STAT 480

Fall 2024 Part of Term 1

Part of Term 1
Aug 26-Dec 11

Big Data Analytics

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Credit: 3 OR 4 hours.

Examines current topics and techniques for efficiently and effectively managing and analyzing large-scale data. The course focuses on applications of advanced statistical analysis in data science for massive data sets. Topics include current best practices and technologies for implementation such as parallel and distributed processing, distributed storage techniques, and modern computational frameworks such as cloud computing.

3 undergraduate hours. 4 graduate hours. Prerequisite: (STAT 440 or STAT 447) and (STAT 420 or STAT 425); or permission of the instructor.

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STAT 480 class schedule data for fall 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
65110	Lecture-Discussion	1GR	9:30AM -10:50AM	TR	English Building	Wang, Y	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Info: This class covers topics and techniques for efficiently and effectively analyzing large-scale datasets, and translating insights into actionable applications. In the course, we will gain a deep understanding of the characteristics and challenges inherent in big data, along with advanced statistical models tailored for these challenges. Both basic underlying concepts and practical computational skills, such as distributed and parallel computing, will be covered. We will explore implementation in a cloud computing environment. Heavy emphasis is placed on analyzing real datasets and extracting meaningful and interpretable insights. We will consider examples such as consumer database mining, internet and social media tracking, asset pricing, network analysis, sports analytics, and text mining. Restriction(s): Restricted to Graduate - Urbana-Champaign.
65111	Lecture-Discussion	1UG	9:30AM -10:50AM	TR	English Building	Wang, Y	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 3 hours Section Info: This class covers topics and techniques for efficiently and effectively analyzing large-scale datasets, and translating insights into actionable applications. In the course, we will gain a deep understanding of the characteristics and challenges inherent in big data, along with advanced statistical models tailored for these challenges. Both basic underlying concepts and practical computational skills, such as distributed and parallel computing, will be covered. We will explore implementation in a cloud computing environment. Heavy emphasis is placed on analyzing real datasets and extracting meaningful and interpretable insights. We will consider examples such as consumer database mining, internet and social media tracking, asset pricing, network analysis, sports analytics, and text mining. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
71530	Lecture-Discussion	2GR	11:00AM -12:20PM	TR	Henry Administration Bldg	Wang, Y	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Info: This class covers topics and techniques for efficiently and effectively analyzing large-scale datasets, and translating insights into actionable applications. In the course, we will gain a deep understanding of the characteristics and challenges inherent in big data, along with advanced statistical models tailored for these challenges. Both basic underlying concepts and practical computational skills, such as distributed and parallel computing, will be covered. We will explore implementation in a cloud computing environment. Heavy emphasis is placed on analyzing real datasets and extracting meaningful and interpretable insights. We will consider examples such as consumer database mining, internet and social media tracking, asset pricing, network analysis, sports analytics, and text mining Restriction(s): Restricted to Graduate - Urbana-Champaign.
71531	Lecture-Discussion	2UG	11:00AM -12:20PM	TR	Henry Administration Bldg	Wang, Y	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 3 hours Section Info: This class covers topics and techniques for efficiently and effectively analyzing large-scale datasets, and translating insights into actionable applications. In the course, we will gain a deep understanding of the characteristics and challenges inherent in big data, along with advanced statistical models tailored for these challenges. Both basic underlying concepts and practical computational skills, such as distributed and parallel computing, will be covered. We will explore implementation in a cloud computing environment. Heavy emphasis is placed on analyzing real datasets and extracting meaningful and interpretable insights. We will consider examples such as consumer database mining, internet and social media tracking, asset pricing, network analysis, sports analytics, and text mining Restriction(s): Restricted to Undergrad - Urbana-Champaign.