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Introduction to Data Science
Tags:Machine Learning, Statistics, Big Data
Data is the new soil of business and (soon) at the core of essentially all domains from material science to healthcare. Mastering big data requires a set of skills spanning a variety disciplines from distributed systems over statistics to machine learning, a deep understanding of a complex ecosystem of tools and platforms, as well as communication skills to explain advanced analytics. This course will provide an overview of the wide area of data science. A particular focus will be given to the tools required to store, clean, manipulate, model and ultimately extract information out of data.
Introduction to Data Science
Tags:Machine Learning, Statistics, Big Data
Michael J. Franklin is a Professor of Computer Science at UC Berkeley, specializing in large-scale data management infrastructure and applications (these days called "Big Data"). I work primarily in the Database (DB) and Operating Systems and Networking Technology (OSNT) areas.
Introduction to Data Science
Tags:Machine Learning, Statistics, Big Data
Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).
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