Statisticians typically need at least a master's degree in statistics, mathematics, or another quantitative field. However, a bachelor's degree is sufficient for some entry-level jobs. Research and academic jobs generally require a Ph.D.

Education

Statisticians typically need at least a master's degree, although some entry-level jobs are available for those with a bachelor's degree. Most statisticians have degrees in mathematics, economics, computer science, or another quantitative field. A bachelor's degree in statistics typically includes courses in linear algebra, calculus, experimental design, survey methodology, probability, and statistical theory.

Many colleges and universities advise students to take courses in a related field, such as computer science, engineering, physics, or mathematics. These courses can help prepare students to work in a variety of industries. Coursework in engineering or physical science, for example, may be useful for statisticians working in manufacturing on quality or productivity improvement. A background in biology, chemistry, or health sciences is useful for work testing pharmaceutical or agricultural products.

Because statisticians often work with data analysis software, computer programming courses may be particularly beneficial for students.

Important Qualities

Analytical skills. Statisticians use statistical techniques and models to analyze large amounts of data. They must determine the appropriate software packages and understand computer programming languages to design and develop new techniques and models. They must also be precise and accurate in their analyses.

Communication skills. Statisticians often work with, and propose solutions to, people who do not have extensive knowledge of mathematics or statistics. They must be able to present statistical information and ideas so that others will understand.

Math skills. Statisticians use statistics, calculus, and linear algebra to develop their models and analyses.

Problem-solving skills. Statisticians must develop techniques to overcome problems in data collection and analysis, such as high nonresponse rates, so that they can draw meaningful conclusions.