Introduction
The WKU Mathematical Sciences program with emphasis on Data Analytics is provides a sufficient background in Mathematics, Statistics and Data Analytics. Mathematics is essential for Data Analytics. Data Analytics, and its associated field, is one of the fastest growing employment opportunities in the world. The degree is structured to allow additional and elective courses electives in mathematics, business and computer science that enable the student to focus on a variety of further areas of interest. Students are strongly encouraged to take a minor in their applied field of interest such as Computer Science, Finance, Economics etc.
The Mathematical Sciences program is committed to developing students with a solid theoretical knowledge of Mathematical and Statistics concepts and nurturing them to work with a variety of more practical methods including predictive/prescriptive analytics, Big Data Computing, Applied Machine Learning, Statistical Data Mining and Data Visualization using programming environments such as R, MATLAB and Python.Many courses of the specialization end with a project that provide an opportunity to see how the material of the course is used in Data Analytics. In these project studentwill analyze the complex real-world problems arising in Data Science and learn to solve them through analysis of data with statistical software.
Career Prospects
The Data Analytics skills are in high demand (The Sexiest Job of the 21st Century) as the huge amount of data are generated by scientific, social activity and business from sensors, internet and other sources. Data Analytics experts are needed in virtually every job sector.
Graduates from the WKU Mathematical Sciences program prepare students for the emerging and high-growth fields of digital information and big data. This program will help prepare future Data Scientists to meet the demands of data driven industries, governmental agencies and labor market in financial sectors, health industry, social media and education. It provides a foundation for further graduate study and research.
Knowledge and Skills Students will Grasp during the Study
- Mastering the fundamentals of data analytics and the data science connections.
- Developing competency in R programming.
- Learning how to collect the resources required for a data science project.
- Applying predictive analytics techniques to use unstructured enterprise information and data to drive a better client experience and to leverage new data types: sentiment data, clickstream data, video, images and text data.
- Using data mining practices and machine learning technology tools on data sets from a variety of domains, including the Web, social networks, media, and smart cities.
- Applying visualization principles to clarify ideas, enhance understanding and help decision making.
Major (Core) Required Courses
Probability and Math Statistics
Big Data Computing
Applied Statistics
Foundations of Data Analysis
Big Data Visualization
Statistical Data Mining
Big Data Statistical Analysis
Big Data Techniques
Regression Analysis
