Professional Topics

Program Format
Application Deadlines
Multiple start dates throughout the year; inquire for specifics.
Credits Required for Graduation

Professional Topics

Learn Data Science straight from the experts through Northeastern University’s Professional Topics.

Data Science helps businesses make better decisions by having the right information at the right time. Northeastern University’s Professional Topics are a series of short courses designed to give participants in-depth education on topics in data science through a limited engagement format. Our students will have instructors that are prominent in the field of computer and data science, and come from an industry located in Silicon Valley.

What You’ll Learn
Data Science is an essential key to anyone looking for career development in the tech industry. It is used to meet the growing client needs and demands in many of today’s businesses. The Professional Topics will not only help you stay on top of latest trends but also grow your professional network by learning side by side with like-minded Silicon Valley professionals. Below are just some of the classes offered under our Professional Topics series.

> Deep Learning
Provides an overview of the basic concepts behind neural networks. Topics include the basics of neural networks, main variants, designing neural networks, and deep learning tools. The audience for this course includes computer scientists, data scientists, mathematicians and engineers with machine learning knowledge. Students should possess a strong mathematical background and knowledge of machine learning concepts.

> Introduction To Search
Introduces the foundational concepts of search and how to search large amounts of unstructured data. Topics include basic architecture, retrieval models, indexing, querying and ranking, evaluation, and applications. Audiences for this course include developers, computer scientists, data scientists, and engineers. Students should possess a comprehensive background in algorithms.

> Data Exploration and Visualization
This course will allow students to learn and practice how to explore and visualize data. Topics include basic concepts of design, design rules, visual encoding, perception, techniques, and practice in Tableau and similar toolkits. Audiences for this course include computer scientists, data scientists, developers, and engineers. Students should possess knowledge of data processing and statistics.


Who should sign up?
Whether you’re a computer science student, a technology professional or an entry-level data scientist wanting to learn more, our Professional Topics are for you. Our students can quickly take what they learned in the classroom and apply it in the workplace.




Meet the Instructors

Dr. James (Jimi) Shanahan
Dr. Shanahan has over 20 years of experience researching and developing information management systems that harness information retrieval, statistical natural language processing, and machine learning in application domains such as web search and computational advertising. He has published seven books and 45 refereed papers in machine learning and information systems. He has fourteen US patent filings, ten of which have been granted to date.  He is the Founder and Principal of Boutique Data Consultancy in San Francisco and his previous positions include Chief Scientist at Turn Inc., Principal Research Scientist at Clairvoyance Corp., and Research Scientist at Xerox Research Centre Europe among others.  Dr. Shanahan is the instructor for Northeastern’s Deep Learning course in Silicon Valley.

Ph.D. in Machine Learning, University of Bristol
Bachelor of Science in Computer Science and Business, University of Limerick


Ricardo Baeza-Yates
Ricardo is currently the CTO of NTENT, a search technology company based in Carlsbad, California. Prior to this, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California. Between 2008 and 2012 he also supervised Yahoo Labs Haifa and between 2012 and 2014 Yahoo Labs London. He was also the director of the Center for Web Research at the Department of Computer Science of the Engineering School of the University of Chile; and ICREA Professor and founder of the Web Research Group at the Dept. of Information and Communication Technologies of Universitat Pompeu Fabra in Barcelona, Spain. He maintains ties with both mentioned universities as a part-time professor.
Ricardo’s research interests include algorithms and data structures, information retrieval, web search and data mining, and data science and visualization.  He is an ACM and IEEE Fellow.  Ricardo is the instructor for Northeastern’s Introduction to Search course in Silicon Valley.

Ph.D. in Computer Science, University of Waterloo
MS in Engineering, Universidad de Chile
MS in Computer Science, Universidad de Chile
BS in Electronic Engineering, Universidad de Chile
BS in Computer Science, Universidad de Chile


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