# Statistics and Data Science

## Why take courses in this discipline?

Studying statistics and data science develops logical thinking, quantitative reasoning, and creative problem-solving skills. Data analysis techniques play an essential role in numerous other disciplines. Our faculty members have strong research careers and provide opportunities for students to engage in research of their own. The curriculum also helps to prepare students for graduate study in fields like statistics, data science, engineering, psychology, and economics.

## How does the discipline contribute to the liberal arts?

Courses in statistics and data science focus on quantitative reasoning, writing, and communication.

## What kinds of questions are asked in this discipline?

Using real-world data, statisticians draw conclusions and make decisions based on available information. Here are examples of questions that might be asked in the field of statistics:

• Is one medical treatment better than another?
• Is there evidence of racial discrimination by a particular police department?
• What level of certainty do we have about our conclusions?

## How does a student get started?

Fall registration tip: Statistics courses often fill to capacity during first-year registration. If a student does not prioritize a math or stats course during registration, that student will likely be on a waiting list and will have to wait until the spring term to take the course. Note that SST 115 is not open to first years, but STA 209 and STA 230 are. Many courses in the Science and Social Studies Divisions have a stats prerequisite.

There are three possible entry points into the statistics curriculum, depending on a student’s quantitative background:

SST 115, Introduction to Statistics, introduces the notions of variability and uncertainty as well as common statistical procedures such as hypothesis testing and confidence intervals. It is an introductory course intended for students with a limited quantitative background and who do not plan to take further courses in the statistics curriculum. Compared to STA 209, SST 115 covers fewer topics and proceeds at a slower pace. Students cannot receive credit for both SST 115 and STA 209.

STA 209, Applied Statistics, covers applications of basic statistical methods, including hypothesis testing and confidence intervals, in both single- and multivariate settings. It is an introductory statistics course intended for students whose quantitative background includes calculus. This course is a prerequisite for several other courses in the statistics curriculum and is a requirement for the statistics concentration as well as for the economics major. Compared to MAT/SST 115, STA 209 covers a greater number of topics and proceeds at a faster pace. Students cannot receive credit for both SST 115 and STA 209.

STA 230, Introduction to Data Science, introduces core topics in data science using R programming including data cleaning, data management, exploratory data analysis, reproducible research, and data visualization. This class is the recommended entry point for a student who received a 5 on the AP statistics exam, or who has other significant statistics. A student who is interested in STA 230 and who has earned credit for SST 115 (for example, due to receiving a 4 or 5 on the AP statistics exam), should consult with a member of the statistics faculty to discuss waiving the STA 209 prerequisite for STA 230.

## AP/IB Credit

A score of 4 or 5 on the AP statistics exam can count as four credits of SST 115 and can fulfill the SST 115 requirement for departments other than political science, which requires that either SST 115 or STA 209 be taken at Grinnell. A student who received a 5 on the AP statistics exam may want to consider taking STA230 instead of STA209.

Students with any of the following exam scores will receive credit for MAT 131:

• A score of 4 (or higher) on the AP Calculus AB exam.
• A score of 3 (or higher) on the AP Calculus BC exam.
• A score of 5 (or higher) on the IB Mathematics: Applications and Interpretation exam, or the IB Mathematics: Analysis and Approaches exam.

## Courses in Statistics

All Courses in Statistics

Regular 200-Level Courses

• Applied Statistics (STA 209)
• Introduction to Data Science (STA 230)

Regular 300-Level Courses

• Statistical Modeling (STA 310)
• Design and Analysis of Experiments (STA 309)
• Applied Data Science (STA 330)
• Probability and Statistics I and II (MAT/STA 335 and 336)
• Bayesian Statistics (STA 340)

## Structure of a Statistics Concentration

The requirements of a statistics concentration are summarized on the Concentration and Requirements page. Suggestions for several different focuses within the concentration are listed on the Course Planning page.

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