Online Master of Science in Analytics (OMS Analytics)

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Why Take This Course?

This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.

Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. In this course, we introduce the characteristics and related analytic challenges on dealing with clinical data from electronic health records. Many of those insights come from medical informatics community and data mining/machine learning community. There are three thrusts in this course: Application, Algorithm and System

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:

• classification;
• clustering;
• change detection;
• data smoothing;
• validation;
• prediction;
• optimization;
• experimentation;
• decision making.

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.

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
• reinforcement learning

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 is a three-credit course.

Simulation is a top technology used in engineering and science. This self-contained course will cover everything you need to know to hit the ground running on real-world simulation projects.

Simulation can be used to model everything from call center operations, manufacturing centers, traffic flow, physical phenomena (such as the weather), and even the propagation of a disease through a population.

In this course, we’ll emphasize general modeling skills, powerful simulation programming tools, and the mathematics and statistical analysis techniques needed to ensure that your results are meaningful and rigorous.

Our instructor has over 35 years of experience in teaching simulation and you will get extensive hands-on experience with state-of-the-art simulation analysis tools.

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.

This course takes you through the first eight lessons of CS6750: Human-Computer Interaction as taught in the Georgia Tech Online Master of Science in Computer Science program.

In this course, you’ll take the first steps toward being a solid HCI practitioner and researcher. You’ll learn the fundamentals of how HCI relates to fields like user experience design, user interface design, human factors engineering, and psychology. You’ll also learn how human-computer interaction has influence across application domains like healthcare and education; technology development like virtual and augmented reality; and broader ideas like context-sensitive computing and information visualization.

You’ll then dive into the fundamentals of human-computer interaction. You’ll learn three views of the user’s role in interface design: the behaviorist ‘processor’ view, the cognitivist ‘predictor’ view, and the situationist ‘participant’ view. You’ll discover how these different views of the user’s role affect the scope we use to evaluate interaction. These perspectives will be crucial as you move forward in designing interfaces to ensure you’re considering what goes on inside the user’s head, as well as in the environment around them.

You’ll then learn the gulfs of execution and evaluation, which determine how easily the user can accomplish their goals in a system and how well they can understand the results of their actions. All of user interface design can be seen as taking steps to bridge these gulfs. You’ll also investigate the notion of direct manipulation, which shortens the distance between the user and the objects they are manipulating in the interface. With these tools, you’ll be well-equipped to start designing effective interfaces.

You’ll then take a deeper dive into what humans are even capable of accomplishing. You’ll learn the limitations of human sensing and memory and how we must be aware of the cognitive load we introduce on the user while using our interfaces. Cognitive load can have an enormous impact on a user’s satisfaction with an interface, and must be kept in mind as you begin your career as a designer.

You’ll finally conclude with an overview of the major design principles in human-computer interaction. Curated from the work of Don Norman, Jakob Nielsen, Ronald Mace, Larry Constantine, and Lucy Lockwood, these design principles cover revolutionary ideas in the design of interfaces: discoverability, affordances, perceptibility, constraints, error tolerance, and more. These principles are crucial whether you move forward as a designer, an evaluator, a front-end engineer, or any other role in technology design.

By the end of the course, you’ll have an understanding of where HCI sits in the broader field, a grasp of the goals of HCI, and a foundation in core principles that inform interface design.

This course takes you through lessons 9 through 13 of CS6750: Human-Computer Interaction as taught in the Georgia Tech Online Master of Science in Computer Science program.

In this course, you’ll expand the scope through which you view human-computer interaction. You’ll start by going further inside the user’s mind to understand the role of mental models in guiding a user’s interaction with your system. A good user interface designer understands the mental models of their users and how representations can be used to correct those mental models.

You’ll then learn methods for breaking down user behavior into more objective, discernible, and measurable chunks. Through the principles of task analysis and with artifacts like GOMS models, you’ll discover how to take the often-ethereal patterns of human interaction and distill them into externalizable, manipulable chunks. You’ll also learn how to use these artifacts to inform the design and improvement of interfaces.

You’ll then widen your view to look at the context in which your interfaces are deployed. You’ll begin by learning about distributed cognition, which includes the notion that humans may offload cognitive tasks onto interfaces, and that humans and interfaces together may be considered higher-level cognitive systems. You’ll also learn about theories for investigating interaction in context, such as activity theory and situated action, and the role that human improvisation plays in any interface we design. Through these lenses, you’ll be equipped to design not just user interfaces, but user experiences developed with an understanding of the context around the interaction.

You’ll conclude by expanding your view even further to investigate how interfaces interact with society itself: both how society guides the interfaces we create, and how the interfaces we create affect society. You’ll learn how interface design can be used to address societal issues, but also how it can have danger unintentional side effects.

By the end of the course, you’ll have a deeper understanding of how human cognition interacts with user interfaces, and how user interfaces in turn interact with the world. You’ll be able to design interfaces that consider what the user knows and what is going on around the user.

This course takes you through lessons 14 through 18 of CS6750: Human-Computer Interaction as taught in the Georgia Tech Online Master of Science in Computer Science program.

In this course, you’ll begin by learning the design life cycle. This is the process by which we investigate user needs, brainstorm potential designs, create prototypes, and evaluate those prototypes. This life cycle provides the structure for the third and fourth courses in this professional certificate.

A key part of the design life cycle, however, is human subjects research. In interface design, this involves asking users for information about what they do and what they need, and then asking them for feedback on the prototypes that you develop. In HCI more broadly, this may involve testing different ideas with users to see what facilitates the best user experience. Whenever we interact with users, though, we need to keep in mind users’ rights to privacy and transparency, and so we begin this course with a discussion of ethics in HCI. This is grounded in the university Institutional Review Board process, but also investigates the role of ethics in HCI in industry as well.

From there, you’ll move on to needfinding and requirements gathering. It is always tempting to jump straight into designing an interface based on our intuitive understanding of a task or need, but successful interface design always starts with an understanding of the users: who are they, what they do, and what they need. This involves both interacting directly with them via surveys and interviews, as well as observing them at a distance or even attempting the tasks ourselves. This concludes with an understanding of the requirements of any interface we create.

From there, you’ll move on to brainstorming design alternatives. Again, it is often tempting to jump straight to the design we have in mind, but successful interface design starts with the results of needfinding and attempts a more grounded investigation of possible solutions. Through this lesson, you’ll learn techniques for managing effective brainstorming sessions and approaches to exploring the ideas that are created including artifacts like user personas, interaction timelines, and storyboards.

Finally, you’ll conclude by learning about prototyping. Implementing an interface is a complicated process, and there is a risk that we may invest lots of time into an interface that is doomed to fail because we do not get user feedback on the idea. The goal of prototyping is to get an idea in front of users as quickly as possible to validate and improve it before we move on to the high pressures of implementation.

By the end of this course, you’ll have an understanding of the design life cycle and its first three major stages: needfinding, brainstorming, and prototyping. You’ll also understand the ethical implications of HCI research and how to safeguard users’ rights.