Data Science
Overview
Data Science, the art and science of extracting information from large datasets, is an interdisciplinary field that offers many exciting and challenging opportunities. Formed from the amalgamation of Computer Science, Statistics, and Mathematics, Data Science aims to solve the world's problems by revealing information hidden in what has become known as "Big Data". As today’s businesses and IT systems continue to produce massive and ever-increasing amounts of digital data, the need for data scientists is greater than ever. Whether you are interested in analyzing consumer transactions, tweets, call data records, text corpuses, or sounds in nature, or creating stunning data visualizations, you will find that the concepts, techniques, and tools covered in our Data Science program will be extremely employable in a wide range of industrial domains and disciplines where data scientists are in high demand. These skills will also form a strong foundation for advanced graduate studies.
The area of concentration starts with courses that form the foundational knowledge and skills in mathematics, statistics, computer programming, databases, and data munging, followed by more advanced courses on statistical models, algorithms, distributed computing, software engineering, and machine learning. Students will then have the option to carry out an applied data science thesis in any domain of their interest under the supervision of one or more interdisciplinary faculty, including from the Divisions of Natural Sciences, Humanities, or Social Sciences depending on the area of focus.
Faculty in Data Science
Melissa Crow, Instructor of Statistics
David Gillman, Associate Professor of Computer Science
Bernhard Klingenberg, Professor of Statistics/Interim Director of Data Science
Patrick McDonald, Professor of Mathematics/Vice Chair of the Faculty
Tiago Perez, Assistant Professor of Data Science
Eirini Poimenidou, Professor of Mathematics (On Leave)
Tania Roy, Associate Professor of Human Centered Computing
Andrey Skripnikov, Assistant Professor of Statistics
Necmettin Yildirim, Professor of Mathematics/Soo Bong Chae Chair of Applied Mathematics
Requirements for the AOC in Data Science
A minimum of sixteen (16) academic units.
Code | Title |
---|---|
Core Requirements | |
Calculus I* | |
Introduction to Programming in Python* | |
Intermediate Python | |
or CSCI 2400 | Object-Oriented Programming |
Dealing with Data I* | |
Dealing with Data II | |
Probability I and Probability II* (Mods I & II) | |
Linear Algebra | |
Data Science Area Courses | |
Algorithms for Data Science | |
Databases for Data Science | |
Software Engineering in Data Science | |
Applied Linear Models | |
Artificial Intelligence and Data Mining | |
Ethics in Data Science | |
Thesis Preparation Courses | |
Select three elective courses either from either Pool A or Pool B: ^{1} | |
Pool A: | |
3xxx and 4xxx courses | In CSCI, STAN, MATH or the Graduate Program |
Distributed Computing | |
Pool B: | |
3xxx and 4xxx courses | In Humanities, Social Sciences, or Natural Sciences not in Pool A |
Additional Requirements | |
Data Science Internship or Community Project ^{2} | |
Senior Thesis or Senior Capstone Project in Data Science, and Baccalaureate Exam |
- ^{ 1 }
Students will conduct their thesis either as a theoretical/methodological Data Science thesis, or as an applied Data Science thesis that combines skills acquired earlier in the program with skills and knowledge that will be gained by taking cross-disciplinary courses (e.g. courses from Humanities or Social Sciences). Hence, the student is expected to select all three elective courses either from Pool A or from Pool B.
- ^{ 2 }
Data Science is a practical field. As such, each AOC student is expected to do an internship or a community project in applied Data Science, preferably following the completion of their third year in the program. The internship or project topic must be approved by the student’s advisor or internship coordinator.
Requirements for a Secondary Field in Data Science
A minimum of eight (8) academic units.
Code | Title |
---|---|
Core Requirements | |
Introduction to Programming in Python* | |
Intermediate Python | |
or CSCI 2400 | Object-Oriented Programming |
Dealing with Data I* | |
Dealing with Data II | |
Databases for Data Science | |
Artificial Intelligence and Data Mining | |
Electives | |
Select two from the following examples: | |
Algorithms for Data Science | |
Software Engineering in Data Science | |
Applied Linear Models | |
Distributed Computing | |
Statistical Learning | |
Data Visualization and Communication |
Sample Pathways
The sample pathway starts with the first-year introductory courses for Data Science including three courses that involve programming in Python and R and a two-module course sequence on probability. These courses are intended to provide prospective Data Science students an initial view into the discipline and allow them to decide whether they would like to pursue the AOC. In the second year, students are expected to take the remaining foundation courses (Calculus and Linear Algebra), a Python continuation course, and also three of the core courses of Data Science (Databases, Algorithms, and Software Engineering). With this background, students can go on to take the remaining core courses of Data Science and elective courses oriented towards their thesis.
Sample Four-Year Pathway
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall Term | Spring Term | ||||||
Dealing with Data 1 | Dealing with Data 2 | ||||||
Intro. to Programming in Python | CYC 1 | ||||||
Probability 1 & 2 | CYC 2 | ||||||
Second Year | |||||||
Fall Term | Spring Term | ||||||
Calculus 1 | Linear Algebra | ||||||
Databases for Data Science | Algorithms for Data Science | ||||||
Intermediate Python or Object-Oriented Programming | Software Eng. for Data Science | ||||||
Third Year | |||||||
Fall Term | Spring Term | Summer | |||||
Applied Linear Models | Artificial Intelligence and Data Mining | Internship or Community Project | |||||
Ethics in Data Science | Elective 2 | ||||||
Elective 1 | Elective 3 | ||||||
Fourth Year | |||||||
Fall Term | ISP | Spring Term | |||||
Thesis | Thesis | Thesis |
Sample Two-Year Pathway
This pathway assumes a student has completed two statistics courses, two programming courses (at least one in Python), Calculus 1, and Linear Algebra.
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall Term | Spring Term | Summer | |||||
Probability 1 & 2 | Algorithms for Data Science | Internship or Community Project | |||||
Databases for Data Science | Software Eng. for Data Science | ||||||
Ethics in Data Science | Elective 1 | ||||||
Second Year | |||||||
Fall Term | ISP | Spring Term | |||||
Applied Linear Models | Thesis | Artificial Intelligence and Data Mining | |||||
Elective 2 | Elective 3 | ||||||
Thesis | Thesis |
Requirements for 3+2 Pathway for Combined Undergraduate + Graduate Degrees (BA and MS in Data Science)
This pathway is intended for high-performing students who aspire to complete a combined sequence of undergraduate + graduate studies faster than the normal duration of 6 years. This is for current and future New College majors (other than Data Science) who would like to pursue a graduate degree in Data Science. Undergraduate students in this track can take additional courses in their third and fourth years from the Data Science graduate program, followed by the second and final year of the Graduate Program itself, earning the two degrees by the end of fifth year.
A student is eligible for this pathway after entering the undergraduate program and showing sufficiently high performance. In other words, acceptance into this 3+2 pathway is not automatically granted at the time of undergraduate admission; students will have to apply and seek admission only after they satisfy certain minimum conditions:
- Complete 2 years of study with no Unsatisfactory grade
- Complete prerequisite courses (see below)
- Be recommended for the 3+2 pathway by a faculty member
The Data Science Graduate Program admissions committee will also review applications for this pathway and make admission decisions. Other application requirements of the Graduate Program will still apply.
Code | Title |
---|---|
Prerequisites | |
In addition to the chosen AOC requirements, the following courses must be completed during the first two years of undergraduate study: ^{1} | |
Calculus I* | |
Calculus II* | |
Introduction to Programming in Python* | |
Intermediate Python | |
or CSCI 2400 | Object-Oriented Programming |
Probability I | |
Probability II* | |
Linear Algebra | |
Third Year: Fall Term | |
Applied Statistics I | |
Data Munging and Exploratory Data Analysis | |
Fourth Year: Fall Term ^{2} | |
Algorithms for Data Science | |
Databases for Data Science | |
Industrial Seminar Series I | |
Fourth Year: January Interterm | |
Industrial Workshops | |
Fourth Year: Spring Term | |
Applied Statistics II | |
Data Visualization | |
Applied Machine Learning | |
Distributed Computing | |
Industrial Seminar Series II | |
Fourth Year: Summer or Fifth Year: January Interterm | |
Industrial Practicum I | |
Fifth Year: Fall Term | |
Advanced Applied Statistics | |
Advanced Applied Computing | |
Practical Data Science | |
Industrial Seminar Series III | |
Fifth Year: Spring Term | |
Industrial Practicum II |
- ^{ 1 }
If the student’s AOC already includes some or all of the prerequisite courses, these courses can be counted towards fulfilling the prerequisite course requirements for the 3+2 track. They also count towards satisfying the IDC 5100 Introduction to Data Science Bootcamp course in the graduate program. However, IDC courses must be taken in addition to the AOC requirements and can only be counted towards the graduate program requirements.
- ^{ 2 }
The undergraduate program is completed after the Fourth Year Fall Term.
Data Science Facilities
New College has a number of servers that support students and faculty in the computer science and data science programs. These include 5 HP physical servers with NVIDIA graphics processing units (Tesla, Titan X, and 1080 Ti); 1 SuperMicro physical server with 4 NVIDIA graphics processing units (Quadro RTX 6000); 1 SuperMicro physical server with 4 NVIDIA graphics processing units (RTX A5000 and 1080 Ti); and 12 virtual servers used in a variety of computer science, data science, and statistics courses.