Wind Resource Assessment - Data Analysis Using MATLAB

In this webinar, you will learn how to use MATLAB for data analysis from data access through visualization and modeling. Using measured wind data for wind farm siting, MathWorks engineers will demonstrate the use of MATLAB and data analysis products for the entire data analysis and modeling process. Webinar highlights include: Importing measured data recorded from a data logge. Performing data quality assurance tests for erroneous and missing data. Exploratory data analysis and visualization, including wind rose plots and velocity histograms. Turbine performance estimation. Automating repetitive data analysis and reporting tasks.
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Spotlight

OTHER ON-DEMAND WEBINARS

Common Mistakes to avoid when implementing your Data Governance Program

Are you planning to implement a data governance program? What are the things that practitioners often overlook or underestimate, and what are the most common mistakes they make? Join us to hear from Katherine Fraser, CEO of 1 to learn more about the key components of a successful data governance program, common mistakes made and to avoid when implementing a data governance program, and tips for maintaining a successful data governance program.
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How to Operationalize Data Mapping for Engineering

In this webinar, we will discuss the importance of data maps in building a privacy program. We look at the gap between privacy and engineering teams, the problems of scaling privacy programs with a business process approach to data mapping, and how an application-led approach helps you develop dynamic & complete data maps.
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BI & Data Trends 2022

According to PwC, nearly 4 out of 5 CEOs believe that remote collaboration will last after the pandemic. And Gartner predicts that through 2025, 80% of organizations trying to scale digital business will fail because they don’t take a modern approach to data and analytics governance.
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Planning for a Scalable Enterprise Data Lake

tdwi.org

In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.
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