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The University of Texas at Austin

Master’s Degree in Data Science

The University of Texas at Austin via edX

Overview

The Department of Statistics and Data Sciences at The University of Texas at Austin has partnered with the Department of Computer Science to offer a Master of Science in Data Science. This new online master's program embodies the defining principles of Data Science, combining the leaders from both fields, to present a curriculum designed from the ground up to offer a solid foundational knowledge in Statistical theory upon which to build a Computer Science application. Course curriculum incorporates ideas and methods such as simulation, data visualization, data mining, data analysis, large scale data-based inquiry for big data, and non-standard design methodologies, along with topics of machine learning, algorithmic techniques, and optimization, to tackle issues that come up with large-scale data such as memory and computational speed.

Our program is designed to prepare you for the fastest growing, highest demanded job prospect in recent history. Step into the world of data-driven models and multi-dimensional datasets. Find answers in the areas of bioinformatics, linguistics, industry, academia, government, and nonprofits to name just a few.

At The University of Texas at Austin, we say: “What starts here changes the world.” Be that change. Prepare yourself for a career in Data Science.

Syllabus

This is a 30 hour program (3 credit hours per course). There are 3 core required courses and 7 additional required courses for a total of 10 courses. The core requirement will be satisfied with three foundational courses which will provide students with a broad, foundational understanding of the field and will also establish the basis for some of the prescribed electives. They include:

  • DSC 381: Probability and Simulation Based Inference for Data Science
  • DSC 382: Foundations of Regression and Predictive Modeling
  • DSC 388G: Algorithms: Techniques and Theory

Non-core requirements include the following courses:

  • DSC 383: Advanced Predictive Models for Complex Data
    • Pre-requisite of DSC 382
  • DSC 384: Design Principles and Causal Inference for Data-Based Decision Making
    • No Pre-requisite
  • DSC 385: Data Exploration, Visualization, and Foundations of Unsupervised Learning
    • No Prerequisite
  • DSC 91L: Principles of Machine Learning
    • Pre-requisite of DSC 382
  • DSC 395T: Advanced Linear Algebra for Computation
    • No Prerequisite
  • DSC 395T: Optimization
    • Pre-requisite of DSC 388G
  • DSC 395T: Deep Learning
    • Pre-requisite of DSC 382

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