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Big Data Analysis

A required course in the Data Science Certificate Program

Course closed to new registrations: Call ( 949 ) 824-5414 for more information or sign up below to be notified when this course becomes available.×

Course Description

Big data is one of the most important technology trends to fundamentally impact the way organizations operate and compete. As more and more companies collect large amounts of data through their daily operations, the ability to analyze and glean knowledge from big data has become an integral part of a successful business. This course will help students navigate through the complex layers of Big Data while providing insight on ways to effectively use technologies and architectures to create and manage big data workflows. Concepts covered include an introduction to Big Data and related technologies, discussion of Big Data Processing Architectures, explanation of major concepts behind Big Data Management, and how all of those topics are applied in Big Data Analysis. Students will gain an understanding of the characteristics of big data and techniques for working on big data platforms through hands-on exercises in the tools and systems used by data scientists and data engineers including Hadoop (HiveQL & PIG), Apache Spark, and SparkSQL. Prerequisite: I&C SCI X427.05 Fundamentals of Data Science

Prerequisite: I&C SCI X427.05 Fundamentals of Data Science

NOTE: This course may use live sessions via Zoom. While students are highly encouraged to attend, all sessions are optional and will be recorded. A device with audio and visual will be needed to participate. The following student guide provides additional resources/information on how to use and access your courses Zoom sessions.

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  • Details
  • $820
  • July 12, 2021 to September 05, 2021
  • Delivery Mode: Online
  • Reg#: 00258
  • ID/Units: I&C SCI X427.07  (2.50)
    ( Section 1 )
  • Quarter: SUMMER 2021

Instructor


Nick Kadochnikov is a distinguished Engineer in Data Science and Cognitive Analytics within IBM’s Chief Analytics Office, as well as a lecturer at the University of Chicago, Master of Science in Analytics program. Nick’s expertise lies in the areas of Big Data, data mining, predictive modeling, econometric modeling, social media analytics and natural language processing. In his 15+ years professional career Nick has been applying analytics to various areas of the business, such as marketing, sales, fraud detection, product development, financial optimization, process simplification, and engagement analytics. Nick has been leveraging Big Data and data mining techniques to develop a multitude of analytical deliverables, including client wallet estimates and client segmentation models; cost-benefit modeling; propensity to buy, cross-sell and up-sell modeling; fraud, abuse and error detection; development productivity optimization and social business analytics.

Textbook Information

Textbooks for your course may be purchased from any vendor or bookseller of your choice.

No textbook information is available for this course.

Meeting Schedule

EventDateDayStart TimeEnd TimeLocationRoom
START07/12/2021Monday------Online (Access Begins)---
OL-LEC07/15/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC07/22/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC07/29/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC08/05/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC08/12/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC08/19/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC08/26/2021Thursday8:00 PM9:00 PMZoom---
OL-LEC09/02/2021Thursday8:00 PM9:00 PMZoom---
END09/05/2021Sunday------Online (Access Ends)---