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Data Science Specialist Track

Utilize your math, statistics, and computational aptitude skills to mine large data sets and streams.  Learn how to use industry specific algorithms, machine learning, even deep learning to help industry professionals uncover new market opportunities, greater efficiencies, optimal performance, and so much more.

All students in the MS Applied Data Science/Specialist Track are required to complete 5 common core courses, 4 track-specific courses, and a choice of 3 elective courses.

The four foundational courses in this track takes a deeper dive into theory (than the Generalist track) and requires an additional math background.

common core COURSES (REQUIRED): ADS521, ADS534, ADS637, ADS638, ads670
Elective Courses (Choose 3): ADS650, ADS652, ADS654, (Or any other graduate level courses offered at bay path university as approved by the applied data science program director)

Is this the right track for you? Check out the Generalist Track instead.

Curriculum & Schedules

Code Course Name Credit Hours
ADS521 Foundations of Data Science 3

Serving as an introduction course to the ADS program, ADS521 examines the history of data science, its status as a scientific and applied discipline in a modern-day world, and surveys all the important topics covered in the courses in the program and many of their applications to everyday life. Part of the course will also serve as review of mathematics and basic programming knowledge.

ADS532 Probability for Data Analytics 3

This course is an in-depth introduction of probability and some foundational concepts in statistics. Topics include key concepts in probability, conditional probability, random variables, common distributions, expected values, variance, covariance, limit theorems, sampling and estimation of parameters. The course will use R or similar programming language.

ADS533 Statistical Inference for Data Analytics 3

Continuing from ADS532, ADS533 is an in-depth introduction of making inference using statistics. Topics include point and interval estimation of parameters and hypothesis testing. Methods including likelihood, frequentist, Bayesian, resampling methods such as Bootstrap and permutation testing are covered, and properties of estimators such as bias, consistency, efficiency and sufficiency are considered. An introduction to categorical data analysis and ANOVA is also included. The course will use R or similar programming language.

ADS534 Statistical Modeling 3

This application-focused course focuses on regression analysis including linear, multiple linear and logistic regression models, with detailed discussions of model formulation, model inference, and model interpretation. Programming languages such as a SAS will be utilized.

ADS635 Data Mining I 3

Statistical analysis and data mining has been recognized as one of the “Hottest Skills” on LinkedIn year after year. The growing complexity and size of data has given rise to unprecedented demands and challenges for the field. Moreover, the mastery of various methods, the selection and application of appropriate techniques is equally important as the effective presentation and interpretation of findings. The objective of ADS635 and ADS636 is to modernize student training to better suit these demands.

Topics for ADS635 will focus on supervised learning, including feature selection, discriminant analysis, regularization methods, ensemble methods, support vector machines and model assessment, all with hands-on application utilizing statistical packages and programming languages.

ADS636 Data Mining II 3

This course will cover additional topics in data mining with a focus on unsupervised learning. Topics include association rules, clustering methods, self-organizing maps, recommender systems, ensemble methods, dimension reduction and probabilistic graphical models. Special emphasis will be on data with high-dimensionality or massive sample size. Concepts will be reinforced with hands-on applications that utilize statistical packages and programming languages.

ADS637 Data Exploration and Visualization 3

This course is an introduction to data visualization. It includes data preprocessing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics covered include design principles, perception, color, statistical graphs, maps, trees and networks, data visualization tools, and other topics as appropriate. Visualization tools may include Tableau, Python, and R, etc. The course introduces the techniques necessary to successfully implement
visualization projects using the programming languages studied.

ADS638 Database Systems 3

It is increasingly important for data scientists to understand various database models and their associated data access methods. This course covers both the fundamental concepts of database systems and associated tools. Topics include conceptual data modeling, database design and normalization, database implementation and the use of SQL for data definition, manipulation, and query processing. The course also includes a survey of techniques for handling non-relational data models, massive datasets, and unstructured data, including data warehousing, in-memory databases, NewSQL, NoSQL, and Hadoop.

ADS650 Time Series Analysis 3

Essential to the analysis of economic and financial data, time series analysis has wide applications and can be applied to any data that has been observed over time. This course introduces both the theory and practice of time series analysis, covering classical topics including stationarity, autocorrelation functions, autoregressive moving average models, partial autocorrelation functions, forecasting, seasonal ARIMA models, power spectra, parametric spectral estimation and nonparametric spectral estimation. The analysis of real-life data and hands-on practice will be emphasized throughout the course.

ADS652 Text Mining 3

Mining high-quality information from text has become critical to many industries. Starting from basic natural language processing techniques and document representation, to text categorization and clustering, sentiment analysis and text-based prediction, this course serves as a comprehensive introduction to the topic. Relevant tool-kits will be utilized and case studies from various industries will be examined.

ADS654 Deep Learning 3

Many recent breakthroughs in artificial intelligence have been made possible by deep learning, a branch of machine learning concerned with the development and application of modern neural networks. This is an advanced course that builds upon the knowledge of probability, statistics, linear algebra, optimization and basic neural networks. Topics include convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains such as speech recognition and computer vision.

ADS670 Case Analysis Capstone 3

This is a project-oriented course at the end of the program. Students will demonstrate their competence in the theory and practice learned from the program through the whole process of a complex data analysis project, including data collection, exploration, preparation, analysis, interpretation and presentation. The project can be either relevant to students’ experience or aspired filed, accompanied by a final essay in which students reflect upon the goals of the program and their personal goals, demonstrate how they met these goals, and what work supports their arguments.