’re considering a shift to data analysis, you’re likely exploring various programming languages to kickstart your journey. Take a cue from industry giants like Netflix, Facebook, and Amazon, who rely on SQL for data analysis. Don’t let misconceptions hinder your data analysis journey. Equip yourself with this comprehensive guide and master SQL to unlock new opportunities.

In Generative AI: Navigating the Course to the Artificial General Intelligence Future, Martin Musiol delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space.

Data science is an exciting field and will remain as such for many years to come. With more people than ever studying data science, machine learning & AI, there is an increasing gap between what you learn in the classroom and how data science is done in the real world. Inspired by conversations with nearly 10,000 social media followers, this book aims to close that gap and address some of the common tasks that you will face in your first year as a data scientist.

Untangle the mesh of complex data with this user-friendly manual to SQL. Imagine a world overflowing with data – numbers, stats, facts, all buzzing around, waiting to tell a story. There's one common element that can help you navigate this vast sea of data – Structured Query Language (SQL).

Radically improve the quality of your data visualizations by employing core principles of color, typography, chart types, data storytelling, and more. Everyday Data Visualization is a field guide for design techniques that will improve the charts, reports, and data dashboards you build every day. Everything you learn is tool-agnostic, with universal principles you can apply to any data stack.

Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them.

Structured query language, or SQL (pronounced "sequel" by many), is the most widely used programming language in database management and is the standard language for relational database management systems (RDBMS). SQL programming allows users to return, analyze, create, manage, and delete data within a database - all within a few commands.

If you've been following the news, you probably know that artificial intelligence is taking over. Self-driving trucks, face recognition, machines, and automatic cashiers are becoming the replacement for regular human beings. If you want to learn more about artificial intelligence, where it comes from, how some of it works, and what the future might look like, this audiobook is for you.

This book consists of 6 titles, namely: Assembly Language: The Ultimate Guide to Programming and Virtual Machines. Computer Science: The Complete Guide to Principles and Informatics. Cyber Security: Beginners’ Guide to Hacking, Phishing, Social Engineering, and Malware. Databases: System Concepts, Designs, Management, and Implementation. Operating Systems: Concepts to Save Money, Time, and Frustration. Website Development: Web Design Skills for Beginners

We can't live without the internet, but we know very little about the secret industry that keeps the wheels of modern life turning; the data centre industry. This is where the internet lives. This is the industry that has the power to make or break our connections. Cloaked in mystery, with secrets, scandals, and bloodthirsty boardroom battles raging behind the scenes, this is where the magic happens. Michael Tobin OBE has been at the forefront of the data centre industry since its birth. This is his never-before-published account of the explosion of the data centre industry.

Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications.

The discipline of infonomics takes you beyond thinking and talking about information as an asset to actually valuing and treating it as one. Infonomics provides the foundation and methods for quantifying information asset value and tactics for using information as your competitive edge to drive growth.

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.

Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data.