Online courses directory (4)
In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).
In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
How do you protect the critical data that is increasingly being stored in the cloud? Learn how to build a security strategy that keeps data safe and mitigates risk.
In this course, part of the Cloud Computing MicroMasters program, you will be introduced to industry best practices for cloud security and learn how to architect and configure security-related features in a cloud platform. Case studies and government standard documents will be reviewed to help ensure appropriate levels of security are implemented.
You’ll develop the necessary skills to identify possible security issues in the cloud environment. You will also gain experience with tools and techniques that monitor the environment and help prevent security breaches such as monitoring logs and implementing appropriate security policies.
Want to gain software quality skills used in mission critical systems?
Modeling checking, symbolic execution and formal methods are techniques that are used for mission critical systems where human life depends upon the system working correctly.
In this course, part of the Software Testing and Verification MicroMasters program, you will learn how to perform these techniques manually and by using automation tools.
No previous programming knowledge needed. The concepts from this course can be applied to any programming language and testing software. This course will use Java, Java Path Finder and Java Modeling Language, however, for examples and assignments.
Data driven decision-making systems allow instructional designers to engage in continuous improvement of course design to optimize student learning.
In this education and teacher training course, part of the Instructional Design and Technology MicroMasters program we will delve into data driven decision-making and you will learn data mining techniques to collect actionable data. You will learn how to analyze learner statistics to improve the impact of design and technology on learning.
It’s important for instructional designers to have systematic plans for gathering data and learner analysis to guide ongoing course adjustments to improve teaching and learning. Data is critical to the continuous improvement process.
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