Forecasting with Big Data: A Review <s> Skills <s> An improved computed tomography radiation detector is disclosed. One embodiment includes first and second layers of crystalline scintilation material mutually aligned in a path of x-rays to be detected, to receive the x-rays in sequence. The layer upstream in the x-ray path comprises a scintillation material having a relatively high efficiency for converting x-ray energy to light. The downstream one of the layers comprises a scintillation material having a relatively lower efficiency for x-ray/light conversion. A photodiode is positioned to view both scintillation layers simultaneously and to respond to scintillations in either or both. Scintillation crystal material surfaces can be coated with reflective material to enhance the effects of their scintillations. The photodiode thus combines x-ray indicating scintillations from both crystals while in analog form. The detector exhibits enhanced response to lower energy x-rays. Another embodiment comprises a photodiode and an optically coupled scintillation crystal, with the photodiode upstream in the x-ray beam path relative to the crystal. <s> BIB001 </s> Forecasting with Big Data: A Review <s> Skills <s> Many believe that "big data" will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics. <s> BIB002
The skills required for tackling the problem of forecasting with Big Data, and the availability of personnel skilled for this particular task is one of the foremost challenges. As Arribas-Bel (2013) states, the advanced skills required to handle Big Data is a major challenge whilst Poynter (2013) notes there is a short supply of data scientists equipped with the skills required to tackle Big Data.  also agrees that there is a shortage of people who can understand Big Data. In a world where academics, researchers and statisticians are highly experienced in using traditional statistical techniques for over fifty years to obtain accurate forecasts, the availability of Big Data in itself is challenging. BIB001 states that the inaptness of traditional statistical techniques which are meant for obtaining forecasts from traditional data are hindering the effectiveness and application of forecasts from Big Data and Arribas-Bel (2013) shares this same concern. As majority of statisticians are experienced in these traditional techniques, BIB002 points out that it is a challenge to develop the required skills for Big Data forecasting. In order to overcome this issue, it is important that Higher Educational Institutes around the globe give due consideration to upgrading the educational syllabuses to incorporate the skills necessary for understanding, analysing, evaluating and forecasting with Big Data so that the next generation of statisticians will be well equipped with the mandatory skills.