Statistics
Overview
With the digital revolution, the world is becoming increasingly more quantitative, and the field of statistics has become essential in advancing our understanding of the natural, political, and social sciences as well as the fields of medicine and public health. Statistics also constitutes a crucial part of decision making in industry, business, and government, and is at the heart of the emerging field of Data Science.
Students studying statistics at New College will develop statistical reasoning skills and apply them when analyzing and modeling data from many different sources. They will learn both classical and modern statistical techniques as well as the theoretical foundations underlying these methodologies. At the same time, they will acquire the necessary computational skills to work with data and evaluate the role of uncertainty in inferential statistical analyses. Through their experience working on both individual and team projects, students will also learn how to effectively communicate and report statistical results to different audiences.
Faculty in Statistics
Melissa Crow, Instructor of Statistics
Bernhard Klingenberg, Professor of Statistics/Interim Director of Data Science
Milo Schield, Visiting Professor of Statistics
Andrey Skripnikov, Assistant Professor of Applied Statistics
Requirements for the AOC in Statistics
A minimum of eleven (11) academic units.
| Code | Title |
|---|---|
| Mathematics 1 | |
| Calculus I | |
| Calculus II* | |
| Linear Algebra | |
| Probability I and Probability II 2 | |
| Core Requirements | |
| Dealing with Data I* | |
| Dealing with Data II | |
| Applied Linear Models | |
| Electives 3 | |
Select four from the following examples: | |
| Introduction to Categorical Data Analysis | |
| R for Data Science | |
| Statistical Learning | |
| Applied Time Series Analysis | |
| Data Visualization and Communication | |
| Statistical Estimation and Inference | |
| Data Munging and Exploratory Data Analysis | |
| Databases for Data Science | |
| Additional Requirement | |
| Senior Thesis in Statistics and Baccalaureate Exam | |
- 1
It is recommended that students planning an AOC in Statistics complete the calculus and linear algebra courses by the end of their second year.
- 2
These are each one-mod courses; together they count as one academic unit.
- 3
This list is not exhaustive. Please consult with the Statistics faculty as other courses may also satisfy this requirement, such as certain undergraduate courses in Mathematics or other fields, undergraduate or graduate Data Science courses, or tutorials supervised by Statistics faculty.
Requirements for the Joint AOC in Statistics
A minimum of eight (8) academic units.
| Code | Title |
|---|---|
| Mathematics | |
| Calculus I | |
| Probability I and Probability II 1 | |
| Core Requirements | |
| Dealing with Data I* | |
| Dealing with Data II | |
| Applied Linear Models | |
| Electives 2 | |
Select three from the following examples: | |
| Introduction to Categorical Data Analysis | |
| R for Data Science | |
| Statistical Learning | |
| Applied Time Series Analysis | |
| Data Visualization and Communication | |
| Statistical Estimation and Inference | |
| Data Munging and Exploratory Data Analysis | |
| Databases for Data Science | |
| Additional Requirement | |
Senior Thesis demonstrating knowledge of statistical methods and Baccalaureate Exam | |
- 1
These are each one-mod courses; together they count as one academic unit.
- 2
This list is not exhaustive. Please consult with the Statistics faculty as other courses may also satisfy this requirement, such as certain undergraduate courses in Mathematics or other fields, undergraduate or graduate Data Science courses, or tutorials supervised by Statistics faculty.
Requirements for a Secondary Field in Statistics
A minimum of six and one-half (6 1/2) academic units.
| Code | Title |
|---|---|
| Mathematics | |
| Probability I 1 | |
| Core Requirements | |
| Dealing with Data I* | |
| Dealing with Data II | |
| Applied Linear Models | |
| Electives 2 | |
Select three from the following examples: | |
| Introduction to Categorical Data Analysis | |
| R for Data Science | |
| Statistical Learning | |
| Applied Time Series Analysis | |
| Data Visualization and Communication | |
| Data Munging and Exploratory Data Analysis | |
| Databases for Data Science | |
- 1
This is a one-mod course.
- 2
This list is not exhaustive. Please consult with the Statistics faculty as other courses may also satisfy this requirement, such as certain undergraduate courses in Mathematics or other fields, undergraduate or graduate Data Science courses, or tutorials supervised by Statistics faculty.
The four-year sample pathway to a Statistics AOC starts with the introductory courses Dealing with Data I & II, which are non-calculus based, and the Calculus I & II sequence, which provides the necessary mathematical background for the study of statistics. In the second year, this is followed by courses in Probability and Linear Algebra, in addition to at least one applied statistics elective. With this background, students are well prepared to take the core course in Linear Models along with many other elective courses starting in their third year.
Sample Four-Year Pathway
| First Year | |||||
|---|---|---|---|---|---|
| Fall Term | ISP | Spring Term | |||
| Dealing with Data I | ISP | Dealing with Data II | |||
| Calculus I | Calculus II | ||||
| Second Year | |||||
| Fall Term | ISP | Spring Term | |||
| R for Data Science | ISP | Introduction to Categorical Data Analysis | |||
| Probability I and II | Linear Algebra | ||||
| Third Year | |||||
| Fall Term | ISP | Spring Term | |||
| Linear Models | ISP | Mathematical Statistics | |||
| Applied Time Series Analysis | |||||
| Fourth Year | |||||
| Fall Term | ISP | Spring Term | |||
| Statistical Learning | Thesis | Thesis | |||
| Thesis | |||||
Sample Two-Year Pathway
The two-year pathway requires that a student has completed Calculus I & II and a one-semester introductory statistics course.
| First Year | |||||
|---|---|---|---|---|---|
| Fall Term | ISP | Spring Term | |||
| Dealing with Data II | ISP | Introduction to Categorical Data Analysis | |||
| R for Data Science | Linear Algebra | ||||
| Probability I & II | |||||
| Second Year | |||||
| Fall Term | ISP | Spring Term | |||
| Linear Models | ISP | Mathematical Statistics | |||
| Applied Time Series Analysis | Thesis | Statistical Learning | |||
| Thesis | Thesis | ||||
Representative Senior Theses in Statistics
- Spatial Modeling of the Relative Abundance of Bird Populations in Peninsular Florida Using Citizen Science Data
- Effects of Multicollinearity in Variable Selection Algorithms
- Distance, Movement, and Turnout: The Relationship Between Precinct Polling Locations and Turning Out to Vote
- Statistical Modeling of Solar Flare Occurrences and Their Energy Distributions
- Teaching Statistics through Mobile Applications
- Dynamics of Protein Synthesis with Autoregulation: A Computational Biology Approach