Showing results by author "Lars Nielsen"

Lars Nielsen and Noreen Burlingame provide a brief, understandable, user-friendly guide to all aspects of Data Science. The authors address the various skills required, the key steps in the Data Science process, software technology related to the effective practice of Data Science, and the best rising academic programs for training in the field.

The need for precise, actionable, real-time Business Intelligence (BI) lies at the heart of the role of Big Data in modern commerce. In turn, the art of Data Science lies at the nexus of Big Data and BI, providing the essential methods by which BI can be extracted from Big Data's great black mass of constantly flowing, unstructured information. This combination has a created a new profession: an elite and specialized class of highly-compensated professionals specially skilled at data cleaning, analysis, and visualization.

Noted systems developer, Lars Nielsen reveals the dynamic combination of innovation, competition and, often eccentric, entrepreneurship that went to shape today's tech landscape. From the military's ENIAC of 1946 to the ripening of the Web and e-commerce, Nielsen's Computing: A Business History details the evolution of computing as it has impacted the models and practices of markets and professional services around the globe.

In Unicorns Among Us, Lars Nielsen lays out the brief history of data science since its beginnings not long ago in this century. He as well explores the evolution of the role of the data scientist through this same period, and explores the best qualifications, the tools, the techniques, and the training involved in the practice of this arcane discipline.

Taking up where the best-selling A Simple Introduction to Data Science, left off, Lars Nielsen's A Simple Introduction to Data Science, Book 2 expands on elementary concepts introduced in the first volume while at the same time embracing several new and key topics.

Simply put, Dark Data is stored, largely non-inventoried, unstructured data not currently used for the purpose of conducting data science, but which is nevertheless maintained on a "just in case" basis - either to meet regulatory requirements, or in the hope that the data will prove useful for research purposes at some time in the future.