Master

In Muncie (USA)

Price on request

Description

  • Type

    Master

  • Location

    Muncie (USA)

  • Duration

    Flexible

  • Start date

    Different dates available

Gain a profound understanding of statistical and computational methods through our master’s degree in statistics.

Offered as either a master of arts (MA) or master of science (MS), this two-year program will prepare you to tackle a variety of research and analytical roles in today’s data-centric world. The degree also provides excellent preparation for pursuing a Ph.D. in statistics.

Facilities

Location

Start date

Muncie (USA)
See map

Start date

Different dates availableEnrolment now open

About this course

When you pursue a master’s degree in statistics from Ball State, you’ll benefit from courses covering everything from probability and stochastic processes to regression analysis to multivariate statistics.

During our program, you’ll also learn to use statistical packages, used and required often in businesses, industries, and government agencies.

Questions & Answers

Add your question

Our advisors and other users will be able to reply to you

Who would you like to address this question to?

Fill in your details to get a reply

We will only publish your name and question

Reviews

This centre's achievements

2020

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 5 years

Subjects

  • Statistics
  • Computational
  • Business
  • Time Series Models
  • Probability
  • Variables
  • Theory of Statistics
  • Statistical Learning
  • Applications
  • Linear
  • Generalized

Course programme

32-33 credits

The master’s program in statistics provides students with the background suitable for employment as a statistician in business, industry, or government. The degree also provides suitable preparation for pursuing a PhD degree in statistics.

Degree requirements
  • Regression and Time Series Models
  • Probability and Random Variables
  • Theory of Statistics
  • Introduction to Statistical Learning
  • Generalized Linear Models with Applications
  • Computational Methods in Statistics
  • Research Methods in Mathematics and Statistics
9-10 credits from
  • Theory of Sampling and Surveys
  • Analysis of Variance in Experimental Design Models
  • Statistical Programming: Base SAS 9
  • Environmental Statistics
  • Introductory Survival Analysis
  • Bayesian Methods and Linear Mixed Models
  • Measure Theory and Integration 1

Statistics

Price on request