BS Statistics (2017-2018 Catalog)

Prof. Elizabeth Johnson, coordinator (

Catalog Entry

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The Bachelor of Science in Statistics is designed to provide a framework for students to develop connections between statistical concepts and theories and their applications to statistical practice. It will prepare statisticians who can use modern statistical techniques to design studies, collect data, analyze and visualize high dimensional data sets, and draw valid conclusions in an increasingly data-centric world. In this program, students will meld the time-tested concepts and theories of statistics with modern methods of analysis, in order to interpret the data that is collected in nearly every discipline and every sector of industry and government. Students will select one of three concentrations: Applied Statistics, Mathematical Statistics, or Statistical Analytics.

All graduates of the BS Statistics program will be able to:

  1. Analyze and interpret data by appropriately fitting, assessing, and interpreting a variety of statistical models.
  2. Summarize data through data reduction and visualization techniques.
  3. Demonstrate their knowledge of basic mathematical skills needed for statistics.
  4. Demonstrate competence utilizing various statistical software packages, e.g. SAS and R, needed for data analysis.
  5. Prepare and communicate oral and written statistical reports for a variety of audiences

In addition:

  • Students in the Applied Statistics concentration will be able to:
    • Design and analyze experiments with applications to a specific discipline.
    • Develop and conduct surveys with applications to a specific discipline.
  • Students in the Statistical Analytics concentration will be able to:
    • Demonstrate proficiency in the use of statistical tools to analyze data.
    • Interpret data from high-dimensional data sets.
  • Students in the Mathematical Statistics concentration will be able to:
    • Demonstrate a mastery of probability and mathematical statistics at the mathematical level of calculus and linear algebra.
    • Identify and apply valid analysis techniques to non-standard data.

Admission Requirements

Admission decisions are handled by the Admissions Committee of the Office of Admissions. Guidelines for admission to the BS Statistics program will follow the guidelines listed in the catalog of George Mason University,

Degree Requirements (120 credit hours)

Mason Core Courses (31 Credits)
  • Foundation Requirements (9 credits)
    • Written Communication (6 credits)
    • Oral Communication (3 credits)
  • Core Requirements (22 credits)
    • Arts (3 credits)
    • Global Understanding (3 credits)
    • Literature (3 credits)
    • Natural Science (7 credits)
    • Social and Behavioral Sciences (3 credits)
    • Western Civilization/World History (3 credits)
  • Some of the Mason Core requirements are fulfilled by major core requirements; the associated credit hours (as indicated below) are counted with the major core requirements.
  • Foundation Requirements
    • Quantitative Reasoning: Fulfilled by MATH 113 (4 credits)
    • Information Technology: Fulfilled by CS 112 (4 credits) and either CS 105 (1 credit) or CDS 151 (1 credit)
  • Capstone Experience Requirement: Fulfilled by STAT 490 (3 credits)
  • Writing-Intensive Course Requirement: Fulfilled by STAT 490 (3 credits)
Mathematics Core Courses (11 Credits)
  • MATH 113 Analytic Geometry and Calculus I (4 credits)
  • MATH 114 Analytic Geometry and Calculus II (4 credits)
  • MATH 203 Linear Algebra (3 credits)
Computational Skills Core Courses (5 Credits)
  • CS 105 Computer Ethics and Society (1 credit) or CDS 151 Data Ethics in an Information Society (1 credit)
  • CS 112 Introduction to Computer Programming (4 credits)

Statistics Core Courses (24 Credits)
  • STAT 260 Introduction to Statistical Practice (3 credits)
  • STAT 334 Introduction to Probability Models and Simulation (3 credits) or STAT 346 Probability for Engineers (3 credits)
  • STAT 354 Probability and Statistics for Engineers and Scientists II (3 credits)
  • STAT 362 Introduction to Computer Statistical Packages (3 credits)
  • STAT 456 Applied Regression Analysis (3 credits)
  • STAT 463 Introduction to Exploratory Data Analysis (3 credits)
  • STAT 489 Pre-Capstone Professional Development (3 credits)
  • STAT 490 Capstone in Statistics (3 credits)

Restricted Statistics Elective Courses (9 Credits)

Statistics restricted elective courses will be chosen from any existing Statistics (STAT) courses numbered 440-499. Courses selected cannot be used to fulfill other curriculum requirements.

  • STAT 455 Experimental Design (3 credits)
  • STAT 460 Introduction to Biostatistics (3 credits)
  • STAT 462 Applied Multivariate Statistics (3 credits)
  • STAT 465 Nonparametric Statistics and Categorical Data Analysis (3 credits)
  • STAT 472 Introduction to Statistical Learning (3 credits)
    [This course cannot be selected by students in the Statistical Analytics Concentration. It is a required course for the concentration.]
  • STAT 474 Introduction to Survey Sampling (3 credits)
  • STAT 498 Independent Study in Statistics (3 credits)
  • STAT 499 Special Topics in Statistics (3 credits)

Restricted Technical Elective Courses (9 Credits)

Technical elective course will be chosen from the following courses. Specific course selections must be pre-approved by the undergraduate coordinator.

  • Computational and Data Sciences (CDS) course numbered above 200 (1-3 credits)
  • Computer Science (CS) course numbered above 200 (1-3 credits)
  • Mathematical Sciences (MATH) course numbered above 200 (1-3 credits)
  • Operations Research (OR) course numbered above 300 (1-3 credits)
  • BENG 322 Health Data Challenges (3 credits)
  • CYSE 325 Discrete Events Systems Modeling (3 credits)
  • ENGH 388 Professional and Technical Writing (3 credits)
  • IT 214 Database Fundamentals (3 credits)
  • SYST 473 Decision and Risk Analysis (3 credits)
  • SYST 488 Financial Systems Engineering (3 credits)

Concentration Courses (15-24 Credits)

Selection of a concentration will allow a student to tailor the program toward more applied, theoretical, or computational aspects of statistical practice.

  • Applied Statistics (15-21 Credits):
    This concentration is designed for students interested in developing proficiency in analytical methods applicable to specific types of jobs, which will enhance a student's marketability when seeking future employment. This is accomplished through the additional requirement in this concentration of completion of a minor in a field that makes substantial use of data analysis. The selected minor must be pre-approved by the undergraduate coordinator.

    • Approved Minor (15-21 credits)

  • Mathematical Statistics (15 Credits):
    This concentration is designed for students interested in mastering the theoretical underpinnings of statistics and probability, which will increase future employment opportunities in more research-oriented jobs. This concentration is also recommended for students intending to continue with graduate studies in statistics.

    • STAT 356 Statistical Theory (3 credits)
    • CDS 130 Computing for Scientists (3 credits)
    • MATH 213 Analytic Geometry and Calculus III (3 credits)
    • MATH 290 Introduction to Advanced Mathematics (3 credits)
    • MATH 315 Advanced Calculus I (3 credits)

  • Statistical Analytics (24 Credits):
    This concentration is designed for students interested in both concepts from statistics and computer science, which will increase future employment opportunities in fields requiring analysis of massive data sets in a very modern way. This concentration has additional course requirements designed to enhance the student's computational skill level beyond that acquired via the program's core coursework.

    • STAT 472 Introduction to Statistical Learning (3 credits)
    • CS 211 Object-Oriented Programming (3 credits)
    • CS 310 Data Structures (3 credits)
    • CS 330 Formal Methods and Models (3 credits)
    • CS 450 Database Concepts (3 credits) or CDS 302 Scientific Data and Databases (3 credits)
    • CS 484 Data Mining (3 credits) or CDS 303 Scientific Data Mining (3 credits)
    • MATH 125 Discrete Mathematics I (3 credits) or 481 Numerical Methods in Engineering (3 credits)

Free Elective Courses (7-16 Credits)

The number of free elective credits varies with choice of concentration.