Foundations of Data Science

Careers in Data Science Courses in Foundations of Data Science

Combine math, programming & problem-solving
Create & communicate data-driven solutions

With the Graduate Certificate in Foundations of Data Science (GCFDS) program, you'll gain a solid foundation in data science theories, methods and applications using complex data from a variety of fields, including business, science, health, engineering, agriculture, social science, natural resources, entertainment, communications, sports and more. In both academic and industry contexts, learn to use data management tools and gain skills in data visualization, machine learning model construction, results interpretation and communication.

What to expect from this program:

• Learn to store, prepare, visualize, analyze, model and communicate data
• Gain basic terminology and concepts of data science
• Understand the components of a data science workflow and
• Apply data science workflow to answer targeted questions
• Acquire advanced knowledge in data processing and machine learning, such as:
  • data cleaning
  • data wrangling
  • regression and classification tasks
  • supervised and unsupervised machine learning approaches
  • regression trees and deep neural networks
  • data visualization and communication of complex results
• Apply machine learning algorithms and associated tools in a cloud computing environment
• Understand limitations and ethical concerns around solving real-world problems using data collection and machine learning
• Develop competency with programming languages like Python

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Courses for the Graduate Certificate in Foundations of Data Science (12.0 credit hours):

In case of discrepancies between this page and the Graduate Studies Calendar and Course Catalogue, the Graduate Studies Calendar and Course Catalogue shall prevail.

• Data Science 5010/Business Analytics 5010 - Introduction to Data Science and Analytics in Python I (1.5 credit hours)
• Data Science 5020/Business Analytics 5020 - Data Visualization (1.5 credit hours)
• Data Science 5050/Business Analytics 5050 - Data Wrangling (1.5 credit hours)
• Data Science 5110/Business Analytics 5110 - Introduction to Data Science and Analytics in Python II (1.5 credit hours)
• Data Science 5140/Business Analytics 5140 - Data Management (1.5 credit hours)
• Data Science 5180 - Final Project (1.5 credit hours)