Courses and training in applied statistics are available through many colleges and universities at the graduate and undergraduate levels. They may be offered as part of an applied statistics certificate program or as components of a bachelor's or master's degree program in various disciplines, including engineering, economics, mathematics and political science. Because applied statistics plays such an integral role in so many fields, graduates of such programs may find work in a variety of settings.
Here are a few common concepts in applied statistics courses:
- Investigating a hypothesis
- Data configuration
- Using computers for mathematical calculations
- Practicing the scientific method
- Using graphs
List of Common Courses
Quantitative Analysis Course
In an introductory quantitative analysis course, students learn about graphical analysis, hypothesis testing, confidence intervals and regression analysis. These principles and techniques are widely applicable to inquiries in the social sciences. Students also learn how to collect, manage and analyze data with computer software and finalize and present their statistical results.
Applied Statistics and Probability Course
This introductory course on probability and statistics introduces discrete and continuous random variables, regression, hypothesis testing and other fundamental elements of statistical analysis. With an understanding of these techniques, students can effectively interpret data presented in papers, studies or other research materials. The course covers also techniques for creating and interpreting graphs using statistical software or specialized graphing calculators.
Experimental Design Course
Students learn how to design and analyze experiments to study data related to research projects in their area of study. Topics covered include sample sizes, residual analysis, checking assumptions and transformations. Students practice using industry-standard statistics software to analyze data.
Statistics and Computational Methods Course
In a computer statistics course, students learn about the structure and use of computer languages that are used to interpret and graph statistical data. Courses cover matrices, resampling strategies and linear and nonlinear modeling methods. Specialized algorithms such as the Monte Carlo simulation may also be discussed.