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Data science

A-Z guide to Smart City Initiatives

White Paper: PTC

Urbanization is not a recent phenomenon. The migration of people from rural to urban areas has been going on for millennia. But what is different today is the speed and scale of that migration.   According to a United Nations report, by 2050 two-thirds of humanity will live in more than 40 mega-cities of 10 million people each. Cities will have to improve efficiency and reduce expenditures through new technologies and other means. Global companies are looking to transform major cities through advanced computer technologies, sensors, high-speed data networks, predictive analytics, big data, and the IoT. Before a city begins a smart city initiative, its leaders need to identify the challenges the city faces and must be ready to look at the way its systems, services, policies, and procedures are working. The right technology solutions will help to focus on addressing the challenges defined by city's unique nature. Download the guide now to discover: How will newer cities acquire the resources necessary for supporting large populations? How can data science be used to solve urban problems? What is the role of citizens in smart cities?

How to Make the Most of Your Data-Science Dollar

White Paper: Mosaic Data Science

Data scientists are a scarce commodity, and are likely to remain so for years to come. At the same time, data science can create a substantial competitive advantage for early adopters who make the best use of their scarce data-science resources. data scientists often struggle to recognize that every data-science project is an optimization problem.iii In business the objective function is usually expected value, but sometimes expected value is and should be traded away to avoid the possibility of catastrophic losses. Data science has a long, highly technical learning curve. An inexperienced team with limited background has two ways to shorten its learning curve. The first is training. For example, Coursera offers a series of online classes designed to train data scientists in specific computational techniques and tools.  This paper explains how management can make the most of data-science resources and opportunities, to achieve data-driven competitive advantage.    

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