Data Science Theory

Whether you're new to the world of programming and are looking for clarity on what direction to take, or you're simply curious about what machine learning, AI, and big data has in store for the future and want to prepare for it, this guide makes it easy to actually understand implement machine learning and data analytics effectively.

Un científico de datos es alguien que sabe más de informática que un estadístico, y conoce más estadísticas que un científico de la computación. Si bien, en gran medida, esto es cierto, convertirse en un buen científico de datos requiere el dominio no solo de estas dos áreas clave, sino también de algunas teorías y modelos cruciales para este campo. El libro se recomienda a todos los lectores interesados ​​que aspiran a destacarse en el campo de la ciencia de datos.

Today, 91 percent of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence. So, future programmers will no longer find work without having knowledge of this particular field that is constantly evolving.

A team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.

The ultimate guide of how-to templates with real-world examples that demystify the art and science of AI prompt writing, offering clear frameworks, proven templates, and step-by-step instructions. This empowering guide reveals the exact formulas behind successful prompts with structured templates, troubleshooting guides, and practical exercises. Develop the critical thinking skills needed to achieve consistent, high-quality AI outputs.

Presenting a clear, accessible guide to solving important leadership challenges through human resources-focused and other data analytics, this engaging audiobook tells you how to transform the HR function and overall organizational effectiveness by using data to make decisions grounded in facts vs opinions, identify root causes behind your company's thorniest problems, and move toward a winning, future-focused business strategy.

Machines are able to interpret and understand human beings, and also interpret the data that is fed to them because of artificial intelligence. So, what is artificial intelligence? If you have been reading the basics, you will know that artificial intelligence is a human being’s attempt to make a machine smart enough to handle processes that a human being would normally do. It is not easy to do this, since you will need to understand the system and also learn the required programming languages to instruct the computer to behave in the required manner.

The Inclusion Equation is a comprehensive, one-of-a-kind guide to merging DEI and employee wellbeing concepts with data analytics and AI. In this book, renowned thought leader and professional keynote speaker Dr. Serena H. Huang explains exactly how to quantify the effectiveness of new talent strategies by connecting them to a firm ROI estimate, enabling listeners to approach and win the favor of higher-ups in any organization with the same effectiveness that marketing and financial departments do.

Artificial intelligence is the secret behind the big ones like Google, Facebook, Amazon, and we all know that. But it can also be a powerful tool in your own hands. With this audiobook, you will prepare the ground for your future success, either if you want to start up your own AI enterprise, apply your knowledge to your current business, or find a job at the greatest and most innovative companies. If programming will open you many doors, Python programming will open you even more.

As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion.

Data Lake Architecture will explain how to build a useful data lake where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.

Yes, you can succeed in an AI-driven world by unleashing your potential in machine learning! A working knowledge of AI and ML is no longer a “nice-to-have” but an “essential” in today's job market. But what if you feel lost amid the tech jargon? What if terms like “algorithms”, “neural networks”, or “data preprocessing” sound like a foreign language to you?

Artificial intelligence is the future, but it’s also a great opportunity today. You can use it to create incredible products with never seen before features. Probably, you are already using AI technologies in your everyday routine, and you don’t even know it! By the way, AI represents a significant opportunity if you want to grow your business or if you are searching for a new job, but it requires a solid background in terms of processes and technologies involved and, of course, you must fully understand how to implement AI in the business world.

If you need to learn how to use machine learning, big data, data science, and neural networks but you can’t (or there is no time to) study the complicated math and algorithms behind these technologies, then keep reading. All technologies related to artificial intelligence represent an incredible opportunity if you want to grow your business or if you are searching for a new job, but it’s not very easy to understand how they work.

In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally - hiring, driving, paying bills, even choosing romantic partners - that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology.

This book will open your eyes to the pitfalls of perception, and help you build a foundation for trust that elevates organizational performance... so you can make your data and AI initiatives succeed where others fail. Data, Strategy, Culture & Power is a must-listen for anyone who works with (or uses) data, analytics, and AI in today's complex organizations.

Alex Vail took a sabbatical from his job on the board of one of the country's leading manufacturers to conduct several research projects, including the largest ever study into the UK's corporate AI capabilities. In total, he surveyed 234 senior leaders and interviewed 92 executives from FTSE350 companies to identify why digital transformations succeed or fail; the data dependency of organisations; and their levels of data literacy at senior levels.

ChatGPT e gli altri sistemi di intelligenza artificiale generativa, capaci di produrre testi e immagini in risposta a una richiesta dell’utente, rappresentano in prospettiva una vera e propria rivoluzione. Probabilmente la terza grande rivoluzione causata dal digitale, dopo la diffusione del personal computer e dell’idea di informatica personale e dopo l’esplosione di Internet e dell’accesso mobile e universale alla rete.

AI Product Development is a comprehensive and practical guide for product managers, engineers, data scientists, and tech leaders who are committed to creating intelligent products that solve real-world problems at scale. This book bridges the gap between machine learning theory and practical product delivery, offering a step-by-step blueprint for integrating AI into every stage of product development.

Machine learning is a use of artificial intelligence (AI) that gives software the capacity to naturally learn and improve without being unequivocally modified. Machine learning centers around the improvement of computer programs that can get to data and use it to learn for themselves. The way toward learning starts with perceptions or data - for example, models, direct understanding, or guidance, so as to search for designs in data and settle on better choices later, depending on the models that we give.