Micro Credentials in San Francisco
Advance your career with an education from a globally recognized university.
Our Micro Credentials offer continued training in areas including Data Science, Networks and Security, Cloud and Product Development. These courses are designed for working professionals interested in improving their technology skill set. Students will learn applicable skills from industry experts that they can apply in their workplace.
Northeastern University’s Micro Credentials provide focused and immediate impact to professionals looking to enhance their technology skill set without the time commitment of a full-time program.
By submitting the form, you will receive information from Northeastern University with details about Micro Credentials and other programs by email or receive a phone call from one of our enrollment coaches. You can unsubscribe at any time.
Why Micro Credentials?
Our classes are stackable and allow students to apply the credit earned towards a Masters in Computer Science.
Register now for Machine Learning in San Francisco!
Provides a broad look at a variety of techniques used in machine learning and data mining, and examines issues associated with their use. Topics include algorithms for supervised learning including decision tree induction, artificial neural networks, instance-based learning, probabilistic methods, and support vector machines; unsupervised learning; and reinforcement learning. Also covers computational learning theory and other methods for analyzing and measuring the performance of learning algorithms. Course work includes a programming term project.
Students must have an undergraduate degree in Computer Science or Computer Engineering or seven (7) or more years of experience in software development.
Class Schedule and Location
Machine Learning class starts in June 2018.
Classes will be held at Lookout in San Francisco – 1 Front Street, Suite 2700, San Francisco, CA 94111.
How to Register
Please complete the inquiry form above to receive registration information for Data Mining Techniques.
Other Classes Coming Soon
Below is a list of classes on Data Science, Security and Cloud Computing that will also be offered in San Francisco.
Data Mining Techniques
This class covers various aspects of data mining, including classification, prediction, ensemble methods, association rules, sequence mining, and cluster analysis. Students will have the opportunity to experience hands-on practice of mining useful knowledge from a large data set.
Parallel Data Processing in MapReduce
Introduces the MapReduce programming model and the core technologies it relies on in practice, such as a distributed file system and the distributed consensus protocol. Also discusses related approaches and technologies from distributed databases and cloud computing. Emphasizes practical examples and hands-on programming experiences. Examines both plain MapReduce and database-inspired advanced programming models running on top of a MapReduce infrastructure.
Studies the theory and practice of computer security, focusing on the security aspects of multiuser systems and the Internet. Introduces cryptographic tools, such as encryption, key exchange, hashing, and digital signatures in terms of their applicability to maintaining network security. Discusses security protocols for mobile networks. Topics include firewalls, viruses, Trojan horses, password security, biometrics, VPNs, and Internet protocols such as SSL, IPSec, PGP, SNMP, and others.
Software Vulnerabilities and Security
Learn about systems security issues and gain a basic understanding of security. Presents the principal software and applications used in the Internet, discussing in detail the related vulnerabilities and how they are exploited. Also discusses programming vulnerabilities and how they are exploited. Examines protection and detection techniques. Includes a number of practical lab assignments as well as a discussion of current research in the field.
Mobile Application Development
Focuses on mobile application development on a mobile phone or related platform. Discusses memory management; user interface building, including both MVC principles and specific tools; touch events; data handling, including core data, SQL, XML, and JSON; network techniques and URL loading; and, finally, specifics such as GPS and motion sensing that may be dependent on the particular mobile platform. Students will work on a project that produces a professional-quality mobile application and to demonstrate the application that they have developed.
Building Scalable Distributed Systems
This course will cover the essential elements of distributed, concurrent systems and build upon that knowledge with engineering principles and practical experience with state-of-the-art technologies and methods for building scalable systems. Scalability is an essential quality of Internet-facing systems, and requires specialized skills and knowledge to build systems that scale at low cost.