Are you looking for a low-cost data science degree to help advance your career and increase earning potential? I’d say it’s time you look into the best data science college in Austin. Now, you know it’s not that easy to simply pick the right program – especially when so many are out there and it seems like they all offer the same thing. But hey, let me save you time and give you some options so you can quickly decide what’s right for you nowadays.

The University of Texas at Austin has a reputation for being one of the best, if not the best, universities in the country. It’s no surprise that they offer a Master’s in Data Science program—not to mention it being completely online.

The UT-Austin Online Masters in Data Science program is a great option for professionals who want to upgrade their skillset and advance their career. The coursework focuses on analyzing large datasets, which can be used to improve business processes and make better decisions about product design and development. The program includes an internship where you’ll get real world experience working with data scientists or other professionals in your field.

The acceptance rate for UT-Austin’s online masters data science program is around 40%, so you’ll want to apply early if you want to be considered. The application process is fairly straightforward; however, there are some required courses that must be completed before applying (see below). You’ll also need:

1) A bachelor’s degree from an accredited institution with at least a 3.0 GPA (or equivalent). You may also be required to submit an official copy of your transcript(s).

2) TOEFL score of 79 iBT or above (pending verification).

Collegelearners is replete with up-to-date information on ut austin online masters data science acceptance rate, ut austin online masters computer science, ut data science masters, and so much more. Need more information? Kindly visit our catalog for more. What are you waiting for?

ut austin online master’s data science

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.

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

CURRICULUM

Curriculum for the Master of Science in Data Science program is designed to offer a balance between foundational statistical theory and application through computer science processes. This is accomplished through courses in statistics with topics such as probability and simulation, regression analysis, data visualization, and with computer science topics such as machine learning, algorithms, and optimization. We believe this balanced program design will provide students with a holistic understanding of Data Science experience. Students will learn not only the “how” but also the “why” of Data Science application. Students will progress through the courses in a weekly released, asynchronous instruction, delivered through the edX platform, created and supervised by UT Austin faculty and staff, with rigorous assessments, projects, and exams.

At Your Own Pace

Built to provide maximum flexibility, whether you’re a full-time student or a working professional, the online Master’s in Data Science was designed to enable students to further their education on their own terms. Many people will complete the degree within two to three years, but student may take up to six years to complete their program of work.

Rigorous Courses

Online program students will enjoy the same rigorous training and the same credential as our existing top-ten-ranked graduate program. The resulting degrees will be indistinguishable.

Foundational Knowledge

Our program has been designed to offer you a balanced understanding of the field of Data Science, by providing foundational statistical knowledge in areas such as probability, simulation, and regression-based models, and then incorporating that knowledge into the applied processes of data science in areas such as machine learning and optimization. A core guide in our course creation process has been providing students with not only the “how” but also the “why” of Data Science application.

COURSEWORK OVERVIEW

three foundational courses

+

seven additional required courses

=

10 courses

This is a 30 hour program which will consist of a student completing 10 courses. It is the hope of the program that students begin the program with 9 hours (or 3 courses) of foundational coursework. These foundational courses will include:

• Probability and Simulation Based Inference for Data Science
• Foundations of Regression and Predictive Modeling
• Data Structures & Algorithms

To complete the program of work, there are 21 hours ( or 7 courses) of additional required courses. These courses include:

• Advanced Predictive Models for Complex Data
• Design Principles and Causal Inference for Data-Based Decision Making
• Data Exploration, Visualization, and Foundations of Unsupervised Learning
• Principles of Machine Learning
• Deep Learning
• Natural Language Processing
• Optimization

ut austin online masters data science acceptance rate

University of Texas-Austin Application RequirementsEnrolling at the University of Texas-Austin is classified “moderately difficult” by Peterson’s since 18,620 of the 51,033 Fall 2017 applicants were successful for just 36 percent acceptance. Post-grad Longhorns pursuing the M.S.

Talk to an Admission Officer

Studying Computer Science at the University of Texas at Austin is a tremendous opportunity. That means there are many applicants competing for a limited number of admissions spaces. UTCS sees a record number of applicants each year not just from Texas but from out-of-state and foreign applicants.

Applicant numbers to Computer Science have skyrocketed since 2010. Assuming a similar rate of increase, it’s possible that more than 6,500 students attempted to gain admission for about 570 spaces in Fall 2021.

Talk to an Admission Officer

My clients have found success in gaining admission to UTCS including three Turing Scholars. 21 out of 39 CS clients have gained admission (54%) since 2017.

It’s safe to assume that less than 10% of UT Computer Science applicants will gain admission for Fall 2021 first-time freshman applicants and onward. That makes UTCS on par with some of the most selective programs nationwide.

Competitive applicants to Computer Science at a minimum come from the top 10% of their senior class scoring at least 1450 on the SAT. I’ve seen applicants with stronger credentials than this who routinely get denied.

https://youtu.be/h18ZoliYwbQ

COURSES

Advanced Predictive Models CATHERINE CALDER & PURNAMRITA SARKA

Advanced Predictive Models for Complex Data covers random/mixed effects models for multilevel data (clustered data, repeated measures, and longitudinal data) and Gaussian process models for dependent data.  Emphasis is placed on both interpretation of inferences on model parameters and prediction.  The second part of the course introduces nonparametric regression models, including kernel regression, additive models, and random forests.  The course introduces resampling, including the bootstrap, as a tool for quantifying uncertainty in nonparametric regression functions.  A primary goal of the course is for students to be able to select and successfully apply appropriate advanced regression models in applied settings.  The use of statistical software (R) for model fitting, evaluation, and selection is emphasized.

What You Will Learn

• How to identify different types of dependencies in outcome variables
• How to select appropriate advanced regression models in applied settings
• The benefits/limitations of parametric and nonparametric methods
• How to quantify uncertainty in predictions
• How to assess model fit

Syllabus

• Review of the (generalized) linear regression model
• Multilevel data structures and random effects models
• Regression models for dependent outcomes
• Gaussian processes and the spatial (generalized) linear mixed model
• Nonparametric regression models, including kernel regression, additive models, and random forests
• Resampling methods and the Bootstrap
• Evaluating model fit and model selection

Estimated Effort

10-12 Hours/week

Course Availability

• Spring 2022

Visualization CLAU Data Exploration & Visualization CLAUS O. WILKE

In Data Exploration, Visualization, and Foundations of Unsupervised Learning, students will learn how to visualize data sets and how to reason about and communicate with data visualizations. Students will also learn how to assess data quality and providence, how to compile analyses and visualizations into reports, and how to make the reports reproducible. A substantial component of this class will be dedicated to learning how to program in R.

What You Will Learn

• Data visualization
• R programming
• Reproducibility
• Data quality and relevance
• Data ethics and providence
• Dimension reduction
• Clustering

Syllabus

• Introduction, reproducible workflows
• Aesthetic mappings
• Telling a story
• Visualizing amounts
• Coordinate systems and axes
• Visualizing distributions I
• Visualizing distributions II
• Color scales
• Data wrangling 1
• Data wrangling 2
• Visualizing proportions
• Getting to know your data 1: Data providence
• Getting to know your data 2: Data quality and relevance
• Getting things into the right order
• Figure design
• Color spaces, color vision deficiency
• Functions and functional programming
• Visualizing trends
• Working with models
• Visualizing uncertainty
• Dimension reduction 1
• Dimension reduction 2
• Clustering 1
• Clustering 2
• Data ethics
• Visualizing geospatial data
• Redundant coding, text annotations
• Interactive plots
• Over-plotting
• Compound figures

Estimated Effort

10-12 Hours/week

Course Availability

• Spring 2022

Data Structures & AlgorithmsCALVIN LIN

In this course, students will develop their programming skills while learning the fundamentals of data structures and algorithms. Students will hone their programming skills by completing non-trivial programming assignments in Python, and they will be introduced to important programming methodologies and skills, including testing and debugging. Students will learn a variety of data structures, from the basics, such as stacks, queues, and hash tables, to more sophisticated data structures such as balanced trees and graphs. In terms of algorithms, the focus will be on the practical use and analysis of algorithms rather than on proof techniques.

For those who matriculated in Spring 2021 and Fall 2021, Algorithms or Data Structures may be taken to fulfill the Foundational requirements.

Syllabus

• Programming Skills: Testing
• Programming Skills: Debugging
• Programming Skills: Programming Methodology
• Data Structures: Stacks, Queues
• Data Structures: Linked lists
• Data Structures: Hash Tables
• Data Structures: Trees
• Data Structures: Balanced Trees
• Data Structures: Binary Heaps
• Data Structures: Graphs
• Algorithms: Algorithm Analysis
• Algorithms: Searching and Sorting
• Algorithms: Divide and Conquer Algorithms
• Algorithms: Greedy Algorithms
• Algorithms: Dynamic Programming

Course Availability

• Spring 2022

Deep LearningPHILIPP KRÄHENBÜHL

This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games.

Part 1 covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. The class covers both the theory of deep learning, as well as hands-on implementation sessions in pytorch. In the homework assignments, we will develop a vision system for a racing simulator, SuperTuxKart, from scratch.

Part 2 covers a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. In the homework assignments, we develop a vision system and racing agent for a racing simulator, SuperTuxKart, from scratch.

What You Will Learn

• About the inner workings of deep networks and computer vision models
• How to design, train and debug deep networks in pytorch
• How to design and understand sequence
• How to use deep networks to control a simple sensory motor agent

Syllabus

• Background
• First Example
• Deep Networks
• Convolutional Networks
• Making it Work
• Computer Vision
• Sequence Modeling
• Reinforcement Learning
• Special Topics
• Summary

Estimated Effort

10-15 hours/week

Course Availability

• Summer 2021
• Fall 2021
• Spring 2022

Design Principles & Causal InferenceCORWIN ZIGLER

While much of statistics and data sciences is framed around problems of prediction, Design Principles and Causal Inference for Data-Based Decision Making will cover basic concepts of statistical methods for inferring causal relationships from data, with a perspective rooted in a potential-outcomes framework.  Issues such as randomized trials, observational studies, confounding, selection bias, and internal/external validity will be covered in the context of standard and non-typical data structures. The overall goal of the course is to train learners on how to formally frame questions of causal inference, give an overview of basic methodological tools to answer such questions and, importantly, provide a framework for interrogating the causal validity of relationships learned from data.  The target audience for this course is someone with basic statistical skills who seeks training on how to use data to characterize the consequences of well-defined actions or decisions.

What You Will Learn

• How to formalize causality with observed data
• Common threats to causal validity
• Non-typical data structures
• Novel design strategies
• Causal inference

Syllabus

• What is “causal inference”
• Potential outcomes
• Regression
• Standardization
• Matching designs
• Quasi-experimental designs

Estimated Effort

10-12 Hours/week

Course Availability

• Fall 2021

Natural Language ProcessingGREG DURRETT

This course focuses on modern natural language processing using statistical methods and deep learning. Problems addressed include syntactic and semantic analysis of text as well as applications such as sentiment analysis, question answering, and machine translation. Machine learning concepts covered include binary and multiclass classification, sequence tagging, feedforward, recurrent, and self-attentive neural networks, and pre-training / transfer learning.

What You Will Learn

• Linguistics fundamentals: syntax, lexical and distributional semantics, compositional semantics
• Machine learning models for NLP: classifiers, sequence taggers, deep learning models
• Knowledge of how to apply ML techniques to real NLP tasks

Syllabus

• ML fundamentals, linear classification, sentiment analysis (1.5 weeks)
• Neural classification and word embeddings (1 week)
• RNNs, language modeling, and pre-training basics (1 week)
• Tagging with sequence models: Hidden Markov Models and Conditional Random Fields (1 week)
• Syntactic parsing: constituency and dependency parsing, models, and inference (1.5 weeks)
• Language modeling revisited (1 week)
• Question answering and semantics (1.5 weeks)
• Machine translation (1.5 weeks)
• BERT and modern pre-training (1 week)
• Applications: summarization, dialogue, etc. (1-1.5 weeks)

Course Availability

• Summer 2021
• Fall 2021

OptimizationSUJAY SANGHAVI & CONSTANTINE CARAMANIS

This class covers linear programming and convex optimization. These are fundamental conceptual and algorithmic building blocks for applications across science and engineering. Indeed any time a problem can be cast as one of maximizing / minimizing and objective subject to constraints, the next step is to use a method from linear or convex optimization. Covered topics include formulation and geometry of LPs, duality and min-max, primal and dual algorithms for solving LPs, Second-order cone programming (SOCP) and semidefinite programming (SDP), unconstrained convex optimization and its algorithms: gradient descent and the newton method, constrained convex optimization, duality, variants of gradient descent (stochastic, subgradient etc.) and their rates of convergence, momentum methods.

Syllabus

• Convex sets, convex functions, Convex Programs (1 week)
• Linear Programs (LPs), Geometry of LPs, Duality in LPs (1 week)
• Weak duality, Strong duality, Complementary slackness (1 week)
• LP duality: Robust Linear Programming, Two person 0-sum games, Max-flow min-cut (1 week)
• Semidefinite programming, Duality in convex programs, Strong duality (1 week)
• Duality and Sensitivity, KKT Conditions, Convex Duality Examples: Maximum Entropy (1 week)
• Convex Duality: SVMs and the Kernel Trick, Convex conjugates, Gradient descent (1 week)
• Line search, Gradient Descent: Convergence rate and step size, Gradient descent and strong convexity (1 week)
• Frank Wolfe method, Coordinate descent, Subgradients (1 week)
• Subgradient descent, Proximal gradient descent, Newton method (1 week)
• Newton method convergence, Quasi-newton methods, Barrier method (1 week)
• Accelerated Gradient descent, Stochastic gradient descent (SGD), Mini-batch SGD, Variance reduction in SGD (1 week)

Course Availability

• Fall 2021

Principles of Machine LearningADAM KLIVANS & QIANG LIU

This course focuses on core algorithmic and statistical concepts in machine learning.

Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.

Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.

What You Will Learn

• Techniques for supervised learning including classification and regression
• Algorithms for unsupervised learning including feature extraction
• Statistical methods for interpreting models generated by learning algorithms

Syllabus

• Mistake Bounded Learning (1 week)
• Decision Trees; PAC Learning (1 week)
• Cross Validation; VC Dimension; Perceptron (1 week)
• Linear Regression; Gradient Descent (1 week)
• Boosting (.5 week)
• PCA; SVD (1.5 weeks)
• Maximum likelihood estimation (1 week)
• Bayesian inference (1 week)
• K-means and EM (1-1.5 week)
• Multivariate models and graphical models (1-1.5 week)
• Neural networks; generative adversarial networks (GAN) (1-1.5 weeks)

Course Availability

• Summer 2021
• Fall 2021
• Spring 2022

Probability & InferenceMARY PARKER & PETER MÜLLER

Probability and Simulation Based Inference for Data Science is a statistics-based course necessary for developing core skills in data science and for basic understanding of regression-based modeling. Students can look forward to gaining a foundational knowledge of inference through the simulation process.

What You Will Learn

• Definition of probabilities and probability calculus
• Random variables, probability functions and densities
• Useful inequalities
• Sampling distributions of statistics and confidence intervals for parameters
• Hypothesis testing
• Introduction to estimation theory (Properties of estimators, maximum likelihood esti-mation, exponential families)

Syllabus

• Events and probability (1 week)
• Random variables (1 week)
• Moments and inequalities (1 week)
• Continuous random variables (1 week)
• Normal distribution and the central limit theorem (1 week)
• Sampling distributions of statistics and confidence intervals (1.5 weeks)
• Hypothesis testing (2 weeks)
• Introduction to Estimation Theory (1.5 weeks)

Estimated Effort

10 Hours/week

Course Availability

• Fall 2021
• Spring 2022

Regression & Predictive ModelingSTEPHEN WALKER

Foundations of Regression and Predictive Modeling is designed to introduce students to the fundamental concepts behind the relationships between variables, more commonly known as regression modeling. Learners will be exposed not only to a theoretical background of the regression models, but all models will be extensively demonstrated using regression. The emphasis throughout will be on hypothesis testing, model selection, goodness of fit, and prediction.

What You Will Learn

• Learn the key ideas behind regression models.
• Apply the ideas and analysis to various types of regression model.
• Understand and interpret the output from an analysis.
• Understand key procedures such as hypothesis testing, prediction, and Bayesian methods.

Syllabus

• Foundations and Ideas, Simple Linear Model, Correlation; Estimation; Testing.
• Multiple Linear Regression, Vector and matrix notation; Colinearity; Ridge regression.
• Bayes Linear Model; Conjugate model; Prior to posterior analysis; Bayes factor.
• Variable Selection, LASSO, Principal component analysis; Bayesian methods.
• ANOVA Models, One-way ANOVA; Two-way ANOVA; ANOVA Table; F-tests.
• Moderation & Interaction, Testing for interaction; Sobel test.
• Nonlinear Regression, Iterative estimation algorithms; Bootstrap.
• Poisson regression, Analysis of count data, Weighted linear model.
• Generalized Linear Model, Exponential family; GLM theory; Logistic regression.
• Nonparametric Regression, Kernel smoothing; Splines; Regression trees.
• Mixed Effects Model, Fixed and random effects; EM algorithm; Gibbs sampler.
• Multiclass Regression, Classification tree; Multinomial logistic regression.

Estimated Effort

8-12 Hours/Week

Course Availability

• Summer 2021
• Spring 2022

ENROLLMENT OPTIONS

Courses are offered by semester and follow The University of Texas at Austin academic calendar. Students may begin courses in the semester they applied for admissions (either the fall or spring semester). Students are required to be enrolled in the long semester, fall and spring semesters, whereas the summer semester is optional.

Students may enroll in the MSDS program on a part-time or full-time basis. For working professionals, we recommend taking one to two courses per semester.

Students are allowed a maximum of six years to complete the MSDS degree.