Free Big Data Analytics and Advanced Big Data Analytics Course – Columbia

With the advance of IT storage, processing, computation, and sensing technologies, Big Data has become a novel norm of life. Only until recently, computers are able to capture and analysis all sorts of large-scale data from all kinds of fields — people, behavior, information, devices, sensors, biological signals, finance, vehicles, astronology, neurology, etc. Almost all industries are bracing into the challenge of Big Data and want to dig out valuable information to get insight to solve their challenges.

Free Big Data Analytics and Advanced Big Data Analytics Course

Free Big Data Analytics and Advanced Big Data Analytics Course
Free Big Data Analytics and Advanced Big Data Analytics Course

This course shall provide the fundamental knowledge to equip students being able to handle those challenges. This discipline inherently involves many fields. Because of its importance and broad impact, new software and hardware tools and algorithms are quickly emerging. A data scientist needs to keep up with this ever changing trends to be able to create a state-of-the-art solution for real-world challenges.

This Big Data Analytics course shall first introduce the overview applications, market trend, and the things to learn. Then, I will introduce the fundamental platforms, such as Hadoop, Spark, and other tools, e.g., Linked Big Data. Afterwards, the course will introduce several data storage methods and how to upload, distribute, and process them. This shall include HDFS, HBase, KV stores, document database, and graph database. The course will go on to introduce different ways of handling analytics algorithms on different platforms. Then, I will introduce visualization issues and mobile issues on Big Data Analytics. Students will then have fundamental knowledge on Big Data Analytics to handle various real-world challenges.

Afterwards, the course will zoom in to discuss large-scale machine learning methods that are foundations for artificial intelligence and cognitive networks. The course will discuss several methods to optimize the analytics based on different hardware platforms, such as Intel & Power chips, GPU, FPGA, etc. The lectures will conclude with introduction of the future challenges of Big Data, especially on the ongoing Linked Big Data issues which involves graphs, graphical models, spatio-temporal analysis, cognitive analytics, etc.


  • Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
  • Gain knowledge on this fast-changing technological direction. Big Data Analytics is probably the fastest evolving issue in the IT world now. New tools and algorithms are being created and adopted swiftly. Get insight on what tools, algorithms, and platforms to use on which types of real world use cases.
  • Get hands-on experience on Analytics, Mobile, Social and Security issues on Big Data through homeworks and final project
  • Final Project Reports will be published as Proceedings and Final Project Software will become Open Source.

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