In this video, data management expert John Adler leads you through the maze of data governance issues facing companies today—security breaches, regulatory agencies, in-house turf battles over who controls the data, monetizing data, and more. In this fast-paced and thorough discussion of how to plan for, implement, and run a successful data governance program, you'll get an overview of the ways data has been managed in the past and how the smart companies do it now.
How to Process Survey Data and Analyze Likert Scales In SPSS.
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.
Introduction to Descriptive Statistics / Data Analysis in SPSS and Beyond
Get a practical introduction to Hadoop, the framework that made big data and large-scale analytics possible by combining distributed computing techniques with distributed storage. In this video tutorial, hosts Benjamin Bengfort and Jenny Kim discuss the core concepts behind distributed computing and big data, and then show you how to work with a Hadoop cluster and program analytical jobs. You'll also learn how to use higher-level tools such as Hive and Spark.
Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques.