Program Overview

Data Science Program

The Data Science program offers a comprehensive curriculum that covers the core skills needed for gathering, cleaning, organizing, analyzing, interpreting, and visualizing data.

Course Descriptions

• DSCI403: Introduction to Data Science: This course teaches students the core skills needed for data science, including basic SQL, Python programming, and statistical and machine learning toolkits. Prerequisite: CSCI128 with a grade of C- or higher, MATH201 or MATH334.
• DSCI470: Introduction to Machine Learning: This introductory course studies the theoretical properties of machine learning algorithms and their practical applications. Students will experiment with machine learning techniques and apply them to a selected problem. Prerequisite: CSCI101 or CSCI102 or CSCI261 or CSCI200; MATH201, MATH332.
• DSCI503: Advanced Data Science: This course teaches students the core skills needed for gathering, cleaning, organizing, analyzing, interpreting, and visualizing data. Students will propose and design a semester project using a dataset from their domain of interest. Prerequisite: CSCI200 with a grade of C- or higher or CSCI262 with a grade of C- or higher, MATH201 or MATH334 OR Graduate level standing and at least CSCI128 or equivalent.
• DSCI530: Statistical Methods I: Introduction to probability, random variables, and discrete and continuous probability models. Prerequisite: MATH334 or equivalent.
• DSCI560: Introduction to Key Statistical Learning Methods I: Part one of a two-course series introducing statistical learning methods with a focus on conceptual understanding and practical applications. Prerequisite: DSCI530 or MATH530.
• DSCI561: Introduction to Key Statistical Learning Methods II: Part two of a two-course series introducing statistical learning methods with a focus on conceptual understanding and practical applications. Prerequisite: DSCI560 or MATH560.
• DSCI570: Introduction to Machine Learning: The goal of machine learning is to build computer systems that improve automatically with experience. This introductory course will study both the theoretical properties of machine learning algorithms and their practical applications. Prerequisite: DSCI503.
• DSCI575: Advanced Machine Learning: The goal of machine learning research is to build computer systems that learn from experience and that adapt to their environments. Prerequisite: DSCI570.
• DSCI598: Special Topics:
  • 0-6 Semester Hr.
  • 1-6 Semester Hr.
  • 1-6 Semester Hr.
  • 1-6 Semester Hr.
  • DATA SCIENCE, 1-6 Semester Hr.
• DSCI599: Independent Study:
  • 0.5-6 Semester Hr.
  • 0.5-6 Semester Hr.

Learning Outcomes

• Conduct data acquisition using a varied set of techniques structured and unstructured datasets.
• Apply preprocessing strategies to complex and dynamic datasets using industry-standard toolkits and machine learning algorithms.
• Differentiate between machine learning approaches such as classification, regression, clustering, and neural networks for predictive analytics and pattern recognition.
• Evaluate the predictive power of the different statistical and machine learning methods to solve real-world data science problems.
• Develop storytelling and visualization techniques to effectively communicate findings to a specific audience.
• Critically assess ethical considerations and challenges related to data collection and analysis.
• Construct a comprehensive data science project from inception to presentation, integrating the various techniques and tools learned throughout the course.
• Apply supervised, unsupervised, reinforcement machine learning models and deep learning models to solve problems in areas such as prediction, recognition, and classification.
• Explore and develop with various tools, techniques, and libraries in Python for data processing, feature extraction, visualization, validation, and evaluation.
• Create data visualization tools, techniques, and libraries in Python to visualize high-dimensional or complex data for stakeholders.
• Determine ethical implications through interpretability of big data and results from the application of various machine learning models.
• Design and develop a machine learning product that solves a chosen real-world challenge.
• Create a video presentation that succinctly outlines the problem, solutions, conclusions, and lessons learned regarding product development for stakeholders.