Core Programming and Data Skills
Programming languages form the tools through which data scientists and other data-driven professionals build models and transform raw data into impactful insights. To that end, several programming languages are heavily utilized within the ADS program — often applied alongside statistical packages. Applied experiences ensure that future data science leaders understand not only how programming languages are constructed but also how they function within problem-solving scenarios.
R and Python for Data Science
Flexible and production-ready, Python is uniquely well-suited to contemporary data science due to its vast data library ecosystems and strong support for artificial intelligence and machine learning applications. Other perks include robust integrations and excellent end-to-end workflows.
R is also appealing, largely due to its impressive statistical modeling capabilities. Offering a rich technical ecosystem that includes a wide range of purpose-driven packages, R promises exceptional data visualization capabilities and community support.
Several Bay Path alumni speak to the degree’s strong grounding in programming languages such as R and Python for data science, explaining that exploring and applying these languages has helped them gain the confidence needed to tackle advanced analytical tasks.
Database Management and Structured Query Language (SQL)
Today's data scientists leverage massive volumes of information, which is far more accessible when organized and stored within well-designed database systems. Effective database management allows for the efficient storage and retrieval of critical information.
SQL and database management dominate this effort. As a standardized programming language designed to support the creation and manipulation of structured information (known as relational data), SQL allows data scientists to query and transform datasets with precision.
At Bay Path, a targeted course involving database systems reveals how SQL influences data definition, manipulation, and query processing. Bay Path alumni frequently reference SQL expertise as a core advantage of the ADS program. For example, graduate Kristen Rezac referenced this (along with R and Python) as "basic tool knowledge" needed to build confidence and facilitate career advancement.
Data Visualization Tools
Data visualization allows data scientists to bring complex insights to life, using data to create compelling stories that feel accessible to non-technical professionals. The ADS program offers a well-rounded introduction to data visualization and exploration, using industry-recognized tools — such as Tableau and cloud-based AWS Quicksight — to help students create intuitive and impactful visuals. Data visualization courses reveal how to make statistical graphs, maps, and other visuals more compelling and, in turn, more impactful.
Statistical Modeling and Machine Learning
One of the core learning outcomes of the MS in Applied Data Science involves applying mathematical principles to data analysis. This is where statistical modeling comes into play. At the heart of data science, statistical modeling involves mathematical representations, embodying statistical assumptions that surround the creation of sample data.
Because statistical modeling plays such a central role in data science, it forms the basis for a foundational course that prioritizes the following topics:
Regression, Classification, and Predictive Modeling
As a statistical method focused on the relationships between variables, regression analysis is a central topic within the ADS application-focused statistical modeling course. This class emphasizes processes or stages such as model formulation (determining the appropriate variables and assumptions), model inference (evaluating associations between predictors and outcomes), and interpretation (using results to uncover real-world insights).
The ADS also explores statistical classification, revealing how predefined categories are assigned based on feature values. Coursework demonstrates how classification models enable the efficient organization of large datasets. These concepts — along with broad-based statistical modeling insights and techniques — offer a strong mathematical foundation to support a deeper understanding of predictive models: how they're created, validated, and applied.
Unsupervised Learning and Clustering
While many ADS courses and learning experiences center on supervised learning (involving the use of labeled datasets), there is a growing need for expertise in unsupervised learning (driven by unlabeled datasets).
Core machine learning courses detail unsupervised learning algorithms such as k-means, which place unlabeled data into groups known as clusters. Data scientists use these techniques to uncover hidden patterns, with applied AI courses revealing not only which techniques are the most effective but also how these can be applied to make sense of the vast datasets produced across diverse industries.
Evaluation and Validation Methods
Evaluation determines whether various models are suitable for desired tasks, highlighting respective strengths and weaknesses to avoid common issues such as underfitting (failing to recognize important patterns due to excessively simple models) or overfitting (learning too much from training data, such as noise).
The ADS program draws attention to validation techniques that detect these issues, using project-based scenarios to actively practice evaluating models. These experiences prioritize the justification of model selection based on accuracy and interpretability.
Generative AI and Emerging Technologies
An applied data science degree offers the chance to explore emerging technologies, applying foundational statistical methods and concepts to today's most innovative models. Innovation-oriented coursework demonstrates how core analytical principles influence advanced architectures and even large language models.
Introduction to Generative AI
Generative AI represents the future of intelligent computing. Moving forward, we can expect these already powerful systems to become even more adaptive — and even more integrated into systems and workflows that span industries.
ADS coursework promises a strong statistical and computational foundation so that future data science leaders can truly understand the advanced principles and methods that underscore the most cutting-edge AI solutions. One of the program's most innovation-oriented courses explores generative AI in data science, revealing the impact of fundamental methods while offering hands-on opportunities to build generative solutions. These applied experiences involve large language models (LLMs), along with image generators and creative systems.
Applied Use Cases
Capstone experiences provide the chance to apply statistical models and programming languages to real-world scenarios, revealing how generative AI and other innovative solutions can help address complex challenges across many fields.
Through capstones, students learn to work with actual datasets, using practical workflows and models to echo the realities of data-driven initiatives or environments. These experiences inspire confidence, allowing for a smoother transition into advanced roles in which professionals are expected to leverage or design AI-enhanced solutions.
Ethical and Responsible AI
Ethical AI reveals how we can continue using AI to drive innovation while safeguarding the communities and industries impacted by advanced technologies. Transparency and fairness must be prioritized, especially as powerful AI-driven solutions can exacerbate existing biases or introduce new inequities.
Bay Path's ADS master’s program encourages students to explore ethical concerns and reflect on the responsible use of AI. Ethical topics are built into multiple areas of the ADS curriculum, including Bay Path's generative AI-focused course. Meanwhile, project-based experiences encourage future data science visionaries to actively build ethical safeguards into models or workflows. The goal is to empower tomorrow's tech leaders to promote innovation without compromising integrity.
Practical and Applied Learning Opportunities
Practical learning is embedded into every aspect of the online MS in Applied Data Science, as the program name implies. Aspen Gulley explains that, as an ADS student, she was able to select projects that aligned with her unique interests. She specifies that these hands-on learning opportunities allowed her to gain "practical experience applying a diverse set of data science skills" and led to the development of a rich and diverse professional portfolio.
Capstone Projects and Real-World Data Challenges
We've discussed the value of capstone learning when exploring generative AI, but this represents just one example of project-based learning within the ADS program. Many other thought-provoking learning experiences are offered, including projects tied to areas such as database management or visualization.
The ADS program culminates in a final case analysis capstone that integrates a variety of topics and techniques explored in other graduate-level classes. This experience synthesizes previously discussed topics such as data collection and exploration, all while bringing an applied approach to preparation, analysis, interpretation, and presentation.
Personal reflection is encouraged, with a final essay providing the chance to showcase achieved learning objectives and set the stage for future data science breakthroughs.
Industry-Relevant Tools and Platforms
Throughout the ADS program, students are introduced to industry-recognized tools and platforms that support everything from data management to visualization. The objective is to expand each student's data science toolbox so that, as Professor Liu explains, future innovators enjoy the "freedom to select."
We've discussed the use of Tableau, but this is only the beginning. The ADS also offers the chance to use industry-aligned tools such as SAS (a comprehensive suite centered around statistical modeling capabilities) and Amazon Quick Suite (a cloud-based business intelligence and agentic AI application).
Preparing for the Job Market
Beyond building technical skills, the ADS program is designed to help students translate their work into job-ready qualifications. By completing applied projects across multiple tools and problem types, students graduate with tangible portfolio pieces that demonstrate real-world capabilities — including data cleaning, analysis, modeling, and visualization. Just as importantly, these experiences help students practice explaining findings clearly, which is a critical skill for interviews and workplace collaboration.
Explore Bay Path’s Online MS in Applied Data Science
Unlock your future in data science with a graduate program that promises technical depth and practical application. Pursue Bay Path's Master of Science in Applied Data Science (ADS) and develop in-demand technical skills that help you drive impact in a data-driven world. Check out Bay Path's ADS information session for more details about our applied data science degree, request personalized information today, or explore our other compelling graduate degree programs.
