Undergraduate Courses
The Data Science Bachelors of Science degree requires completion of a minimum of 128 credits, with at least 64 credits counting towards the major, including 14 four-credit courses covering the foundational, methodological and applied dimensions of the data science, as well as completion of a four-credit capstone. The final four credits may be satisfied through any course that meets CDS requirements.

Register for classes by heading to BU Student Link .

Mathematical & Computational Foundations
CDS DS 110: Introduction to Data Science with Python
CDS DS 110 Introduction to Data Science with Python
4 credits. Either sem.

CDS DS 110 is the first in a two-course sequence (leading to CDS DS 210) that builds students' competence in computing techniques central to data science. Students will use Python to explore fundamental CS concepts and processes used in data science with a focus on descriptive data analysis, including data structures, development of functions and more advanced recursion, object- oriented programming, data processing and data visualization. Numpy, pandas, and matplotlib will be used to analyze real-world data. Prior experience with Python is not required.

Section A1, FALL 2022 Sep 7th to Dec 12th
Gold
Lecture
MWF
03:35:00 PM–04:25:00 PM
CAS 313

Section A2, FALL 2022 Sep 7th to Dec 7th
Gold
Discussion
W
04:40:00 PM–05:30:00 PM
CGS 315

Section A3, FALL 2022 Sep 7th to Dec 7th
Gold
Discussion
W
11:15:00 AM–12:05:00 PM
CGS 123

Section A4, FALL 2022 Sep 7th to Dec 7th
Gold
Discussion
W
01:25:00 PM–02:15:00 PM
MUG 203

Section A5, FALL 2022 Sep 7th to Dec 7th
Gold
Discussion
W
02:30:00 PM–03:20:00 PM
CGS 311

CDS DS 120: Foundations of Data Science
CDS DS 120 Foundations of Data Science
4 credits. Either sem. Basic knowledge of a programming language such as Python is expected

The first in a 3-course sequence (with CDS DS 121 and CDS DS 122) that introduces students to theoretical foundations of Data Science. Introduction to key concepts from Calculus (differentiation and integration), Probability (discrete and continuous random variables) and Linear Algebra (vector spaces, matrices, and linear systems). The course links mathematics and computational thinking through problem sets requiring students to answer mathematically- posed questions using computation.

Section A1, FALL 2022 Sep 7th to Dec 12th
Chatterjee
Lecture
MWF
10:10:00 AM–11:00:00 AM
CGS 511

Section A2, FALL 2022 Sep 12th to Dec 12th
Chatterjee
Discussion
M
11:15:00 AM–12:05:00 PM
CGS 121

Section A3, FALL 2022 Sep 12th to Dec 12th
Chatterjee
Discussion
M
12:20:00 PM–01:10:00 PM
CGS 515

Section A4, FALL 2022 Sep 12th to Dec 12th
Chatterjee
Discussion
M
01:25:00 PM–02:15:00 PM
CGS 121

Section A5, FALL 2022 Sep 12th to Dec 12th
Chatterjee
Discussion
M
02:30:00 PM–03:20:00 PM
CGS 323

CDS DS 121: Foundations of Data Science II
CDS DS 121 Foundations of Data Science II
4 credits. Either sem. CDSDS120 OR equivalent; CDSDS110 OR equivalent.

BU Hub Learn More Quantitative Reasoning I Digital/Multimedia Expression Critical Thinking
CDS 121 is the second in the three-course sequence (CDS DS 120, 121, 122) that introduces students to theoretical foundations of Data Science. DS 121 covers an introduction to key concepts from Linear Algebra (vector space, independence, orthogonality and matrix factorizations). The DS theme running through the course is exploratory data analysis, enabling a better understanding of the data at hand. The course will link mathematical concepts with computational thinking, specifically through the use of problem sets that require students to answer mathematically-posed questions using computation. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning I, Digital/Multimedia Expression, Critical Thinking.

Section A1, FALL 2022 Sep 6th to Dec 8th
Varia
Lecture
TR
03:30:00 PM–04:45:00 PM
PSY B33

Section A2, FALL 2022 Sep 12th to Dec 12th
Varia
Discussion
M
01:25:00 PM–02:15:00 PM
EOP 266

Section A3, FALL 2022 Sep 12th to Dec 12th
Varia
Discussion
M
02:30:00 PM–03:20:00 PM
EOP 266

Section A4, FALL 2022 Sep 12th to Dec 12th
Varia
Discussion
M
04:40:00 PM–05:30:00 PM
EOP 260

CDS DS 122: Foundations of Data Science III
CDS DS 122 Foundations of Data Science III
4 credits. Either sem. CDSDS121 OR equivalent

CDS DS 122 is the third in a three-course sequence (with CDS DS 120 and CDS DS 121) that introduces students to theoretical foundations of Data Science. DS 122 covers topics in probability (including common probability distributions, conditional probability, independence, Bayes Theorem, prior and posterior distributions, sampling, and the central limit theorem), statistics (including maximum likelihood), basic numerical optimization (including gradient descent methods), and topics in calculus (including sequences and series). Knowledge of a programming language (such as Python) is expected. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.

Data Science Core
CDS DS 210: Programming for Data Science
CDS DS 210 Programming for Data Science
4 credits. Either sem. CDSDS110 OR equivalent

BU Hub Learn More Quantitative Reasoning II Digital/Multimedia Expression Creativity/Innovation
Second course in the CDS DS-110-210 sequence. The first half of DS 210 continues the Python programming experience begun in DS-110, with enhanced focus on machine learning applications. The second half of the course introduces students to compiled programming languages, such as Rust, Go and Java, suitable for building large projects. Basic data structures (stacks, queues, priority queues, binary search trees), techniques for representing graphs, and basic graph algorithms will be explored. Concepts are developed and reinforced through consideration of data-driven inquiries in real-world settings. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Digital/Multimedia Expression, Creativity/Innovation.

Section A1, FALL 2022 Sep 7th to Dec 12th
Kontothanass
Lecture
MWF
01:25:00 PM–02:15:00 PM
CAS B18

Section A2, FALL 2022 Sep 9th to Dec 9th
Kontothanass
Discussion
F
09:05:00 AM–09:55:00 AM
EOP 266

Section A3, FALL 2022 Sep 9th to Dec 9th
Kontothanass
Discussion
F
10:10:00 AM–11:00:00 AM
CGS 321

Section A4, FALL 2022 Sep 9th to Dec 9th
Kontothanass
Discussion
F
11:15:00 AM–12:05:00 PM
IEC B10

CDS DS 310: Data Mechanics
CDS DS 310 Data Mechanics
4 credits. Either sem. CDSDS110 AND CDSDS210

Course focused on developing students' capacity to design and implement data flows and the computational workflows meant to inform online/offline decision-making within large systems. Students explore the data science lifecycle, including question formulation, data collection and cleaning (data wrangling), exploratory data analysis and visualization, statistical inference and prediction, and decision-making. Relational (SQL) and MapReduce (noSQL) paradigms used to assemble analysis, optimization, and decision-making algorithms to track and scale data.

Section A1, FALL 2022 Sep 6th to Dec 8th
Seferlis
Lecture
TR
02:00:00 PM–03:15:00 PM
PSY B51

Section A2, FALL 2022 Sep 9th to Dec 9th
Seferlis
Discussion
F
10:10:00 AM–11:00:00 AM
CGS 421

Section A3, FALL 2022 Sep 9th to Dec 9th
Seferlis
Discussion
F
11:15:00 AM–12:05:00 PM
CGS 111B

CDS DS 320: Algorithms for Data Science
CDS DS 320 Algorithms for Data Science
4 credits. Either sem. CDSDS110 or equivalent AND CDSDS122 or equivalent

This course covers the fundamental principles underlying the design and analysis of algorithms. We will walk through classical design methods, such as greedy algorithms, design and conquer, and dynamic programming, focusing on applications in data science. We will also study algorithmic methods more specific to data science and machine learning. The course places a particular emphasis on algorithmic efficiency, crucial with large and/or streaming data sets, for which multiple scans of data are infeasible, including the use of approximation and randomized algorithms. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.

CDS DS 340: Introduction to Machine Learning & AI
CDS DS 340 Introduction to Machine Learning and AI
4 credits. Either sem. A second course in programming (CDSDS210 or equivalent) should be taken prior to this class, and algorithms (CDSDS320 or equivalent) shouldbe taken simultaneously with or prior to this class.

This course instructs students in key algorithms for classic artificial intelligence (AI) and modern machine learning (ML). Along the way, we seek to explore what kinds of problems these techniques are good and bad at, and build intuition for what makes a good search or machine learning problem. The primary assessment tools will be programming problem sets in Python, using Jupyter notebooks. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Quantitative Reasoning II, Critical Thinking.

Section A1, FALL 2022 Sep 6th to Dec 8th
Gold
Lecture
TR
11:00:00 AM–12:15:00 PM
EPC 205

Section A2, FALL 2022 Sep 9th to Dec 9th
Gold
Discussion
F
01:25:00 PM–02:15:00 PM
MUG 203

Section A3, FALL 2022 Sep 9th to Dec 9th
Gold
Discussion
F
02:30:00 PM–03:20:00 PM
EOP 260

Section A4, FALL 2022 Sep 9th to Dec 9th
Gold
Discussion
F
03:35:00 PM–04:25:00 PM
MUG 203

CDS DS 380: Data, Society and Ethics
CDS DS 380 Data, Society and Ethics
4 credits. Either sem. CDSDS110 AND CDSDS320

This course develops students' ability to critically examine and question the interplay between data science and computational technologies on the one hand, and society and public policy on the other. Students will complete exercises to demonstrate their facility with key ethics tools and techniques, and analyze a series of real-world case studies presented alongside ethical tools and analyses that are useful both for staying alert to emerging ethical challenges and responding to them as they arise in both employment settings and everyday life. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Social Inquiry II, Research and Information Literacy.

CDS DS 482: Responsible AI, Law, Ethics & Society
CDS DS 482 Responsible AI, Law, Ethics & Society
4 credits. Either sem. CDSDS100/CDSDS110 (Intro to data science OR equivalent) and CDSDS340 (intro to ML and AI OR equivalent)

Course page: https://learn.responsibly.ai/. Instructor: shlomi@bu.edu. This course addresses the deployment of Artificial Intelligence systems in multiple domains of society, and how this raises fundamental challenges and concerns, such as accountability, liability, fairness, transparency and privacy. Tackling these challenges calls for an interdisciplinary approach: deconstructing these issues by discipline and reconstructing with an integrated mindset, principles and practices between data science, ethics and law. This unique course will bring together students from either computing and data science disciplines or law and public policy disciplines from multiple institution. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry II, Ethical Reasoning, Teamwork/Collaboration.

Electives
Data Science Electives: at least four courses with at least one course in each of the three competencies listed under the Methodology or In-The-Field track. It will be important for students pursuing the major to think critically about a chosen pathway, taking into account course prerequisites. If students do not meet stated course pre-reqs, they will be responsible for obtaining permission from professors to take desired course(s).

CDS DS 563: Algorithmic Techniques for Taming Big Data (Methodology & In-the-Field Tracks)
CDS DS 563 Algorithmic Techniques for Taming Big Data
4 credits. Either sem. CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 OR ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivalent; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse

Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets.

CDS DS 537: Data Science for Conservation Decisions (In The Field track)
CDS DS 537 Data Science for Conservation Decisions
4 credits. Either sem. CASGE/EE 270 or equivalent; GE/EE 375 or equivalent; or consent of instructor.

BU Hub Learn More Quantitative Reasoning II Digital/Multimedia Expression Research and Information Literacy
This course covers the application of quantitative methods to support conservation decisions. Ecosystem value mapping, systematic conservation planning, policy instrument design, rigorous impact evaluation, decision theory, data visualization. Implementations in state-of-the-art open-source software. Real-life case studies from the U.S. and abroad. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Quantitative Reasoning II, Research and Information Literacy.

Section A1, FALL 2022 Sep 6th to Dec 8th
Nolte
Independent
TR
02:00:00 PM–03:15:00 PM
PSY B37

CDS DS 574: Algorithmic Mechanism Design
CDS DS 574 Algorithmic Mechanism Design
4 credits. Either sem. CDSDS122, CDSDS320, and CASMA581 or instructor approval

This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. Throughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design.

Section A1, FALL 2022 Sep 6th to Dec 8th
Goldner
Independent
TR
03:30:00 PM–04:45:00 PM
CAS 426

CDS DS 592: Special Topics: Mathematics
CDS DS 592 Special Topics in Mathematical and Computational Sciences
4 credits. Either sem. CASMA242 or equivalent AND CASMA581 or equivalent AND experience writing scientific code

Spring 2022: Stochastic Processes for the Design and Analysis of Algorithms. Introduction to interplay between stochastic processes and algorithms used in statistics and machine learning. Covers core stochastic processes concepts and use to design and analyze algorithms for sampling and large-scale stochastic optimization. Strong emphasis on practical implications of results.

For a full list of electives, click the button below
ELECTIVE LIST

Capstone
CDS DS 490: Directed Study
CDS DS 499: CDS Practicum Courses
CDS DS 499 CDS Practicum Course
4 credits. Either sem. consent of instructor

Courses engage students in interdisciplinary computing and data science projects. Projects may support CDS co-Labs, in partnership with internal and external organizations. Opportunities to connect computing and data sciences with domain-specific knowledge and expertise to advance co-Lab priorities.

CDS DS 519: Spark! Software Engineering X-Lab Practicum
CDS DS 519 Spark! Software Engineering X-Lab Practicum
4 credits. CDSDS310 OR CASCS411 OR equivalent experience in software developmentand consent of instructor. Consent provided upon successful completion of pass/fail diagnostic test to assess student readiness fo

BU Hub Learn More Ethical Reasoning Oral and/or Signed Communication Teamwork/Collaboration
This course offers students in computing disciplines the opportunity to apply their programming and system development skills by working on real-world projects provided from partnering organizations within and outside of BU, which are curated by Spark! The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete a software engineering project including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Oral and/or Signed Communication, Teamwork/Collaboration.

Section A1, FALL 2022 Sep 6th to Dec 8th
Mishr
Independent
TR
05:00:00 PM–06:15:00 PM

CDS DS 539: Spark! Data Science X-Lab Practicum
CDS DS 539 Spark! Data Science X-Lab Practicum
4 credits. Either sem. CASCS506 or equivalent preferred. CDSDSDS110 OR CASCS111 OR CASCS112 OR equivalent. CDSDS121 OR CASCS132 OR equivalent required. Or instructor consent which may involve pass/fail diagnostic test.

BU Hub Learn More Digital/Multimedia Expression Research and Information Literacy Teamwork/Collaboration
This course offers students in computing disciplines the opportunity to apply their data science skills by working on real-world projects provided from partnering organizations within and outside of BU, which are curated by Spark! The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include communications skills, collaborative work processes and an assessment of the ethical considerations of their work. All students participating in the course are expected to complete a data science project including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Research and Information Literacy, Teamwork/Collaboration.

CDS DS 549: Spark! Machine Learning X-Lab Practicum
CDS DS 549 Spark! Machine Learning X-Lab Practicum
4 credits. CDSDS340 OR CASCS542 OR CASCS505 OR CASCS585 OR consent of instructor. Consent may include the successful completion of a pass/fail diagnostic test that will assess student readiness to take the cours

BU Hub Learn More Ethical Reasoning Research and Information Literacy Teamwork/Collaboration
The Spark! Practicum offers students in computing disciplines the opportunity to apply their knowledge in algorithms, inferential analytics, and software development by working on real-world projects provided from partnering organizations within BU and from outside. The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete a project focused on an application of inferential analytics or machine learning, including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Research and Information Literacy, Teamwork/Collaboration.

Section A1, FALL 2022 Sep 6th to Dec 8th
Gardos
Independent
TR
02:00:00 PM–03:15:00 PM
MCS B52

Additional Courses
CDS DS 100: Data Speaks Louder than Words
CDS DS 100 Data Speaks Louder Than Words
4 credits. Either sem.

BU Hub Learn More Social Inquiry I Digital/Multimedia Expression Research and Information Literacy
This course introduces students to three perspectives that are fundamental to their ability to reason with data: critical thinking, inferential thinking, and computational thinking. Through data modeling and visualization, students will construct and communicate arguments that are rooted in data. The course expects only basic computer knowledge and teaches concepts and skills in computer programming (Python), linear regression, and statistical inference. The course delves into dilemmas surrounding data analysis, such as balancing individual privacy and social utility, and prepares students for the data driven world all around us. Students with interests from politics to sports, finance to journalism, entrepreneurship to smart cities, etc., can use the knowledge of data science they gain in this class to enhance those interests. Not to mention a grounding for students who want to pursue the field of data science itself. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry 1, Digital/Multimedia Expression, Research and Information Literacy.

Section A1, FALL 2022 Sep 6th to Dec 8th
White
Lecture
TR
03:30:00 PM–04:45:00 PM
CGS 511

Section A2, FALL 2022 Sep 9th to Dec 9th
White
Discussion
F
09:05:00 AM–09:55:00 AM
IEC B10

Section A3, FALL 2022 Sep 9th to Dec 9th
White
Discussion
F
10:10:00 AM–11:00:00 AM
IEC B10

Section A4, FALL 2022 Sep 9th to Dec 9th
White
Discussion
F
11:15:00 AM–12:05:00 PM
SHA 206

Section A5, FALL 2022 Sep 9th to Dec 9th
White
Discussion
F
01:25:00 PM–02:15:00 PM
EOP 262

CDS DS 199 A1: Confronting Surveillance: Living In Data Science’s Gaze
CDS DS 200: Undergraduate Internship In Data Science
CDS DS 200 Undergraduate Internship in Data Science
1 credits. Either sem.

This course is intended for undergraduate students interested in completing a summer internship in a data science industry company. For international students, this course is required to use CPT. This course comes with a tuition fee and is not repeatable. Please note that this course does not count toward major requirements, but the 1 credit received from the course does count toward the graduation requirement of 128 credits. A 2.0 GPA is required to participate in DS 200.

CDS DS 219: Software Engineering Career Prep Practicum Workshop
CDS DS 219 Software Engineering Career Prep Practicum Workshop
2 credits. Either sem.

Taught by industry software veterans who serve as Spark! Engineers in Residence in CDS, this 2-credit course presents students with an unadulterated view of what they need to know as they take on software engineering projects, in preparation for careers as full-stack software/data engineers. From a brass tacks perspective, the course covers a number of tactical topics. The course covers the language of modern software development including patterns, source control, pull requests, open source, containerization, virtualization, and agile vs waterfall development methods. Additionally, the course introduces students to a few of the specialized professional software engineering and DevOps roles in industry.

Section A1, FALL 2022 Sep 7th to Dec 7th
O'Malley
Independent
W
02:30:00 PM–04:15:00 PM
IEC B09B

CDS DS 280: Spark! UX/UI Design
CDS DS 280 Spark! UX/UI Design
2 credits. Either sem.

User experience design (UX) and user interface engineering (UI) is the design of user interfaces and visualization for computer, information, and data products focusing on maximizing usability and the user experience. Students complete a series of activities within the UX Design toolkit developed by BU Spark! in collaboration with the Red Hat UX Design team. Course covers basic steps of the UX Design process, beginning with user insights and problem definition, empathy maps around personas, to user stories and lo- fidelity wireframes or story maps, to brand design and high fidelity wireframes. Effective Spring 2022, this course fulfills a single unit in the following BU Hub area: Digital/Multimedia Expression.

Section A1, FALL 2022 Sep 7th to Dec 7th
Lyman
Independent
W
04:30:00 PM–06:15:00 PM
HAR 326

CDS DS 291: Spark! Exploring DEI in Tech
CDS DS 291 Spark! Exploring DEI in Tech
2 credits. Either sem.

This workshop will explore topics related to diversity, equity, inclusion, and justice (DEIJ) in the technology sector. The course will implement the theory and practice of DEIJ across the tech sector. Students will start by gaining a foundational understanding of the concepts of identity, intersectionality, and inclusive dialogue. They will then apply this framework to understand issues of DEIJ in the tech sector in Academia and business, and the different technology domains from AI to hardware. The second part of the course will be focused on allyship and action and includes a final project where students will use an intersectional lens to assess a problem they are passionate about and develop solutions they believe can have impact. Through this course, students will learn how to engage in and facilitate impactful discussions about diversity, equity, inclusion and justice. Effective Spring 2022, this course fulfills a single unit in the following BU Hub area: The Individual in Community.

Section A1, FALL 2022 Sep 7th to Dec 7th
Handricken
Independent
W
10:10:00 AM–11:55:00 AM
CGS 111B

CDS DS 290: Spark! Civic Tech Research Workshop
CDS DS 290 Spark! Civic Tech Research Design Workshop
2 credits.

This workshop focuses on how we learn from data. How do we identify and analyze relationships in our data? What conclusions can we draw from our data, and how applicable are our conclusions to broader contexts? How do we communicate effectively about our data and analyses? How can we be critical consumers of data and research, and identify issues and limitations in how data is used by data scientists, journalists, academics, and others? Effective Spring 2023, this course fulfills a single unit in the following BU Hub area: Research and Information Literacy

CDS DS 299 A1: Spark! Practicum Extension
CDS DS 299 B1: Agent-Based Modeling of People, Health and Environment