Applied Statistics & Data Science Major

Applied statistics uses a variety of computational techniques and methods, in order to visualize and explore data, seek and establish the structure and trends in data, investigate relationships between observed phenomena and facilitate data interpretation. Data science expands on statistics to encompass the entire life cycle of data, from its specification, gathering, and cleaning through its management and analysis, to its use in making decisions and setting policy. The Data Science and Applied Statistics program at USMA provides Cadets the opportunity to effectively explore structured and unstructured data, defining answerable questions, performing statistical analysis and communicating results both written and orally.  The program introduces the underlying mathematics of Data Science and Applied Statistics while simultaneously offering an exposure to computation and optimization issues inherent in large and disparate data sets.


Program of Study

The Applied Statistics and Data Science Major offers abundant opportunities for study in a broad range of mathematical subjects. Courses such as linear algebra, applied statistics, mathematical statistics, theory and applications of data science, generalized linear models, and mathematical computation provide a sound mathematical foundation in the data science and statistics fields. In addition, follow-on courses such as cyber foundations, database systems, computer aided systems engineering, and advanced individual study provide both depth in understanding the applications of data science theory, as well as opportunity for study and research in a selected subject. Whenever possible, the use of technology is emphasized to extend the knowledge required for the consideration of realistic and challenging problems of today's world.


Student Outcomes

The student outcomes of the Applied Statistics & Data Science major include:

  1. Demonstrate competence in computational and statistical thinking
    • Understand the basic statistical concepts of data analysis, data collection, modeling and inference
    • Formulate problems, plan data collection campaigns and analyze the data to provide insights
    • Demonstrate proficiency in foundational software skills and the associated algorithmic, computational problem-solving strategies
  2. Demonstrate competence in mathematical foundations
    • Understand the underlying structure of common models used in statistical and machine learning as well as the issues of optimization and convergence of algorithms
  3. SLO 1: Apply statistical model building and assessment techniques
    • Be adapt at data visualization using visualization techniques to communicate with others and identify weaknesses in proposed models
    • Employ statistical inference and draw conclusions using formal modeling.  Understand how data issues impact analysis and interpretation of statistical finding
  4. Employ algorithmic problem-solving skills
    • Define clear requirements to a problem, use efficient strategies to arrive at an algorithmic solution using a suitable high-level computer language
    • Leverage existing packages and tools to solve computational problems
  5. Prepare and manage data through the entire problem-solving process
    • Work with a variety of sources and formats of data
    • Prepare the data for use with a variety of statistical methods and models
    • Ensure the integrity of the data throughout the entire analytical process
  6. Transfer knowledge
    • Communicate results both written and orally
    • Demonstrate understanding of ethical issues in reproducibility