Articles about Data Science

Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice.

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With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.

The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.

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Data Science Handbook offers a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains.

Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding.

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Platform-as-a-Service (PaaS) provider Rafay Systems is launching new capabilities for its enterprise PaaS for modern infrastructure to support graphics processing unit- (GPU-) based workloads.

This makes compute resources for AI instantly usale by developers and data scientists but still with the enterprise-grade protections.

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Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines.

This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together.

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Let Python do the heavy lifting for you as you analyze large datasets.

Python for Data Science for Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond.

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Want to jump into data science but don't know where to start?

Let's be real, data science is presented as something mystical and unattainable without the most powerful software, hardware, and data expertise. Real data science isn't about technology. It's about how you approach the problem.

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Data science and AI leaders are rushing to accelerate new technology adoption. According to Forrester, generative AI alone will see an average annual growth rate of 36 percent for the next seven years, taking 55 percent of the AI software market. The analyst firm also estimates that by 2030, $79 billion will be spent annually on specialized generative AI applications and $42 billion will be spent annually on generalized generative AI use cases. 

Even if Forrester is off by a few billion dollars, moving fast is still critical to achieving success, but so is having an optimal AI strategy. Leaders must ensure that practitioners have the technology they need to innovate. At the same time, they must ensure innovation does not exceed budgets or introduce new risks. 

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In today's interconnected world, businesses not only grapple with the management of vast amounts of data but also face the looming threat of illegal data concealed within their digital repositories. This proliferation of illegal data presents a range of risks and challenges that organizations must confront.

Illegal data encompasses a broad spectrum of content or files that contravene laws, regulations, and/or company policy. It includes materials such as pirated software, confidential information obtained through unlawful means, and content that promotes or facilitates illegal activities; as well as content that is simply not acceptable or useful on the corporate network such as holiday videos and cat pics.

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Artificial intelligence is generally only as good as the data that it's trained on. However, when data is collected and used in the training of machine learning models, the models inherit the bias of the people building them, producing unexpected and potentially harmful outcomes.

We spoke to Matthieu Jonglez, VP, technology at Progress, to discuss the company's recent research around this topic and what organizations can do to reduce bias.

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Written and edited by a team of experts in the field, this book reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

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A new survey of 100 chief data officers (CDOs) and chief data analytics officers (CDAOs) at companies with $1B+ in revenue shows that 95 percent say their company leadership expects investments in AI and ML applications will result in a revenue increase.

The study for Domino Data Lab, carried out by Wakefield Research, shows 67 percent are adopting a more offensive data policy seeking to drive new business value with analytics, ML and AI applications.

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Data science involves analyzing and interpreting large, complex amounts of data and turning it into valuable insight to achieve business goals. Though not exactly a breakthrough field now, it has changed many aspects of how markets conduct business.

In a conversation with Jerry Johnson, founder and president of Marketing Data Science Associates and director of Cascade Strategies, we talk about data science, its applications in marketing, and its future with the emergence of AI.

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More than half (54 percent) of respondents to a new survey say securing data with appropriate access rights is one of their biggest hurdles. While almost 60 percent believe their organizations should be placing extra emphasis on data security.

The third annual State of Data Engineering Survey from Immuta also finds that that 89 percent of organizations report missing business opportunities because of data access bottlenecks.

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Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help

What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is.

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