Faculty of Arts and Science
Data Science 5010/Business Analytics 5010
Introduction to Data Science and Analytics in Python I
Credit hours: 3.00
Contact hours per week: 0-3-0
The basics of data science and data science workflow. Data science process models, framing of problems, preparation of data, visualization of data, predictive models, clustering algorithms, and interpretation and communication of findings. Statistical topics such as summary statistics, distributions, correlation, and regression will also be introduced. This course is based on Python, which is a leading programming language for data science. No prior knowledge of Python or statistics is assumed.
Prerequisite(s):Admission to a graduate program
Equivalent:Statistics 5850 (Introduction to Data Science with Python) (prior to 2022/2023)
Data Science 5020/Business Analytics 5020
Data Visualization
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Sophisticated visual representations appropriate for industry and academic applications which serve to motivate analyses, detect flaws, and communicate data-driven findings. Topics include choosing a visualization approach, best practices for data visualization, and generation and interpretation of various visual representations in Python and other applications.
Prerequisite(s):Data Science 5010/Business Analytics 5010
Data Science 5050/Business Analytics 5050
Data Wrangling
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Preparation of data for analysis. Topics include understanding problem requirements, data profiling and structuring, data imputation, integration of data from multiple sources, data duplication, reformatting, dimensionality, metric construction, loading, metadata, and dimensions of data quality.
Corequisite(s):Data Science 5010/Business Analytics 5010
Data Science 5110/Business Analytics 5110
Introduction to Data Science and Analytics in Python II
Credit hours: 3.00
Contact hours per week: 0-3-0
Introduction to machine learning tools in Python, including deep learning tools for unstructured datasets. Students will learn to set up these analytical tools, choose tools to solve specific problems, use them to inform decisions, and communicate their results to non-specialists.
Prerequisite(s):Data Science 5010/Business Analytics 5010
Data Science 5140/Business Analytics 5140
Data Management
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Introduction to common forms of data storage, such as relational databases, file structures, and data warehouses. Overview of finding and retrieving data from data management systems (e.g., SQL) and web sources.
Corequisite(s):Data Science 5010/Business Analytics 5010
Final Project
Credit hours: 1.50
Contact hours per week: 0-1.5-0
Application of data science tools and workflow to real world datasets.
Prerequisite(s):Data Science 5010/Business Analytics 5010
Corequisite(s):Data Science 5020/Business Analytics 5020 AND
Data Science 5050/Business Analytics 5050 AND
Data Science 5110/Business Analytics 5110 AND
Data Science 5140/Business Analytics 5140