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Eastern University: Master's Degree in Data Science

DTSC 520: Fundamentals of Data Science 

Introduction to foundational concepts, technologies, and theories of data and data science.  This includes methods in data acquisition, cleaning, and visualization.  Taught in Python using NumPy, Pandas, Matplotlib, and Seaborn. Includes an introduction to Python, IPython, and Jupyter Notebooks.  

DTSC 550: Introduction to Statistical Modeling 

Introduction to foundational concepts, theories, and techniques of statistical analysis for data science.  Students will begin with descriptive statistics and probability, and advance through multiple and logistic regression.  Students will also conduct analyses in R.  This course is approachable for students with little statistical background and prepares them for DTSC 650: Data Analytics in R

DTSC 575: Principles of Python Programming 

This course will teach students the introductory skills of programming, problem solving and algorithmic thinking in Python. Topics include variables, input/output, conditional statements/logic, Boolean expressions, flow control, loops and functions.  Approachable for students who have no experience with Python. Prerequisite: DTSC 520: Fundamentals of Data Science

DTSC 600: Information Visualization

This course is designed to teach students the best practices in Data Visualization, the key trends in the industry and how to become great storytellers with data. Students taking this class will learn the importance of using actionable dashboards that enable their organizations to make data-driven decisions. For this class students will be exposed to the main two software in the industry: Qlik and Tableau. Prerequisite: DTSC 550: Introduction to Statistical Modeling

DTSC 650: Data Analytics in R

Continuation of DTSC 550, with an emphasis on statistical techniques most used in modern data science.  Will explore in greater depth linear and logistic regression, and continue to additional regression and classification techniques with a focus on application.  Analyses will be completed in R. Prerequisite: DTSC 550: Introduction to Statistical Modeling

DTSC 670: Foundations of Machine Learning Models

Introduction to machine learning landscape.  Will address questions such as what is machine learning?  Why do we use machine learning?  What is machine learning appropriate for?  What is it inappropriate for?  Will explore supervised and unsupervised learning, such as k-nearest neighbors, support vector machines, decision trees, and principal component analysis. Taught in Python. Prerequisite: DTSC 650: Data Analytics in R

DTSC 660: Data and Database Management with SQL

This course considers the ways data can be organized, cleaned and managed within and between disparate data sets. It also covers database design and the use of databases in data science applications with an emphasis on SQL.  Additional topics include version control and Git. 

DTSC 680: Applied Machine Learning  

Continuation of DTSC 670. Further exploration of modern machine learning applications.  Topics include neural networks and deep learning, including an emphasis on model selection and tuning. Taught in Python. Prerequisite: DTSC 670: Foundations of Machine Learning

DTSC-560 Data Science for Business

Businesses have come to increasingly rely on data in all aspects of operation. This course explores the various ways data science skills can be applied to business scenarios. Topics include how to identify business decision problems and formulate research questions, how to use analytical techniques in spreadsheets and R to address these issues, and how these tools can inform decision making.  

DTSC-580 Data Manipulation

Students will use Python to obtain, store, and clean data. Topics include connecting to databases, web scraping, time series data, and general data cleaning and preparation. This course assumes prior knowledge of Python, NumPy, and Pandas.

DTSC 690: Data Science Capstone 

Ethical and Philosophical Issues in Data Science (3 credits): Part one of the capstone in the Masters in Data Science.  Students will explore contemporary ethical and philosophical issues in data science and artificial intelligence.  Subjects include basic and advanced issues, ranging from social media privacy to implications of machine learning and artificial intelligence for religiousness.  Can be taken jointly with DTSC 691. Prerequisite: DTSC 680: Machine Learning II

DTSC 691: Data Science Capstone: Applied Data Science 

Part two of the capstone in the Masters in Data Science.  Students will also complete a capstone project integrating their learning across courses.  Students will complete a project proposal, including their data source, acquisition, cleaning, analysis, and presentation intentions.  Can be taken jointly with DTSC 690. Prerequisite: DTSC 680: Machine Learning II 

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