### Learning Outcomes

Upon graduation from Dickinson, Data Analytics majors will be able to:

- build a data analysis pipeline through collecting, compiling, curating, and managing data;
- analyze data using appropriate statistical, programming, and computational tools;
- evaluate ethical aspects of data collection and analysis;
- communicate clearly and accurately about data-oriented questions with experts as well as the general public.

### Major

13 courses and an experiential component

*Core Requirements*

DATA 180, 200, and 300

PHIL 258

*Mathematics Requirements*

MATH 170, 171, 225, and 325 (or ECON 298)

*Computer Science Requirement*

COMP 130 or 132

*Three-course Discipline Sequence Requirement*

The three-course sequence can be selected from the list below. The pre-approved three-course sequences below come from nineteen different departments and all three academic divisions at the college. These courses are subject to change by departments as needed. Alternatively, another three-course sequence that is proposed by the student and approved by the data analytics committee can satisfy this requirement.

*Senior Seminar in Data Analytics*

DATA 400

*Experiential Component*

There are four ways to complete the data analytics experiential component. Students can complete the experience during any summer or semester after they take DATA 180 and COMP 130 in the following ways.

- Internship (with INP designation): The student participates in an internship experience using data analytics skills, broadly defined, under the supervision of a mentor in the field.
- Research (with REXP designation): The student participates in a scholarly scientific research experience using data analytics skills, broadly defined, under the supervision of a professional researcher.
- Independent Study/Research or Student-Faculty Research (500, 550, or 560 course): The student participates in a scholarly scientific study or research experience using data analytics skills, broadly defined, under the supervision of a professor.
- Study Abroad Program with Internship/Research Experience: Options in this category may vary depending on the study abroad program, so a student should consult with CGSE about internships while exploring study abroad programs.

**For more detailed information, please see the department's webpage dedicated to the experiential component.**

**Writing in the Discipline:** Data analytics graduates go on to write in a wide variety of styles (such as graphical/visual representations, memos, reports, academic papers, code, and data documentation, among others) for a range of audiences (developers, internal/external technical readers, and other stakeholders). The all-college WiD requirement in the data analytics major is fulfilled through a series of courses, where writing naturally occurs, and creation of a writing portfolio. This thread provides students with practice and feedback on several types of writing that are relevant to the discipline. Specifically, students build a writing portfolio through the completion of assignments in DATA 198, DATA 200, DATA 300, and DATA 400. Upon completion of these four courses and successful submission of a writing portfolio, students satisfy the WiD requirement.

**Pre-approved three-course sequences**

Please refer to the pre-approved three-course sequences list.

### Suggested curricular flow through the major

The following curricular guidelines will help you pace your progress through the major. While no specific course must be taken in any given semester, the vertical structure of the program requires that you successfully complete prerequisites for admission to higher-level classes in a timely manner. A summary of the suggested curricular flow is provided below.

- Introductory Requirements (completed by beginning of 2nd year spring):
- MATH 170: Single Variable Calculus
- MATH 171: Multivariable Calculus
- DATA 180: Introduction to Data Science
- COMP 130: Introduction to Computing or COMP 132: Principles of Object-Oriented Design
- Discipline Course I

- Intermediate Requirements (completed by beginning of 3rd year spring):
- MATH 225: Probability and Statistics I
- DATA 200: Data Systems for Data Analytics
- PHIL 258: Philosophy of DATA
- Discipline Course II

- Advanced Requirements (completed by beginning of 4th year spring):
- MATH 325: Probability and Statistics II or ECON 298: Econometrics
- DATA 300: Statistical and Machine Learning
- Discipline Course III

- Senior Seminar (completed during 4th year spring):
- DATA 400: Data Analytics Capstone

There are many possible paths through the data analytics major. Which path to take depends on the student’s prior coursework and placement (in mathematics and computer science). Below, we show three paths with different entry points based upon your mathematics placement. With careful planning, all three paths allow the possibility for students to spend at least one semester abroad. All paths also require an experiential component (typically completed over the summer) not included in the course plans.A blank line in between courses in the table below indicates that the courses above are prerequisites for the courses below.

Math Entry Point |
First Year |
Sophomore Year |
Junior Year |
Senior Year |

MATH 151 |
MATH 151, |
MATH 171, |
Study Abroad, DATA 198, DATA 300, Discipline II |
MATH 325*, Discipline III DATA 400 |

MATH 170 |
MATH 170, MATH 171, |
MATH 225, DATA 200, Discipline I MATH 325* |
Study Abroad, DATA 198, DATA 300, Discipline II |
Discipline III, DATA 400 |

MATH 171 |
MATH 171, COMP 130 (or 132), DATA 180 |
MATH 225, DATA 200, Discipline I MATH 325* |
Study Abroad, DATA 198, DATA 300, Discipline II |
Discipline III, DATA 400 |

*Students may take ECON 298 instead of MATH 325, but this adds ECON 111 and ECON 112 to the curriculum.

### Independent study and independent research

Each faculty member has special fields of study and will usually be available for advice in that area.

### Courses

101 Special Topics

Topics to be announced when offered.*Prerequisite: Dependent upon topic.*

180 Introduction to Data Science

An introduction to the principles and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervised machine learning. The course includes an introduction to the R statistical programming language.* Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. Offered every semester. *

Attributes: Appropriate for First-Year, ENST Foundations (ESFN)

198 Philosophy of Data

This an introduction to philosophical issues arising in data science. Students will discuss, read and write about some important ethical issues that arise in the practice of data sciences, such as discrimination, privacy, consent, trust, and justice. To help clarify those issues, students will also learn about some connected issues in the epistemology and metaphysics of data science, such as the nature of statistical inference and of algorithms. *Prerequisites: MATH 121 or DATA/COMP/MATH 180 or ECON 298. This course is cross-listed as PHIL 258. Offered every semester.*

Attributes: Ethics Elective, Humanities

200 Data Systems for Data Analytics

A comprehensive introduction to the access, structure, storage, and representation of data as it applies to data analytics. The tabular data model, relational data model, and hierarchical data model are studied. Topics include the use of structured query language (SQL) to extract and manipulate data from a relational database, APIs to extract information from web services, and methodologies for processing unstructured data. The primary programming language used in the course is Python.*Prerequisite: COMP 130 or 132, and DATA/COMP/MATH 180. Cross-listed with COMP 200. Offered every semester. *

201 Special Topics

Topics to be announced when offered.*Prerequisite: Dependent upon topic.*

300 Statistical and Machine Learning

An introduction to the fundamental concepts and methods for statistical and machine learning. Focus is given on providing both a theoretical foundation and the practical skills needed to apply machine learning to a variety of applications in various disciplines. Topics include supervised methods such as regression and classification, and unsupervised methods such as clustering and dimensionality reduction.*Prerequisite: COMP/DATA 200 and MATH 225. Offered every semester.*

301 Special Topics

Topics to be announced when offered.*Prerequisite: Dependent upon topic.*

400 Data Analytics Capstone

A capstone course that provides students with an opportunity to apply the data analytics knowledge they have acquired to independent research projects. At least one of the projects must be derived from the chosen discipline specific electives. Students will get experience in all aspects of solving real-world problems, including project planning, consideration of legal and ethical issues, collecting and processing data, analyzing and interpreting results, writing reports, and giving presentations.*Prerequisites: DATA 300, completion of ECON 298 or concurrent registration in MATH 325, DATA 198/PHIL 258 and the three-course disciplinary sequence. Offered every spring.*