Data Science has been a rapidly growing science in recent years and has many applications. It is widely used by businesses and government organizations to understand changes in the economy, health policies, and business practices and to make forecasts about future events based on past patterns. Statistics based data science models help draw accurate inferences on large databases.
The Department of Statistics and Biostatistics offers a B.S. degree in data science. The program helps students navigate their journey by understanding the various career opportunities for data scientists, providing tools in statistical methodology, programming and mathematics which are required to become a practicing data scientist. Students get hands-on exposure to real life projects and large data sets along with modern statistical software.
This program helps create individuals who are job-ready and support the demands of data scientists in today’s world.
Applications Architect • Business Analyst • Business Intelligence Developer • Computer Systems Analyst • Data Analyst • Data Architect • Data Engineer • Data Scientist • Data Visualization Designer • Database Administrator • Enterprise Architect • Information Systems Analyst • Infrastructure Architect • Machine Learning Engineer • Machine Learning Scientist • Marketing Analyst • R programmer • Statistical Programmer • Statistician
Visit the Bureau of Labor Statistics to learn more about the outlook for your future career.
Department of Statistics & Biostatistics
University CatalogExplore the foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Introduces statistical programming and inference, with hands-on analysis of real-world datasets, including economic data, document collections, and social networks.
An introduction to statistical data analysis using Python. Learn about data preparation and transformation, macros, and descriptive statistics. Topics include diagnostics, t-procedures, ANOVA, nonparametrics, cross-tabulation, chi-squared, correlation, and regression.
Learn introductory probability using simulation methods. Topics include the estimation and accuracy of probabilities using repeated sampling and simulating conditional probabilities using conditional programming techniques.
Understanding ethical implications of one’s personal data, the risks and rewards of data collection and surveillance, and the needs for policy, advocacy, and privacy monitoring. Implications of algorithms and models.
An introduction to the R environment and data analysis. Topics include use of dataframes and lists, importing and exporting data files, writing user-defined functions, R packages, regression, Principle Components Analysis, and clustering.
Studies data visualization and interactive data exploration. Topics include importing, exporting and data merging, graphs and charts, interactive maps, and meaningful visual representations of complex statistics.