Electives
DATA 599 Topics: Cybersecurity (4)
Offering: Cybersecurity can be understood as a mindset or approach rather than a subfield of computer science, such as secure mobile computing, network and operating system security, secure data bases, and secure cryptography algorithms. This course prepares a general audience to incorporate security concepts and ethics into their daily lives and offers some basic familiarity with writing security oriented code.
DATA 599 Topics: Data Science in the Natural Sciences (4)
Offering: As “big data” and the role of data scientist enters into mainstream life, the way science is done is also evolving. Natural scientists are frequently turning to trained data scientists to collaborate and assist with data reduction, data storage, and machine-learning. This course is intended to help bridge the gap across disciplines: to instruct data scientists in the analyses, formats, requirements, and approaches commonly used in the natural sciences. Particular emphasis will be placed on understanding what sorts of information scientists are interested in, and in facilitating communication between data scientists and natural scientists. Additionally, as science can frequently use a slightly different technology stack than commercial or industrial groups, some time will be devoted to learning how to navigate a Linux environment and work with a variety of open-source frameworks.
DATA 599 Topics: Survival Analysis (4)
Offering: Survival analysis methods consist of statistical modeling techniques that predict the time to an event. The event of interest often depends on the application area. Survival analysis techniques have been used in a wide variety of areas—medical professionals working to predict the onset of a disease; HR representatives studying trends in workforce attrition; mechanical engineers investigating reliability of post-released products; and many other applications. In this course, students will be exposed to practice survival analysis techniques through exploration of real data sets. Students can expect to develop a deeper understanding of concepts including: observation censoring, accelerated life testing and experimental design, recurrence analysis, and implementation of survival analysis techniques in statistical software as well as communicating statistical results to different audiences.
- Prerequisite: Integral calculus
- Not explicitly required, but helpful: Knowledge of multiple linear regression
DATA 599 Topics: Advanced Machine Learning
Offering: The advanced machine learning course is designed for students with a practical data science background in machine learning who seek to deepen their understanding and proficiency in advanced concepts and techniques. The course surveys cutting-edge topics and applications, including the following: Ensemble learning, neural network and deep learning, reinforcement learning, and an overview of AutoML and MLops. Students will gain a deep understanding of the machine learning lifecycle, from data preparation and model development to deployment, monitoring and maintenance. Practical hands-on projects and case studies will provide students with experience tackling real life problems.
At the end of the course, students are expected to demonstrate proficiency in implementing and optimizing advanced machine learning models, applying cutting-edge concepts to solve real-world problems and exploring their applications in various domains. Design, develop and evaluate complex machine learning models with an emphasis on interoperability, ethical consideration and practical feasibility. Students will have the opportunity to contribute to the course and to develop team final projects. Proficiency in Python programming, practical experience with scikit-learn and foundations in machine learning concepts and algorithms are required.
Prerequisite: DATA 505
DATA 599 Topics: Cloud Computing
Offering: This course introduces cloud computing as the solution to the problem of data-intensive programming at scale. We will survey the existing techniques in computing, their algorithmic basis, and explore them in practice. With the motivating example of the MapReduce programming model, will examine Microsoft Azure, Google Cloud, Amazon Web Services, three common commercial platforms for cloud computing, and Hadoop, an open-source alternative. The course will be supplemented with discussions of virtualization and cloud security. Experience coding in Python is recommended.
DATA 599 Topics: Power BI Foundations
Offering: Power BI professionals should be comfortable assessing data quality, understanding data security, and comprehending data sensitivity. We will delve into these core topics, building student confidence in foundational modeling, report building, and extracting actionable insights from data in Power BI. As we navigate through foundational learning and hands-on experience, we will refer to domain areas covered in the PL-300 - Microsoft Power BI Data Analyst certification.
DATA 599 Topics: Data Science Consulting: Technical Sales Strategies
Offering: This course prepares students with technical backgrounds for sales roles in software and services, focusing on a consultative selling approach. The curriculum covers sales cycle basics, CRM and marketing automation tools, solution architecture, and advanced strategies like requirement analysis, proof of concept development, negotiation, and Challenger Sale tactics. Students will emerge with a strong foundation in sales strategies that emphasize empathy, identifying client needs, and solution-based selling. This course is ideal for newcomers considering a career in sales while enhancing their skills in the competitive tech market. Nothing happens in the technology industry until it’s sold!
(Cross-listed with Atkinson MBA)