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Data Science Practice: NoSQL & Non-Relational Databases & Monte Carlo Simulations

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NoSQL Databases & Monte Carlo SimulationsNoSQL & Non-Relational DatabasesSlide Presentation: databases have been the de facto technology for storing and querying data for 40 years. What is driving the recent innovation in databases? This talk will touch on the history of databases, why RDBMS have been so successful, and why we are seeing the rise of NoSQL databases. Next we will examine the different categories of NoSQL databases and technology. The presentation will finish with a specific introduction to MongoDB, its design principles, and what it looks like to code against.Will LaForest heads up the Federal practice for 10gen, the MongoDB company. Will is focused on evangelizing the benefits of MongoDB, NoSQL, and (OSS) open source software in solving Big Data challenges in the Federal government. He believes that software in the Big Data space must scale not only from a technical perspective but also from a cost perspective. He has spent 7 years in the NoSQL space focused on the Federal government, most recently as Principal Technologist at MarkLogic. His technical career spans diverse areas from data warehousing, to machine learning, to building statistical visualization software for SPSS but began with code slinging at DARPA. He holds degrees in Mathematics and Physics from the University of Virginia.Monte Carlo Simulation Methods in Energy Risk ManagementSlide Presentation: Carlo methods are stochastic techniques or probabilistic modeling - meaning they are based on the use of random numbers and probability statistics to investigate problems.They are used to model phenomena with significant uncertainty in inputs, such as the calculation of risk in business. When Monte Carlo simulations have been applied in space exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are routinely better than human intuition or alternative "soft" methods.For energy companies, understanding the impact of commodity price movements on the value of a portfolio is critical for hedging, risk management and planning purposes. For example, consider a gas-fired power plant which buys natural gas from a spot market, converts it into electricity, and sells that electricity into a deregulated power spot market.The generator is exposed to fluctuations in the price it must pay to purchase natural gas and the price it will receive for the sales of power. In order to reduce risks, a power plant operator may choose to buy in advance the natural gas that it anticipates it will need, and to sell in advance the power it anticipates it will generate -- that is contract in advance for the forward purchase of gas and the sale of power at a future delivery period, for a fixed price today. This practice, known as hedging, attempts to remove the uncertainty in future cash flows from the power plant.The decision on how much and how often to hedge will, in general, require sophisticated analytical methods. One popular method, Monte Carlo simulation, attempts to simulate future states of the world to understand the impact on cash flows.In this talk, we discuss Monte Carlo methods for energy risk applications. We review one popular approach, which uses a set of linked simulation models to capture the fundamental physical drivers of electricity price formation, and calibrates them to match current prices being quoted in the financial markets. Monte Carlo simulations of weather, load and prices can then be used to value a portfolio of generation assets and trades, and to support hedging and risk management decisions.Scotty Nelson is a Senior Energy Analyst at Ascend Analytics, where he deploys analytic software solutions to help companies understand and manage risk in the energy markets.

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