Course 4 of 5 in the Applied Data Science with Python Specialization.
Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Institution: University of Michigan, via Coursera
Level: Intermediate
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
This course will teach you the skills to deploy software with confidence, agility, and high reliability using modern practices such as Continuous Integration (CI) and Continuous Delivery (CD).
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This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.
Improve your versatility in creating various types of Node.js applications. Covering a broad set of use cases and using Node.js core APIs with selected ecosystem libraries, this course fully prepares you for the Node.js Application Developer Certification.
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This course provides a deep dive into Node core HTTP clients and servers, web servers, RESTful services, and web security essentials. With a major focus on Node.js services and security, this content is an essential counterpart to the Node.js Application Development course and will prepare you for the OpenJS Node.js Services Developer (JSNSD) exam.
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Learn about the fundamentals of the Linux Foundation OPNFV project and how it can help accelerate your Network Functions Virtualization (NFV) transformation from fixed-function, proprietary devices to flexible, software-driven environments.
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This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.