Eastern University Data Science Curriculum

Are you a data science student of Eastern University or a prospective student interested in knowing the school data science curriculum? If so, then we have published the Eastern University data science curriculum for BS and Master’s together with the course descriptions. Below is the curriculum for Eastern University data science.

BS in Data Science Curriculum

Course Number Course Name Credit
MATH 160 Calculus I 3
MATH 221 Statistics for Data Analysis 3
CSCI 175 Introduction to Computer Science 3
CSCI 200 Introduction to Programming using C++ 3
CSCI 201 Data Structures 3
CSCI 405 Artificial intelligence 3
DTSC 220 Introduction to data science 3
DTSC 250 Statistics in R 3
DTSC 320 Data management 3
DTSC 400 Applied data science 3
DTSC 420 Ethical and philosophical issues in computer and data science 3
Electives 9

MS in Data Science Curriculum and Course Descriptions


Course Number Course Title Credits
Required Courses
DTSC 650 Data Analytics in R 3
DTSC 660 Data and Database Management with SQL 3
DTSC 670 Foundations of Machine Learning Models 3
DTSC 690 Data Science Capstone: Ethical and Philosophical Issues in Data Science 3
DTSC 520 Fundamentals of Data Science 3
DTSC 550 Introduction to Statistical Modeling 3
DTSC 560 Data Science for Business 3
DTSC 575 Principles of Python Programming 3
DTSC 580 Data Manipulation 3
DTSC 600 Information Visualization 3
DTSC 680 Applied Machine Learning 3
DTSC 691 Data Science Capstone: Applied Data Science 3

Course Descriptions

DTSC 520: Fundamentals of Data Science (3 credits)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 (3 credits): 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 560: Data Science for Business (3 credits): This course explores the various ways data and science can be applied to business contexts. Particular emphasis will be placed on decision using data to make informed business decisions.

DTSC 580: Data Manipulation (3 credits): 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 575: Principles of Python Programming (3 credits): 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.

DTSC 600: Information Visualization (3 credits): 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.

DTSC 650: Data Analytics in R (3 credits): 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.

DTSC 670: Foundations of Machine Learning Models (3 credits): 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.

DTSC 660: Data and Database Management with SQL (3 credits): 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 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 670: Foundations of Machine Learning Models.

DTSC 680: Applied Machine Learning (3 credits): 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 691: Data Science Capstone: Applied Data Science (3 credits): 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 670: Foundations of Machine Learning Models.

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