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Why Study Statistics & Data Science?

Statistics is the science which studies data – its collection, description, analysis, and interpretation. Almost all modern professions, from economists to engineers and from social scientists to medical scientists, rely on statistics. Statistical methods are used for studying relationships, predicting results, testing hypothesis, and a variety of other purposes.

The Bachelor of Science degree in statistics is designed to provide students with a fundamental understanding of probability and mathematical statistics, a complementary knowledge of basic methods for data collection and inference, and practical computing skills to carry out statistical analyses of problems in many different areas of application.

One option within the major allows students with a strong interest in statistical or biostatistical aspects of a particular science to apply courses in that science to their major. This option should provide the interested student with a good background for employment or graduate work in statistics, biostatistics, or in that science. Emphases in actuarial science and data science enable students to pursue further specializations aligned with professional opportunities in these areas.
Statistics is the discipline at the heart of the scientific method of discovery. Statistical principles are used in designing experiments and surveys to collect information, and statistical procedures are applied to summarize information, draw conclusions, and make decisions.

Because of the broad applicability of their training in statistical reasoning and data analysis, undergraduate majors are prepared for careers in diverse fields – such as biotechnology, environmental science, insurance, industrial manufacturing, and market research – in which the need for professionally trained statisticians is great.

Graduates who seek to acquire additional skills in applied or theoretical statistics may also consider programs of advanced study at the master’s or doctoral level. Statisticians with advanced degrees are sought for senior positions in industry and government, as well as teaching positions in secondary schools, community colleges, and universities.

Bachelor's Degree Options in Statistics & Data Science

The Department offers three options for a Bachelor of Science degree in Statistics and a Minor. Each major degree is listed below, along with the applicable Degree Learning Outcomes (DLOs).

  • Demonstrate knowledge of basic statistical vocabulary and concepts. Students will be able to understand statistics concepts and vocabulary. Statistical terms such as population, sample, central tendency, dispersion, likelihood, parameter and interval estimation, hypothesis testing, and decision theory will be introduced.
    Introduced: Stat 119, 250, 350A
    Demonstrated: Stat 350B, 520, 560
    Mastered: Stat 550, 551AB, 575
  • Use calculus and algebra to study statistical inferences and modeling. When asked to identify and compare basic statistical concepts, students will be able to define terms, use definitions, form arguments using appropriate vocabulary, develop and manipulate mathematical formulations, and utilize the elementary methods of proof.
    Introduced: Stat 350A
    Demonstrated: Stat 520, 560
    Mastered: Stat 550, 551AB, 575
  • Interpret statistical inferences. Given data, students will be able to construct and interpret interval estimates for population parameters. Given data and a model, construct and interpret interval estimates for model parameters. Formulate and test statistical hypotheses; interpret results. Explain problems with the way traditional Fisherian inference has been applied in the scientific community.
    Introduced: Stat 250
    Demonstrated: Stat 350AB
    Mastered: Stat 410, 551AB, 560, 575, 580
  • Evaluate and fit probability models. Students will be able to compare and contrast probability models for characterizing populations. Given data, students will be able to fit these probability models by a suite of methods. Students will be able to use probability theory and foundational statistical principles to assess model fits and draw statistical inferences, both asymptotically and in finite samples. Students will know how to utilize statistical software such as SAS and R to perform model fitting and assessment, and develop the building blocks to extend these ideas to new models and scientific problems.
    Introduced: Stat 200, 250
    Demonstrated: Stat 350A, 350B, 551A, 551B
    Mastered: Stat 410, 520, 575, 580
  • Use statistical software appropriately. Given a real life data set, students will be able to apply statistical software to conduct data analyses, visualize the data, check model assumptions, and interpret the output from statistical software.
    Introduced: Stat 200, 250, 325, 410
    Demonstrated: Stat 520, 550
    Mastered: Stat 580
  • Communicate and Report Statistical Findings. Students will be able to communicate statistical inferences to both the lay person and to scientific audiences. Oral communication skills will include conference-style lecture presentations, business meeting presentations, and small group tutorials. Written communication skills will include statistical analysis reports, tutorials, and conceptual short text pieces. Students will be able to use statistical software such as SAS and R to visualize data and predictive analytics results to communicate statistical solutions to scientific problems.
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 560, 575, 580
  • Apply statistical models to data. Students will be able to determine which statistical methods are appropriate for a given dataset based on whether required mathematical assumptions are met, and whether the methods provide evidence useful in answering relevant questions. For appropriately chosen methods, students will utilize statistical software such as SAS and R to implement the analysis and summarize findings. Students will perform diagnostic tests to evaluate the performance of statistical methods (e.g., evaluating model fit, checking for expected behavior in numerical algorithms).
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 551B, 560, 575, 580
  • Demonstrate knowledge of basic statistical vocabulary and concepts. Students will be able to understand statistics concepts and vocabulary. Statistical terms such as population, sample, central tendency, dispersion, likelihood, parameter and interval estimation, hypothesis testing, and decision theory will be introduced.
    Introduced: Stat 119, 250, 350A
    Demonstrated: Stat 350B, 520, 560
    Mastered: Stat 550, 551AB, 575
  • Use calculus and algebra to study statistical inferences and modeling. When asked to identify and compare basic statistical concepts, students will be able to define terms, use definitions, form arguments using appropriate vocabulary, develop and manipulate mathematical formulations, and utilize the elementary methods of proof.
    Introduced: Stat 350A
    Demonstrated: Stat 520, 560
    Mastered: Stat 550, 551AB, 575
  • Interpret statistical inferences. Given data, students will be able to construct and interpret interval estimates for population parameters. Given data and a model, construct and interpret interval estimates for model parameters. Formulate and test statistical hypotheses; interpret results. Explain problems with the way traditional Fisherian inference has been applied in the scientific community.
    Introduced: Stat 250
    Demonstrated: Stat 350AB
    Mastered: Stat 410, 551AB, 560, 575, 580
  • Evaluate and fit probability models. Students will be able to compare and contrast probability models for characterizing populations. Given data, students will be able to fit these probability models by a suite of methods. Students will be able to use probability theory and foundational statistical principles to assess model fits and draw statistical inferences, both asymptotically and in finite samples. Students will know how to utilize statistical software such as SAS and R to perform model fitting and assessment, and develop the building blocks to extend these ideas to new models and scientific problems.
    Introduced: Stat 200, 250
    Demonstrated: Stat 350A, 350B, 551A, 551B
    Mastered: Stat 410, 520, 575, 580
  • Use statistical software appropriately. Given a real life data set, students will be able to apply statistical software to conduct data analyses, visualize the data, check model assumptions, and interpret the output from statistical software.
    Introduced: Stat 200, 250, 325, 410
    Demonstrated: Stat 520, 550
    Mastered: Stat 580
  • Communicate and Report Statistical Findings. Students will be able to communicate statistical inferences to both the lay person and to scientific audiences. Oral communication skills will include conference-style lecture presentations, business meeting presentations, and small group tutorials. Written communication skills will include statistical analysis reports, tutorials, and conceptual short text pieces. Students will be able to use statistical software such as SAS and R to visualize data and predictive analytics results to communicate statistical solutions to scientific problems.
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 560, 575, 580
  • Apply statistical models to data. Students will be able to determine which statistical methods are appropriate for a given dataset based on whether required mathematical assumptions are met, and whether the methods provide evidence useful in answering relevant questions. For appropriately chosen methods, students will utilize statistical software such as SAS and R to implement the analysis and summarize findings. Students will perform diagnostic tests to evaluate the performance of statistical methods (e.g., evaluating model fit, checking for expected behavior in numerical algorithms).
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 551B, 560, 575, 580
  • Demonstrate knowledge of basic statistical vocabulary and concepts. Students will be able to understand statistics concepts and vocabulary. Statistical terms such as population, sample, central tendency, dispersion, likelihood, parameter and interval estimation, hypothesis testing, and decision theory will be introduced.
    Introduced: Stat 119, 250, 350A
    Demonstrated: Stat 350B, 520, 560
    Mastered: Stat 550, 551AB, 575
  • Use calculus and algebra to study statistical inferences and modeling. When asked to identify and compare basic statistical concepts, students will be able to define terms, use definitions, form arguments using appropriate vocabulary, develop and manipulate mathematical formulations, and utilize the elementary methods of proof.
    Introduced: Stat 350A
    Demonstrated: Stat 520, 560
    Mastered: Stat 550, 551AB, 575
  • Interpret statistical inferences. Given data, students will be able to construct and interpret interval estimates for population parameters. Given data and a model, construct and interpret interval estimates for model parameters. Formulate and test statistical hypotheses; interpret results. Explain problems with the way traditional Fisherian inference has been applied in the scientific community.
    Introduced: Stat 250
    Demonstrated: Stat 350AB
    Mastered: Stat 410, 551AB, 560, 575, 580
  • Evaluate and fit probability models. Students will be able to compare and contrast probability models for characterizing populations. Given data, students will be able to fit these probability models by a suite of methods. Students will be able to use probability theory and foundational statistical principles to assess model fits and draw statistical inferences, both asymptotically and in finite samples. Students will know how to utilize statistical software such as SAS and R to perform model fitting and assessment, and develop the building blocks to extend these ideas to new models and scientific problems.
    Introduced: Stat 200, 250
    Demonstrated: Stat 350A, 350B, 551A, 551B
    Mastered: Stat 410, 520, 575, 580
  • Use statistical software appropriately. Given a real life data set, students will be able to apply statistical software to conduct data analyses, visualize the data, check model assumptions, and interpret the output from statistical software.
    Introduced: Stat 200, 250, 325, 410
    Demonstrated: Stat 520, 550
    Mastered: Stat 580
  • Communicate and Report Statistical Findings. Students will be able to communicate statistical inferences to both the lay person and to scientific audiences. Oral communication skills will include conference-style lecture presentations, business meeting presentations, and small group tutorials. Written communication skills will include statistical analysis reports, tutorials, and conceptual short text pieces. Students will be able to use statistical software such as SAS and R to visualize data and predictive analytics results to communicate statistical solutions to scientific problems.
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 560, 575, 580
  • Apply statistical models to data. Students will be able to determine which statistical methods are appropriate for a given dataset based on whether required mathematical assumptions are met, and whether the methods provide evidence useful in answering relevant questions. For appropriately chosen methods, students will utilize statistical software such as SAS and R to implement the analysis and summarize findings. Students will perform diagnostic tests to evaluate the performance of statistical methods (e.g., evaluating model fit, checking for expected behavior in numerical algorithms).
    Introduced: Stat 200, 250, 350A
    Demonstrated: Stat 325, 350B, 410
    Mastered: Stat 520, 551B, 560, 575, 580