The Georgia Tech Online Master of Science in Analytics (OMS Analytics) is a multidisciplinary degree in collaboration with Georgia Tech’s College of Engineering, College of Computing, and Scheller College of Business.
The top 10-ranked master’s program challenges students with the same curriculum and rigor as its on-campus Analytics counterpart, all with tuition for under $10,000 USD.
This fully online program enables students to take a deep dive into analytics and choose from 3 specialized tracks.
Computation Data Analytics
Designed for your schedule, this online master’s program is for students seeking greater flexibility and can be completed part-time in two to three years.
OMS Analytics equips you with the insight and multidisciplinary skills needed to succeed in today’s analytics world while offering you the prestige, affordability, flexibility you want in a master’s degree.
Gain a credential that commands attention with the Georgia Tech Online Master of Science in Analytics.
The Online Master of Science Analytics degree requires 36 hours of coursework. First, 15 hours of core coursework on big data analytics, visual analytics, computing statistics, and operational research essentials. An additional 15 hours of electives allow students to choose an area of specialization in one of three tracks.
Full curriculum breakdown:
Introductory core – 9 hours
Advanced core – 6 hours
Statistics elective – 6 hours
Operations elective – 3 hours
Track electives – 6 hours
Practicum – 6 hours
Students will have the flexibility to focus on a specific area of interest by selecting to concentrate on one of three tracks. Tracks include:
Computational Data Analytics
Like on-campus students, online learners will complete a 6 credit hour applied analytics practicum with an outside company.
Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.
In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.
You will learn how to use statistical models and machine learning as well as models for:
Today, businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses.
This course prepares students to understand business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to business problems. The use of examples places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques.
The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.
The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.
In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.
The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.
Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data.
This course will introduce students to the field by covering state-of-the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.
Why Take This Course?
You should take this course if you want to cover the state of the art in data modeling and visualization techniques using the R programming language.
Regression Analysis is the most common statistical modeling approach used in data analysis and it is the basis for more advanced statistical and machine learning modeling.
In this course, you will be given fundamental grounding in the use of widely used tools in regression analysis. You will learn the basics of regression analysis such as linear regression, logistic regression, Poisson regression, generalized linear regression and model selection.
Throughout this course, you will be exposed to not only fundamental concepts of regression analysis but also many data examples using the R statistical software. Thus by the end of this course, you will also be familiar with the implementation of regression models using the R statistical software along with interpretation for the results derived from such implementations.
This course is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical.
In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:
statistical supervised and unsupervised learning methods
randomized search algorithms
Bayesian learning methods
The course also covers theoretical concepts such as inductive bias, the PAC and Mistake‐bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.
By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.
This course blends optimization theory and computation and its teachings can be applied to modern data analytics, economics, and engineering. Organized across four modules, it takes learners through basic concepts, models, and algorithms in linear optimization, convex optimization, and integer optimization.
The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques with many applications, basic polyhedral theory, simplex method, and duality theory. The third module is on convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module focuses on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables.
Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, among many other fields of practice. This course will illustrate time series analysis using many applications from these fields.
In this course, students will learn standard time series analysis topics such as modeling time series using regression analysis, univariate ARMA/ARIMA modelling, (G)ARCH modeling, Vector Autoregressive (VAR) model along with forecasting, model identification and diagnostics. Students will be given fundamental grounding in the use of such widely used tools in modeling time series.
Throughout this course, students will be exposed to not only fundamental concepts of time series analysis but also many data examples using the R statistical software. Thus by the end of this course, students will also be familiar with the implementation of time series models using the R statistical software along with interpretation for the results derived from such implementations.
This class is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.