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Fall 2022
Aug 07, 2022
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CS 44000 - Large Scale Data Analytics
Credit Hours: 3.00. This course provides an integrated view of the key concepts of modern algorithmic data analytics. It focuses on teaching principles and methods needed to analyze large datasets in order to extract novel, transformative insights for the underlying application. The course emphasizes the duality between formulating questions that can be answered by statistical data analysis tools (the statistical perspective) and the algorithmic challenge of actually extracting such answers using available parallel and distributed computational resources from massive datasets. The topics cover three areas: (1) algorithmic concepts necessary for big data analytics, (2) bid data systems, including data management and programming, and (3) advanced analytic methods to address characteristics of real-world big data problems.
0.000 OR 3.000 Credit hours

Syllabus Available
Levels: Undergraduate, Graduate, Professional
Schedule Types: Distance Learning, Laboratory, Lecture
All Sections for this Course

Offered By: College of Science
Department: Computer Science

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Identify techniques appropriate for solving a data analytics challenge. 2. Load and manipulate data in distributed computing environments. 3. Apply computing paradigms to effectively write efficient and scalable algorithms for data analysis pipelines. Know limitations, design details and design decisions of these algorithms. 4. Apply mixed-precision optimization and parallel learning algorithms to modern machine learning algorithms. 5. Present results in methods appropriate for domain experts.


Restrictions:
Must be enrolled in one of the following Majors:     
      Data Science
      Data Science
      Data Science

Prerequisites:
Undergraduate level CS 37300 Minimum Grade of C and Undergraduate level STAT 41700 Minimum Grade of C

Short Title: Large Scale Data Analytics

Course Configurations:

Configuration 1: 3.0 Credits
Schedule Type Weekly Contact Hours Instructional Credit Distribution
Lecture 2 2.0
Laboratory 2 1.0
Configuration 2: 3.0 Credits
Schedule Type Weekly Contact Hours Instructional Credit Distribution
Distance Learning 0 3.0

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