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Details
Program Details
Degree
Bachelors
Major
Applied Statistics | Mathematics | Statistics
Area of study
Mathematics and Statistics | Natural Science
Course Language
English
About Program

Program Overview


Program Overview

The B.S. in Applied Statistics is a comprehensive program that aims to equip students with a deep understanding of mathematical concepts and techniques. The program covers a wide range of fields, including single and multivariate calculus, linear algebra, real analysis, probability/statistics, basic differential equations, and mathematical modeling.


Program Objectives

The program is designed to help students demonstrate:


  • Mastery of basic mathematical ideas and techniques
  • Understanding of the nature of proof
  • Ability to think analytically and critically, and to formulate problems, solve them, and interpret their solutions, both collaboratively and individually
  • Ability to use technological tools, such as algebraic and visualization software, statistical packages, and a high-level programming language
  • Proficiency in communicating complex results to a diverse audience with clarity
  • Commitment to collecting and managing data responsibly and ethically, and the ability to discern appropriate methods for analyzing various types of data
  • Proficiency in coding and computation for organizing, summarizing, and analyzing raw data collected from a diverse array of contexts, using technology to draw conclusions and make actionable decisions
  • Ability to think critically and abstractly about the mathematical foundations for statistics and data science, and their application to real data
  • Skill in collaborative problem solving for large-scale projects involving data from a wide variety of domains

Program Details

The program focuses on developing students' skills in:


  • Mathematical modeling
  • Data analysis and interpretation
  • Statistical reasoning and thinking
  • Communication of complex results
  • Responsible and ethical data collection and management
  • Coding and computation for data analysis
  • Collaborative problem solving
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