Bachelor of Science in Applied Statistics

Collaborative Online Degree

This 100 percent online, consortial program is taught by IU South Bend, IU East, IU Purdue University Indianapolis, IU Northwest, and IU Southeast. This consortial model allows you to take coursework from several campuses and learn from a wide range of faculty.

This program is authorized, exempt, or not subject to state regulatory compliance and may enroll students from all 50 states.

Many online support services are available to assist you as you progress through the program.

The IU Bachelor of Science in Applied Statistics is designed to meet the needs of students who wish to pursue careers in the fields of Statistics, Medical Research and Analysis, Industrial Data Analytics, and Marketing. It may also appeal to individuals working in the financial sector. 

>According to the U.S. Bureau of Labor Statistics, employment demand and job openings remain strong for graduates pursing STEM occupations in computer science, engineering, and mathematics. 

The 120-credit hour transfer-friendly curriculum combines the flexibility of 100% online asynchronous delivery with high quality instruction offered by IU faculty members and is tailored to the particular needs of students who are working and/or have family responsibilities.  

Students transferring into the IU Online BS in Applied Statistics will be able to transfer up to 60 credit hours earned in accredited Associate degree programs, and formal articulation agreements are in place to facilitate transfer into the program from Ivy Tech and Vincennes. 

Graduates from the BS in Applied Statistics degree will demonstrate the statistical and computational skills described in the American Statistical Association Curriculum Guidelines for Undergraduate Programs in Statistical Science, and possess strong skills in SQL and familiarity with industrial-leading statistical packages, including SAS or R. 

Core skill areas include

• Foundational mathematical knowledge in calculus (differentiation, integration and infinite series), linear algebra and calculus-based probability theory (properties of univariate and multivariate random variables, discrete and continuous distributions). 

• The application of statistical methods and theory such as distributions of random variables, likelihood theory, point and interval estimation, hypothesis testing, Bayesian methods and resampling to solve problems. 

• Design of studies, proficiency in data collection and analysis with a focus on data management skills including organization, design, and drawing inferences from data using appropriate statistical methodology. 

• Statistical modeling for problem solving in variety of linear and nonlinear parametric, parametric, and semiparametric regression models, including model building and assessment, as well as skills in applying multivariate methods; and statistical learning and statistical data mining techniques for big data analysis.  

• Statistical computation using statistical tools involving computer programming languages, such as R or SQL, for statistical modeling and data analysis. 

• Data analytics communication that employs statistical ideas and appropriate technical terms in oral and written presentations to provides critically reasoned analysis for professional as well as non-statistical audiences. 

Target  Audience 

This mathematically focused degree targets students who are interested in becoming statisticians, data scientists, business/financial analysts, market research analysts, and database administrators. With the program’s preparation in statistics, probability, and computer programming in R and SAS, graduates of this program are likely to find entry-level positions in industries such as scientific research and development, banking and finance, government and insurance, operations management, and technical consulting services.  

Program Goals and Outcomes

Students graduating from the BS in Applied Statistics degree shall demonstrate competence in the statistical and computational skills described in the American Statistical Association Curriculum Guidelines for Undergraduate Programs in Statistical Science. Core skills include:  

Mathematical Foundations

• Students will utilize tools to solve problems in calculus (differentiation, integration, and infinite series), linear algebra, and calculus-based probability theory (properties of univariate and multivariate random variables, discrete and continuous distributions).   

Statistical Methods and Theory   

• Students will define basic terms and concepts in fundamental statistics theory and methods: distributions of random variables, likelihood theory, point and interval estimation, hypothesis testing, Bayesian methods, and resampling.   
• Students will be able to apply these methods properly to solve problems.   

Design of Studies and Exploratory Data Analysis  

• Students will apply data management skills.   
• Students will organize, design, and draw inferences from data, using appropriate statistical methodology.  

• Students will adhere to ethical standards with regards to data management.    

Statistical Modeling  

• Students will apply appropriate modeling methodologies in a variety of linear and nonlinear parametric, parametric, and semiparametric statistical data mining techniques for big data analysis.   
• Students will demonstrate flexible problem-solving skills.   

Statistical Computation  

• Students will use statistical tools involving computer programming languages, such as R, SAS, and database languages, for statistical modeling and data analysis.   

Data Analytics Communication 

• Students will communicate and present statistical ideas clearly in oral and written forms using appropriate technical terms and deliver data analysis results to a non-statistical or statistical audience. 

Admissions

Admissions requirements vary by campus.

Degree Requirements (120 cr.)

To graduate with the BS in Applied Statistics, you must complete a total of 120 credit hours. You may be able to transfer an associate degree or up to 64 credit hours from a regionally accredited two-year college and up to 90 credit hours from a regionally accredited four-year college or university. Course requirements fall into four categories and are defined by student learning outcomes.

• General Education Courses (33 cr.)
• Mathematics Core (22 cr.)
• Probability/Statistics Core (24 cr.)
• Programming Core (4 cr.)
• Upper-Level Statistics Electives (12 cr.)
• Free Electives (balance of credits needed to equal 120 credit requirement)

Mathematics Core (22 cr.)

• MATH-M 215 Calculus I (5 cr.) 
• MATH-M 216 Calculus II (5 cr.)
• MATH-M 301 Linear Algebra and Applications; OR
  MATH-M 303 Linear Algebra for Undergraduates
• MATH-M 311 Calculus 3
• MATH-M 447 Mathematical Models and Applications I
• MATH-M 448 Mathematical Models and Applications II

Probability/Statistics Core (24 cr.)

• STAT-S 352 Data Modeling and Inference
• STAT-S 412 Statistical Learning Using R
• STAT-S 431 Applied Linear Modeling
• STAT-S 437 Categorical Data Analysis
• STAT-S 470 Exploratory Data Analysis

Complete one of the following 9-credit hour sequences

Sequence 1

• MATH-M 360 Elements of Probability;
• MATH-M 366 Elements of Statistical Inference
• MATH-M 367 Introduction to Statistical Programming in R

Sequence 2

• MATH-M 463 Introduction to Probabiity I
• MATH-M 466 Introduction to Mathematical Statistics
• STAT-I 421 Modern Statistical Modeling Using R and SAS
• STAT-S 475 Statistical Learning and High Dimensional Data Analysis

Programming Core (4 cr.)

• CSCI-A 201 Introduction to Programming I (4 cr.)

Upper-Level Electives (12 cr.)

• STAT-I 414 Introduction to Design of Experiments
• STAT-S 432 Applied Linear Models II
  Pending Final Approval
• STAT-S 450 Time Series Analysis
• STAT-S 460 Sampling
• Additional electives may be added by the faculty