Undergraduate Programs
Purdue's Department of Statistics is Indiana's major center for statistical teaching and research. It has the state's largest and oldest doctoral program in statistics and offers a broader range of statistics courses than any other institution in the state. The faculty is recognized as one of the nation's strongest in studies by the National Research Council.
As one of America's leading technical universities, Purdue offers strong programs in many areas that apply statistics. Numerous computational facilities make statistical software easily accessible on a variety of hardware platforms. First courses in statistics generally include laboratory sessions, and all applied statistics courses use and provide instruction in statistical software. The department has its own computing systems for research, and these are used in some advanced classes.
The Statistics Major consists of two options, one in Applied Statistics and one in Mathematical Statistics. The Statistics Major with Mathematical Statistics Option usually leads to a double major in Mathematics and Statistics. The interdisciplinary major in Actuarial Science is jointly administered by the Department of Statistics and the Department of Mathematics. Most Actuarial Science majors have a double major through the Statistics Major with Applied Statistics option.
Students planning to major in Statistics, Actuarial Science, or Mathematics take the same first-year academic program. It is therefore easy to choose among these majors and joint majors after a year at Purdue.
The Statistics Minor is also an option for students majoring in another discipline.
If you haven't made up your mind about a statistics major, please feel free to contact us.
Course Descriptions
Freshman Orientation Seminar
(Banner Course Number: 10100)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
1.00
Objectives:
- Demonstrate the ability to communicate with peers & speakers in classroom.
- Demonstrate the ability to locate and use academic success resources.
- Demonstrate understanding of time management and study skills.
- Demonstrate knowledge in the utilization of Purdue degree planning tools.
- Demonstrate understanding of the academic requirements and career paths.
Description:
A one-credit course for first-year Statistics/Mathematics majors designed to develop concepts and skills in team building, time management, and professional development that complement the students' academic and research training. Talks from a broad range of speakers are included.
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Statistics And Society
(Banner Course Number: 11300)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Distinguish between and qualify methods of data collection.
- Interpret graphs and statistical analyses.
- Express and calculate the likelihood of events.
- Create a narrative from statistical analysis.
Description:
This course is an introduction to statistical literacy and the cultivation of critical thinking skills. Statistical concepts are learned through practical application in the context of contemporary societal issues and debates. Some of the concepts explored include the statistical problem-solving process, data collection methodologies such as surveys and experiments, the ethical considerations entailed in data acquisition, the relationship between variables, the assessment of validity and reliability in the measurement of variables, analysis and graphical representation of single and multiple variables, as well as statistical inference. Furthermore, the curriculum actively encourages the development of critical thinking abilities with a specific focus on data-related inquiries.
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Introduction To Actuarial Science
(Banner Course Number: 17000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
2.00
Objectives:
No objectives provided.
Description:
(MA 17000) An introduction to actuarial science from the point of view of practicing actuaries from life insurance, casualty insurance and consulting; introduction to insurance and the mathematical theory of interest; application of spreadsheets to problems related to actuarial science.
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Topics In Statistics For Undergraduates
(Banner Course Number: 19000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
- Discover data science and professional development opportunities to prepare for a career.
- Design efficient search strategies to acquire new data science skills.
- Devise the most appropriate data science strategy to answer a research question.
Description:
Supervised reading course or special topics course at the freshman level for undergraduates are given under this number. Permission of instructor required.
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Introduction To Probability Models
(Banner Course Number: 22500)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Understand basic terminology, set theory, and counting rules related to probability.
- Identify various discrete and continuous probability models/distributions and apply them to answer probability questions.
- Understand the Normal distribution and how to use the Standard Normal Table.
- Understand basic terms, graphs, and symbols and be able to interpret statistics in the media.
- Have a foundation in statistical methodology including the basics of hypothesis testing.
Description:
An introduction to basic probability. Emphasis is placed on formulation of models and applications. Probability calculus, standard distributions, random variables, and moments. Credit cannot be given for more than one of STAT 22500, 31100, or 41600.
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Introduction To Data Science
(Banner Course Number: 24200)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Use Python, R, and selected tools to scrape, clean, process, and visualize data.
- Apply data management techniques to prepare, parse, manipulate, and store data.
- Use statistical methods to summarize data and identify relationships.
- Explain how to formulate new hypotheses and draw accurate conclusions from data.
- Apply statistics and computational analysis to make predictions based on data.
- Apply basic computer science concepts such as modularity, abstraction, and encapsulation to data analysis problems.
- Effectively communicate the outcome of data analysis using descriptive statistics and visualizations.
Description:
(CS 24200) This course provides a broad introduction to the field of data science. The course focuses on using computational methods and statistical techniques to analyze massive amounts of data and to extract knowledge. It provides an overview of foundational computational and statistical tools for data acquisition and cleaning, data management and big data systems. The course surveys the complete data science process from data to knowledge and gives students hands-on experience with tools and methods. Basic knowledge of Python required. Computer Science majors cannot count this course as a degree requirement but can take it for credit as a free elective if taken before CS 37300, 34800, 47100, 47300, 44800.
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Problems Solving In Probability
(Banner Course Number: 25000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
2.00
Objectives:
No objectives provided.
Description:
(MA 25000) This course is designed to teach techniques for solving problems in probability theory which are relevant to the actuarial sciences. It is intended to help actuarial students prepare for the Society of Actuaries and Casualty Actuarial Society Exam P/1. Credit by Examination is not available for this course. MA/STAT 41600 is strongly recommended.
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Topics In Statistics For Undergraduates
(Banner Course Number: 29000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
No objectives provided.
Description:
Supervised reading course or special topics course at the sophomore level for undergraduates are given under this number. Permission of instructor required.
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Cooperative Experience I
(Banner Course Number: 29199)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication and teamwork.
- Increase understanding of the profession and practice, basic industry principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in statistics. Program coordinated by school with cooperation of participating employers. Students submit summary report and company evaluation. Professional Practice students only. Permission of department required.
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Cooperative Experience II
(Banner Course Number: 29299)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication and teamwork.
- Increase understanding of the profession and practice, basic industry principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in mathematics. Program coordinated by school with cooperation of participating employers. Students submit summary report and company evaluation. Professional Practice students only. Permission of department required.
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Elementary Statistical Methods
(Banner Course Number: 30100)
Semester:
Prerequisites:
college algebra
Credits:
3.00
Objectives:
- Students will be introduced to statistical methods and be able to apply them to multiple fields of study.
- Students will be able to understand, interpret standard statistical techniques, analyze data for one variable and for several variables, calculate and use confidence intervals and significance test for means and proportions, understand the design of samples and experiments, perform and understand basic probability calculations, perform simple and multiple regression procedures, and understand correlation.
Description:
Introduction to statistical methods with applications to diverse fields. Emphasis on understanding and interpreting standard techniques. Data analysis for one and several variables, design of samples and experiments, basic probability, sampling distributions, confidence intervals and significance tests for means and proportions, correlation and regression. Software is used throughout. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, 30301, 35000, 35500, 50100, and in no more than one of STAT 50300 and STAT 51100.
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Elementary Statistical Methods
(Banner Course Number: 3010N)
Semester:
Prerequisites:
college algebra
Credits:
3.00
Objectives:
- Students will be introduced to statistical methods and be able to apply them to multiple fields of study.
- Students will be able to understand, interpret standard statistical techniques, analyze data for one variable and for several variables, calculate and use confidence intervals and significance test for means and proportions, understand the design of samples and experiments, perform and understand basic probability calculations, perform simple and multiple regression procedures, and understand correlation.
Description:
Introduction to statistical methods with applications to diverse fields. Emphasis on understanding and interpreting standard techniques. Data analysis for one and several variables, design of samples and experiments, basic probability, sampling distributions, confidence intervals and significance tests for means and proportions, correlation and regression. Software is used throughout. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, 30301, 35000, 35500, 50100, and in no more than one of STAT 50300 and STAT 51100.
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Probability And Statistics For Business
(Banner Course Number: 30301)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Understand basic terminology, set theory, and counting rules related to probability.
- Identify selected discrete and continuous probability models/distributions and apply them to answer probability questions.
- Understand, interpret standard statistical techniques/methods, analyze data, calculate and use confidence intervals and hypothesis tests for means and proportions, understand the design of samples and experiments.
- Understand basic terms, graphs, and symbols and be able to interpret statistics in the media.
Description:
This course provides fundamentals for describing data, probability theory, and statistical inference. Topics include exploratory data analysis, discrete and continuous probability distributions, sampling techniques, sampling distributions, confidence intervals and hypothesis tests for means and proportions, and 1 -sample variance, and hypothesis tests about the distributions of categorical variables. Students will utilize Microsoft Excel 2019 or 365.
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Introductory Probability
(Banner Course Number: 31100)
Semester:
Prerequisites:
two semesters of college calculus
Credits:
3.00
Objectives:
- Determine appropriate methods for determining probabilities and conditional probabilities.
- Use of distribution functions involving mass functions and cumulative distribution functions.
- Use of specific distribution functions for both discrete and continuous random variables including means and variances.
- Use of distribution functions involving more than one variable.
Description:
An introduction to basic probability with some theoretical background as well as applications to the physical sciences and engineering. Formulation of probability problems, general probability rules, conditional probability and Bayes Theorem, and discrete and continuous random variables and their properties.
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Introduction To Statistics
(Banner Course Number: 35000)
Semester:
Prerequisites:
two semesters of college calculus
Credits:
3.00
Objectives:
- Understand basic terms, graphs, and symbols and be able to interpret statistics in the media.
- Understand and be able to explain statistical processes and be able to fully interpret statistical results.
- Understand why and how statistical investigations are conducted and the "big ideas" that underlie statistical investigations.
- Be able to use a statistical package (R or SAS) to analyze data and interpret result.
- Big Ideas in Statistics: variability, distributions, and models; causation vs correlation; practical significance vs statistical significance, etc.
Description:
This course provides a data-oriented introduction to applied statistics, covering exploratory data analysis, experimental design, probability distributions, simulation, sampling distributions, and the Central Limit Theorem. Students will learn the fundamentals of statistical inference, including confidence intervals and hypothesis tests for population means, paired and independent comparisons of means, analysis of variance, and regression. The course emphasizes hands-on experience with statistical software and is primarily intended for students majoring in the mathematical sciences.
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Introduction To Statistics
(Banner Course Number: 3500N)
Semester:
Prerequisites:
two semesters of college calculus
Credits:
3.00
Objectives:
- Understand basic terms, graphs, and symbols and be able to interpret statistics in the media.
- Understand and be able to explain statistical processes and be able to fully interpret statistical results.
- Understand why and how statistical investigations are conducted and the "big ideas" that underlie statistical investigations.
- Be able to use a statistical package (R or SAS) to analyze data and interpret result.
- Big Ideas in Statistics: variability, distributions, and models; causation vs correlation; practical significance vs statistical significance, etc.
Description:
A data-oriented introduction to the fundamental concepts and methods of applied statistics. Exploratory analysis of data. Sample design and experimental design. Probability distributions and simulation. Sampling distributions. The reasoning of statistical inference. Confidence intervals and tests for one and two samples. Inference for contingency tables, regression, and correlation. Introduction to regression with several explanatory variables. Essential use is made of statistical software throughout. Intended primarily for students majoring in the mathematical sciences. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, 30301, 35000, 35500, 50100, and in no more than one of STAT 50300 and STAT 51100.
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Statistics For Data Science
(Banner Course Number: 35500)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Utilize simulations to generate data (statistical simulations).
- Explain sampling distributions, their properties, and methods for resampling (Statistical Reasoning).
- Explain foundational concepts such as confidence intervals, hypothesis testing, correlations, etc.
- Assess statistical problems, and justify and apply statistical methods used to solve the problem (Statistical Thinking).
Description:
An introduction to methodologies for data analysis and simulation. Populations and sampling. Distributions and summaries of distributions. Algorithms for sampling and resampling. Foundational statistical concepts including confidence intervals, hypothesis testing, correlation. Introduction to classification and regression. Essential use is made of statistical software throughout. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, 30301, 35000, 35500, 50100, and in no more than one of STAT 50300 and STAT 51100.
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Professional Practice Co-Op I
(Banner Course Number: 38199)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication, and teamwork.
- Increase understanding of the profession and practice, basic professional principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in Statistics. Program coordinated by Office of Professional Practice with cooperation of participating employers. Students submit a summary report and company evaluation. Permission of instructor required.
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Professional Practice Co-Op II
(Banner Course Number: 38299)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication, and teamwork.
- Increase understanding of the profession and practice, basic professional principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in Statistics. Program coordinated by Office of Professional Practice with cooperation of participating employers. Students submit a summary report and company evaluation. Permission of instructor required.
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Professional Practice Co-Op III
(Banner Course Number: 38399)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication, and teamwork.
- Increase understanding of the profession and practice, basic professional principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in Statistics. Program coordinated by Office of Professional Practice with cooperation of participating employers. Students submit a summary report and company evaluation. Permission of instructor required.
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Topics In Statistics For Undergraduates
(Banner Course Number: 39000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
- Acquire new data science skills.
- Devise the most appropriate data science strategy in order to answer a research question.
- Apply data science techniques in order to answer a research question about a big data set.
- Apply techniques for documenting, testing, sharing and creating a repository for code.
- Apply the best workflow practices in data science.
Description:
Supervised reading course or special topics course at the junior level for undergraduates are given under this number. Permission of instructor required.
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Cooperative Experience III
(Banner Course Number: 39399)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication and teamwork.
- Increase understanding of the profession and practice, basic industry principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in statistics. Program coordinated by school with cooperation of participating employers. Students submit summary report and company evaluation. Professional Practice students only. Permission of department required.
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Extensive Cooperative Experience IV
(Banner Course Number: 39499)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication and teamwork.
- Increase understanding of the profession and practice, basic industry principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in statistics. Program coordinated by school with cooperation of participating employers. Students submit summary report and company evaluation. Professional Practice students only. Permission of department required.
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Extensive Cooperative Experience V
(Banner Course Number: 39599)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
0.00
Objectives:
- Apply knowledge to identifying and solving problems in the professional context.
- Advance skills related to professional preparation, critical thinking, problem solving, communication and teamwork.
- Increase understanding of the profession and practice, basic industry principles, and principles and practice of ethical responsibility in professional work.
Description:
Professional experience in statistics. Program coordinated by school with cooperation of participating employers. Students submit summary report and company evaluation. Professional Practice students only. Permission of department required.
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Probability
(Banner Course Number: 41600)
Semester:
Prerequisites:
multivariate calculus
Credits:
3.00
Objectives:
No objectives provided.
Description:
(MA 41600) An introduction to mathematical probability suitable as preparation for actuarial science, statistical theory, and mathematical modeling. General probability rules, conditional probability and Bayes theorem, discrete and continuous random variables, moments and moment generating functions, joint and conditional distributions, standard discrete and continuous distributions and their properties, law of large numbers and central limit theorem. Credit cannot be given for more than one of STAT 22500, 31100, or 41600.
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Statistical Theory
(Banner Course Number: 41700)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Understand and able to use inference for standard parametric families of distributions; properties of estimators; Bayes and maximum likelihood estimation; sufficient statistics; properties of test of hypotheses; distribution theory for common statistics based on normal distributions.
Description:
An introduction to the mathematical theory of statistical inference, emphasizing inference for standard parametric families of distributions. Properties of estimators. Maximum likelihood estimation. Sufficient statistics. Hypothesis tests and confidence intervals. Distribution theory for common statistics based on normal distributions, including linear regression. Bayesian Statistics include posterior inference, posterior mean, maximum a-posteriori estimator, credible intervals, and Bayesian hypothesis testing.
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Introduction To Time Series
(Banner Course Number: 42000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Manage time series data; extract subset of time series data.
- Visualize time series data.
- Understand trend and correlation.
- Model time series data.
- Forecast of time series data.
Description:
An introduction to time series analysis suitable for students of actuarial science, engineering, and the sciences. Model building and forecasting with ARMA and ARIMA models. Basic financial volatility models (ARCH and GARCH). Resampling methods for confidence intervals. Basics of spectral analysis, including spectral density estimation and periodograms.
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Fundamental Long Term Actuarial Mathematics
(Banner Course Number: 47201)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
4.00
Objectives:
- Understand and use decrement models in conjunction with interest rate models to evaluate insurance, annuities, and investments.
- Apply models to calculate actuarial values, gross and net premiums, and benefit and expense reserves.
Description:
Mathematical foundation of actuarial science, emphasizing probability models for life contingencies as the basis for analyzing life insurance and life annuities and determining premiums and reserves. This course provides the background for Course MLC of the Society of Actuaries and Course 3L of the Casualty Actuarial Society.
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Introduction To Arbitrage-Free Pricing Of Financial Derivatives
(Banner Course Number: 47301)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Use arbitrage-free option pricing in the binomial market model and the Black-Scholes model.
- Adapt the binomial setting to computing exotic and path-dependent option prices, including the determination of optimal exercise.
- Follow marking-to-market strategies for approximate discrete delta hedging.
Description:
This course exposes students to a number of financial economics concepts related to arbitrage-free option pricing in the binomial market model and the Black-Scholes model. Specific models include (1) Options and parity relationship between options; (2) Option Pricing under the Binomial model; (3) Option Pricing under the Black-Scholes model; (4) Option hedging and the market maker 's overnight profit; (5) Black Scholes theory with Brownian motion and Ito calculus; (6) Risk-neutral option pricing and Monte Carlo valuation; (7) Stochastic interest rates and Stochastic Volatility. This course provides the background for Couse MFE of the Society of Actuaries and Course 3F of the Casualty Actuarial Society.
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Statistics For Risk Modeling I
(Banner Course Number: 47401)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Understand the basics of several important analytic methods, including the following: Linear regression models; Generalized linear models; Principle components analysis; Decision trees; Cluster analysis.
Description:
This course introduces various statistics learning methods for analyzing insurance data. The main objective is to prepare actuarial science students for the Exam SRM: Statistics for Risk Modeling of the Society of Actuaries.
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Advanced Long Term Actuarial Mathematics
(Banner Course Number: 47501)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Understand key concepts concerning multiple state mortality/morbidity and joint life mortality models for insurance and annuity contracts.
- Understand key concepts concerning estimation and construction of multiple state and joint life models for insurance and annuity contracts.
- Perform calculations on the present value random variables associated with benefits and expenses for any of the models in the first learning objective.
- Analyze emerging surplus, and to set premiums and reserves, using profit testing.
- Understand how retirement benefits are accrued, valued, and funded.
- Understand various types of equity-linked life insurance guarantees, options that are embedded in life insurance and annuity contracts and be able to price, reserve and hedge the risk inherent in these options.
Description:
This course covers advanced life contingencies topics such as multiple decrement models, joint life models, pension models, and profit emergence and testing for insurance products. It builds on the basic life contingent models learned in STAT 47200.
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Fundamental Short Term Actuarial Mathematics
(Banner Course Number: 47902)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
- Calculate moments, percentiles, and generating functions.
- Describe how changes in the parameters affect the distribution.
- Recognize classes of distributions, including extreme value distributions, and their relationships.
- Create new distributions by multiplication by a constraint, raising to a power, exponentiation, and mixing.
- Identify the applications to which each distribution may apply and explain why.
- Apply the distribution to an application, given the parameters.
- Compare two distributions based on various characteristics of their tails, including moments, ratios of moments, limiting tail behavior, hazard rate function, and mean excess function.
Description:
This course aligns with one of the Society of Actuaries examinations. The course will cover the learning objectives for a portion of the Society of Actuaries Examination STAM. Permission of department required. Permission of instructor required.
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Topics In Statistics For Undergraduates
(Banner Course Number: 49000)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
No objectives provided.
Description:
Supervised reading course or special topics course at the senior level for undergraduates are given under this number. Permission of instructor required.
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Mathematical Statistics
(Banner Course Number: I5280)
Semester:
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
No objectives provided.
Description:
Description: Sufficiency and completeness, the exponential family of distributions, theory of point estimation, Cramer-Rao inequality, Rao-Blackwell Theorem with applications, maximum likelihood estimation, asymptotic distributions of ML estimators, hypothesis testing, Neyman-Pearson Lemma, UMP tests, generalized likelihood ratio test, asymptotic distribution of the GLR test, and sequential probability ratio test.
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