Enabling Technologies for Data Science and Analytics: The Internet of Things

5 Weeks 7–10 hours per week
1744

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About this course

The Internet of Things is rapidly growing. It is predicted that more than 25 billion devices will be connected by 2020.

In this data science course, you will learn about the major components of the Internet of Things and how data is acquired from sensors. You will also examine ways of analyzing event data, sentiment analysis, facial recognition software and how data generated from devices can be used to make decisions.

What you’ll learn

  • Networks, protocols and basic software for the Internet of Things (IoT)
  • How automated decision and control can be done with IoT technologies
  • Discuss devices including sensors, low power processors, hubs/gateways and cloud computing platforms
  • Learn about the relationship between data science and natural language and audio-visual content processing
  • Study research projects drawn from scientific journals, online media, and novels
  • Review fundamental techniques for visual feature extraction, content classification and high-dimensional indexing
  • Techniques that can be applied to solve problems in web-scale image search engines, face recognition, copy detection, mobile product search, and security surveillance
  • Examine data collection, processing and analysis

Prerequisites

  • High school math
  • Some exposure to computer programming

Meet Your Instructors

Fred Jiang

Fred Jiang

Assistant Professor in the Electrical Engineering Department at Columbia University
Fred received his B.Sc. (2004) and M.Sc. (2007) in Electrical Engineering and Computer Science, and his Ph.D. (2010) in Computer Science, all from UC Berkeley. Before joining SEAS, he was Senior Staff Researcher and Director of Analytics and IoT Research at Intel Labs China. Fred’s research interests include cyber physical systems and data analytics, smart and sustainable buildings, mobile and wearable systems, environmental monitoring and control, and connected health & fitness. His ACme building energy platform has been widely adopted by universities and industries, including Lawrence Berkeley National Laboratory, National Taiwan University, and several commercial companies. His project on wearable and mobile fitness, in collaboration with University of Virginia, was featured on New Scientist and the Economist magazine. His air-quality monitoring project has been featured on China Central Television and People’s Daily, and was successfully incubated into a startup. He is actively serving on several technical and organizing committees including ACM SenSys, ACM/IEEE IPSN, and ACM BuildSys. He was a National Science Foundation (NSF) Graduate Fellow and a Vodafone-US Foundation Fellow.
Michael Collins

Michael Collins

Vikram S. Pandit Professor of Computer Science at Columbia University
Michael J. Collins is a researcher in the field of computational linguistics. His research interests are in natural language processing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. One notable contribution is a state-of-the-art parser for the Penn Wall Street Journal corpus. His research covers a wide range of topics such as parse re-ranking, tree kernels, semi-supervised learning, machine translation and exponentiated gradient algorithms with a general focus on discriminative models and structured prediction.
Shih-Fu Chang

Shih-Fu Chang

Richard Dicker Chair Professor at Columbia University
Shih-Fu Chang’s research interest is focused on multimedia retrieval, computer vision, signal processing, and machine learning. He and his students have developed some of the earliest image/video search engines, such as VisualSEEk, VideoQ, and WebSEEk, contributing to the foundation of the vibrant field of content-based visual search and commercial systems for Web image search. Recognized by many best paper awards and high citation impacts, his scholarly work set trends in several important areas, such as compressed-domain video manipulation, video structure parsing, image authentication, large-scale indexing, and video content analysis. His group demonstrated the best performance in video annotation (2008) and multimedia event detection (2010) in the international video retrieval evaluation forum TRECVID. The video concept classifier library, ontology, and annotated video corpora released by his group have been used by more than 100 groups. He co-led the ADVENT university-industry research consortium with the participation of more than 25 industry sponsors. He has received IEEE Signal Processing Society Technical Achievement Award, ACM SIGMM Technical Achievement Award, IEEE Kiyo Tomiyasu award, IBM Faculty award, and Service Recognition Awards from IEEE and ACM. He served as the general co-chair of ACM Multimedia conference in 2000 and 2010, Editor-in-Chief of the IEEE Signal Processing Magazine (2006-8), Chairman of Columbia Electrical Engineering Department (2007-2010), Senior Vice Dean of Columbia Engineering School (2012-date), and advisor for several companies and research institutes. His research has been broadly supported by government agencies as well as many industry sponsors. He is a Fellow of IEEE and the American Association for the Advancement of Science.
Julia Hirschberg

Julia Hirschberg

Percy K. and Vida LW Hudson Professor of Computer Science at Columbia University
Julia Hirschberg does research in prosody, spoken dialogue systems, and emotional and deceptive speech. She received her PhD in Computer Science from the University of Pennsylvania in 1985. She worked at Bell Laboratories and AT&T Laboratories -- Research from 1985-2003 as a Member of Technical Staff and as a Department Head, creating the Human-Computer Interface Research Department at Bell Labs and moving with it to AT&T Labs. She served as editor-in-chief of Computational Linguistics from 1993-2003 and as an editor-in-chief of Speech Communication from 2003-2006. She is on the Editorial Board of Speech Communication and of the Journal of Pragmatics. She was on the Executive Board of the Association for Computational Linguistics (ACL) from 1993-2003, have been on the Permanent Council of International Conference on Spoken Language Processing (ICSLP) since 1996, and served on the board of the International Speech Communication Association (ISCA) from 1999-2007 (as President 2005-2007). She is currently the chair of the ISCA Distinguished Lecturers selection committee. She is on the IEEE SLTC, the executive board of the North American chapter of the Association for Computational Linguistics, the CRA Board of Directors, and the board of the CRA-W. She has been active in working for diversity at AT&T and at Columbia. She has been a fellow of the American Association for Artificial Intelligence since 1994, an ISCA Fellow since 2008, and became an ACL Fellow in the founding group in 2012. She received a Columbia Engineering School Alumni Association (CESAA) Distinguished Faculty Teaching Award in 2009, received an honorary doctorate (hedersdoktor) from KTH in 2007, is the 2011 recipient of the IEEE James L. Flanagan Speech and Audio Processing Award and, also received the ISCA Medal for Scientific Achievement in the same year.
1744

Experience Level

Introductory

Learning Partner

Columbia University

Program Type

Professional Certificate

Subject

Data Analysis & Statistics Data Science IT
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