Why study Data Science
Data science impacts every aspect of our daily lives, from healthcare and finance to entertainment and technology. It's perfect for students who enjoy analyzing data to make informed decisions.
This is an emerging field that incorporates mathematics, statistics, and programming to develop and apply methodologies to solve problems in the real world. There is a high demand for data science professionals across many industries including technology, public health, business, insurance, banking, environment, and others. The Bureau of Labor Statistics reports that jobs for data scientists will increase faster than the average until the year of 2028. As of February 2021, the average salary for a data scientist is $113,609.
The Department of Mathematics & Statistics offers a bachelor’s degree with a concentration in Data Science, available as both a major and a minor. This program is designed to prepare students for immediate entry into industry, facilitating innovative discoveries. While some data science positions are available with a bachelor's degree, many employers prefer candidates with advanced degrees. Our program provides a robust foundation for further studies in Master’s and Ph.D. programs in mathematics, engineering, statistics, and related fields.
For more information on careers in data science, visit the Datajobs website: https://datajobs.com/Data-Scientist-Jobs. Other useful websites include TechCrunch Jobs, LinkedIn, and the IEEE job site.
Our faculty bring extensive real-world experience to our data science courses, with backgrounds in mathematics, statistics, government, and industry. Many faculty are engaged in multi-disciplinary research and are active in local, state, and national professional organizations. They are dedicated to providing a high-quality education and preparing students for successful careers.
Our program is accredited and is open to individuals of different backgrounds.
What Will I learn
- Demonstrate an advanced understanding of the theoretic underpinnings of data analysis, machine learning, predictive analytics, probability theory, numerical analysis, and computational methods.
- Demonstrate critical thinking, specifically employ appropriate analytical methods and apply critical reasoning processes to describe and model complex datasets.
- Demonstrate the ability to use appropriate statistical methodologies for real-world data analysis settings.
- Be able to integrate in data manipulation, data cleaning, exploratory analysis, feature extraction, visualization, data modeling etc.
- Be able to effectively communicate data driven ideas and results through contexts such as seminars, teaching, outreach, publications, and cross-disciplinary work.
