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
Andrey Skripnikov, Associate Professor of Applied Statistics and Data Science
Bernhard Klingenberg, Professor of Statistics/Director of Data Science Masters Program
Toby Wade, Assistant Professor of Data Science and Statistics
Milo Schield, Visiting Professor of Statistics
Requirements for the AOC in Statistics
A minimum of eleven (11) academic units.
| Code | Title |
|---|---|
| Core Mathematics Requirements (4 units) 1 | |
| MAC 2311 | Calculus I 2 |
| MAC 2312 | Calculus II |
| MATH 2500 & MATH 3510 | and (0.5 unit each) |
| MATH 3105 | |
| Core Statistics Requirements (3 units) | |
| STA 2023 | Introduction to Applied Statistics (Dealing with Data I) 2 |
| STA 3024 | Dealing with Data II |
| STAN 3275 | Applied Linear Models |
| Electives (4 units) 3 | |
| Select four from the following examples: | |
| STAN 3000 | Statistical Learning |
| STA 3100 | R for Data Science |
| or DATA 3110 | Data Munging and Exploratory Data Analysis |
| STAN 3780 | Applied Time Series Analysis |
| STAN 3230 | Data Visualization and Communication |
| STAN 3360 | Financial Markets Modeling using Machine Learning |
| DATA 3130 | 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
AP or IB credit may be counted towards that requirement. Please reach
out to a Statistics faculty to confirm.- 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, Computer Science 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 |
|---|---|
| Core Mathematics Requirements (2 units) | |
| MAC 2311 | Calculus I 1 |
| MATH 2500 & MATH 3510 | and (0.5 unit each) |
| Core Statistics Requirements (3 units) | |
| STA 2023 | Introduction to Applied Statistics (Dealing with Data I) 1 |
| STA 3024 | Dealing with Data II |
| STAN 3275 | Applied Linear Models |
| Electives (3 units) 2 | |
| Select three from the following examples: | |
| STAN 3000 | Statistical Learning |
| STA 3100 | R for Data Science |
| or DATA 3110 | Data Munging and Exploratory Data Analysis |
| STAN 3780 | Applied Time Series Analysis |
| STAN 3230 | Data Visualization and Communication |
| STAN 3360 | Financial Markets Modeling using Machine Learning |
| DATA 3130 | Databases for Data Science |
| Additional Requirement | |
| Senior Thesis demonstrating knowledge of statistical methods and Baccalaureate Exam | |
- 1
AP or IB credit may be counted towards that requirement. Please reach
out to a Statistics faculty to confirm.- 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, Computer Science 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 |
|---|---|
| Core Mathematics Requirements (0.5 units) | |
| MATH 2500 | (0.5 unit) |
| Core Statistics Requirements (3 units) | |
| STA 2023 | Introduction to Applied Statistics (Dealing with Data I) 1 |
| STA 3024 | Dealing with Data II |
| STAN 3275 | Applied Linear Models |
| Electives (3 units) 2 | |
| Select three from the following examples: | |
| STAN 3000 | Statistical Learning |
| STA 3100 | R for Data Science |
| or DATA 3110 | Data Munging and Exploratory Data Analysis |
| STAN 3780 | Applied Time Series Analysis |
| STAN 3230 | Data Visualization and Communication |
| STAN 3360 | Financial Markets Modeling using Machine Learning |
| DATA 3130 | Databases for Data Science |
- 1
AP or IB credit may be counted towards that requirement. Please reach
out to a Statistics faculty to confirm.- 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, Computer Science 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 | |||
| Introduction To Applied Statistics: 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 | Linear Algebra | |||
| Probability I and II | Statistical Learning | ||||
| Third Year | |||||
| Fall Term | ISP | Spring Term | |||
| Linear Models | ISP | Applied Time Series Analysis | |||
| Financial Markets Modeling using Machine Learning | |||||
| Fourth Year | |||||
| Fall Term | ISP | Spring Term | |||
| Thesis | Thesis | Thesis | |||
| Thesis | |||||
Sample Two-Year Pathway
The two-year pathway requires that a student has completed Calculus I, an Introduction to Statistics course, and one of: Calculus II OR Linear Algebra.
| First Year | |||||
|---|---|---|---|---|---|
| Fall Term | ISP | Spring Term | |||
| Dealing with Data II | ISP | Linear Algebra OR Calculus 2 | |||
| Probability I & II | Data Visualization | ||||
| R for Data Science | Statistical Learning | ||||
| Second Year | |||||
| Fall Term | ISP | Spring Term | |||
| Linear Models | ISP | Financial Markets Modeling using Machine Learning | |||
| Thesis | Thesis | Thesis | |||
Representative Senior Theses in Statistics
- Spatial Modeling of the Relative Abundance of Bird Populations in Peninsular Florida Using Citizen Science Data
- 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
- Studying the Effects of Score Differential on Offensive Output When Evaluating Team Performance in Soccer
- Effects of Multicollinearity in Variable Selection Algorithms
- Teaching Statistics through Mobile Applications
- Dynamics of Protein Synthesis with Autoregulation: A Computational Biology Approach