Why study Data Science
The Data Science concentration is a good fit for students who like to apply mathematics to solve real-world problems in business, economics, finance, medical, physics, engineering, and other fields. A data scientist creates sophisticated mathematical models using machine learning and predictive analytics techniques to analyze the data. This program prepares students for employment or further graduate study in data science, economics, applied statistics, computational methods, and engineering.
Data science 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 at APSU offers a B.S. in Mathematics: Data Science concentration, to train students who can promptly obtain positions in industry to spark new discoveries. Graduates of this program will have the theoretical, practical, and comprehensive knowledge about data analysis that helps to become Data analyst, Predictive Analyst, Quantitative analyst, Financial Analyst, and Researchers. The Department has dedicated full-time faculty members and features small class sizes. All classes are taught by professors and instructors satisfying APSU Faculty Qualifications Matrix. Many classes are conducted in modern computer labs, and all classrooms are “smart” classrooms, allowing projector-based demonstrations and lectures.
Department of Mathematics and Statistics is one of the institutions that offers in its (B.S.) degree a variety of concentrations (Actuarial Science, Statistics, Data Science) and multiple Master’s Degree (M.S.) and Professional Science Management (Predictive Analytics, Mathematical Finance, Mathematical Finance). Currently there are more than 80 students in the Master’s program. Mathematical Finance and Predictive Analysis is offered 100% online as well as on-campus. Students' have frequent opportunities to attend conferences and present their research while working closely with the mathematical finance faculty.
The B.S. in Mathematics: Data Science concentration, is innovative, professionally relevant, valuable, an consists of 120-credits that can be completed in four years. Students can also complete their degree more quickly by taking summer courses. A core collection of classes in linear algebra, probability, mathematical statistics, machine learning, time series analysis, mathematical modeling, and predictive analytics are available to students. These courses help the students to gain knowledge and skills in data manipulation, data visualization, data mining, and programming in R, SAS, and Python.
The faculty in the Data Science concentration have experience teaching mathematics/statistics/programming at undergraduate and graduate level. All full-time professors have completed terminal degrees in various mathematics/statistics fields and part-time faculty members have many years of real-world experience in different mathematical/statistics disciplines. Many faculty are engaged in multi-disciplinary research and are active in local, state, and national professional mathematics organizations.
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.
