Master modern data analysis tools: Learn Python, R, and SQL through hands-on practice with real-world datasets and Jupyter notebooks.
Master modern data analysis tools: Learn Python, R, and SQL through hands-on practice with real-world datasets and Jupyter notebooks.
This comprehensive course, part of the Analytics: Essential Tools and Methods MicroMasters program, teaches students how to build and connect components of the modern data analysis pipeline. The curriculum covers collection, preprocessing, storage, analysis, and interactive visualization of data. Students learn how computing and mathematics intersect through numerical linear algebra, optimization, and data processing algorithms. The course emphasizes hands-on practice with Python, R, and SQL through Jupyter notebooks, applied to real-world datasets to build practical skills.
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What you'll learn
Master the complete modern data analysis pipeline from collection to visualization
Develop proficiency in Python, R, and SQL programming languages
Learn to use Jupyter notebooks for data analysis and visualization
Understand numerical linear algebra and optimization fundamentals
Gain practical experience with real-world datasets
Master data processing algorithms and data structures
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a thorough introduction to computing principles for data analysis, combining theoretical foundations with practical applications. Students learn to work with modern analytical tools, understanding how computing and mathematics integrate in data analysis. The curriculum covers the complete data analysis pipeline, from data collection and preprocessing to analysis and visualization, using industry-standard tools like Python, R, and SQL through Jupyter notebooks.
Fee Structure
Instructor
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1 Course
Professor of Computational Science and Engineering
Richard (Rich) Vuduc, Professor at Georgia Institute of Technology's School of Computational Science and Engineering, has established himself as a leading figure in high-performance computing research and education. After earning his B.S. in Computer Science from Cornell University and Ph.D. from UC Berkeley, he began his journey with a postdoctoral position at Lawrence Livermore National Laboratory before joining Georgia Tech in 2007. As the director of The HPC Garage research lab, he focuses on advancing parallel algorithms, performance analysis, and performance engineering for scientific computing applications. His distinguished career includes numerous accolades, including a DARPA Computer Science Study Group grant, NSF CAREER award, and a collaborative Gordon Bell Prize in 2010. His contributions to education earned him the Lockheed-Martin Aeronautics Company Dean's Award for Teaching Excellence, while his research has garnered Best Paper Awards at prestigious conferences including SIAM Data Mining and IEEE IPDPS. Beyond his research, he has served in various leadership roles, including as his department's Associate Chair and Director of graduate programs, Chair of the SIAM Activity Group on Supercomputing, and as an associate editor for leading journals in high-performance computing. His work spans from developing innovative parallel computing algorithms to advancing scientific simulation and data analysis techniques, making him a vital contributor to the field of computational science and engineering.
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