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.

The information here updates the description of Statistics programs in the College of Science Bulletin.

If you haven't made up your mind about a statistics major, please feel free to contact us.

Course Descriptions

Statistics And Society

(Banner Course Number: 11300)
Semester:
Summer Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
No objectives provided.
Description:
Introduction to statistical ideas and their impact on public policy and the sciences. Sample surveys, design of experiments, measurement, analysis of data, simulating probabilities, concepts of inference. Application to current issues and controversies. Not available for credit toward graduation in the School of Science.

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Introduction To Actuarial Science

(Banner Course Number: 17000)
Semester:
Fall
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:
Summer Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
No objectives provided.
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:
Summer Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
No objectives provided.
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:
Fall Spring
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:
Fall Spring
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:
Summer Fall Spring
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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Elementary Statistical Methods

(Banner Course Number: 30100)
Semester:
Summer Fall Spring
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, 35000, 50100, and in no more than one of STAT 50300 and STAT 51100.

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Introductory Probability

(Banner Course Number: 31100)
Semester:
Spring
Prerequisites:
two semesters of college calculus
Credits:
3.00
Objectives:
No objectives provided.
Description:
Formulation of probability problems, discrete and continuous random variables, expectation, standard distributions, applications to statistical problems, and problems in the physical sciences. Credit cannot be given for more than one of STAT 22500, 31100, or 41600.

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Introduction To Statistics

(Banner Course Number: 35000)
Semester:
Fall Spring
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 interpretate 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, 35000, 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:
Fall Spring
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.

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Professional Practice Co-Op I

(Banner Course Number: 38199)
Semester:
Fall Spring Summer
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:
Fall Spring Summer
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:
Fall Spring Summer
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:
Summer Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
No objectives provided.
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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Probability

(Banner Course Number: 41600)
Semester:
Fall Spring
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:
Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
No objectives provided.
Description:
An introduction to the mathematical theory of statistical inference, emphasizing inference for standard parametric families of distributions. Properties of estimators. Bayes and maximum likelihood estimation. Sufficient statistics. Properties of test of hypotheses. Most powerful and likelihood-ratio tests. Distribution theory for common statistics based on normal distributions.

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Introduction To Time Series

(Banner Course Number: 42000)
Semester:
Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
No objectives provided.
Description:
An introduction to time series analysis suitable for actuarial science, engineering, and sciences. Model building and forecasting with ARMA and ARIMA models. Resampling methods for confidence intervals. Multivariate, state-space, and nonlinear models. Volatility models (ARCH and GARCH). Smoothing in time series.

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Actuarial Models- Life Contingencies

(Banner Course Number: 47201)
Semester:
Fall
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:
Fall Spring
Prerequisites:
No prerequisites provided.
Credits:
4.00
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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Life Contingencies II

(Banner Course Number: 47500)
Semester:
Spring
Prerequisites:
No prerequisites provided.
Credits:
2.00
Objectives:
  • Develop the theory and knowledge that actuarial students need to apply basic life contingencies to more sophisticated and complicated models.
  • Calculate expected values, variances, and other moments for situations involving multiple decrements, joint and survivor life functions, pension plans, and other insurance products.
  • Determine the distributions of losses for these situations and use those distributions to calculate premiums, reserves, and capital needs.
  • Use statistical models to determine the premiums necessary using standard profit testing techniques.
  • Analyze the expected profit for each year and determine the source of profit within each year.
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. Permission of instructor required.

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Loss Models

(Banner Course Number: 47900)
Semester:
Spring
Prerequisites:
No prerequisites provided.
Credits:
3.00
Objectives:
  • Understand the steps involved in the modeling process and how to carry out these steps in solving business problems.
  • Analyze data from an application in a business context.
  • Determine a suitable model including parameter values.
Description:
This material provides an introduction to modeling and covers important actuarial methods that are useful in modeling. Students will be introduced to survival, severity, frequency and aggregate models, and use statistical methods to estimate parameters of such models given sample data. The student will further learn to identify steps in the modeling process, understand the underlying assumptions implicit in each family of models, recognize which assumptions are applicable in a given business application, and appropriately adjust the models for impact of insurance coverage modifications. The student will be introduced to a variety of tools for the calibration and evaluation of the models. Permission of instructor required.

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Topics In Statistics For Undergraduates

(Banner Course Number: 49000)
Semester:
Fall Spring Summer
Prerequisites:
No prerequisites provided.
Credits:
1.00 to 5.00
Objectives:
No objectives provided.
Description:
Supervised reading course or special topics cousre at the senior level for undergraduates are given under this number. Permission of instructor required.

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Last Updated: Oct 20, 2017 2:05 PM

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

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