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{
"courses": [
{
"course_id": 1,
"college": "CDS",
"department": "DS",
"course_number": 100,
"course_name": "Data Speaks Louder Than Words",
"course_abstract": "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.",
"prereqs": [],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 2,
"college": "CDS",
"department": "DS",
"course_number": 110,
"course_name": "Introduction to Data Science with Python",
"course_abstract": "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. ",
"prereqs": [],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 3,
"college": "CDS",
"department": "DS",
"course_number": 120,
"course_name": "Foundations of Data Science",
"course_abstract": "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. ",
"prereqs":["programming language such as Python is expected"],
"coreqs": ["CDSDS110 OR equivalent"],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 4,
"college": "CDS",
"department": "DS",
"course_number": 121,
"course_name": "Foundations of Data Science II",
"course_abstract": "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.",
"prereqs":["CDSDS120 OR equivalent; CDSDS110 OR equivalent"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 5,
"college": "CDS",
"department": "DS",
"course_number": 122,
"course_name": "Foundations of Data Science III",
"course_abstract": "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.",
"prereqs":["CDSDS121 OR equivalent"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 6,
"college": "CDS",
"department": "DS",
"course_number": 199,
"course_name": "CDS Workshops (1 credit)",
"course_abstract": "DS 199 workshops provide students the opportunity to develop elective skills and competencies in computing and data science. Each workshop focuses on a subset of skills and competencies necessary for students to engage in particular projects and real-world experiences. Participation in projects pursued within specific co- Labs may require completion of specific workshops. DS 199 workshops will count for 1 credit.",
"prereqs":[],
"coreqs": [],
"credits": 1,
"semesters": []
},
{
"course_id": 7,
"college": "CDS",
"department": "DS",
"course_number": 200,
"course_name": "Undergraduate Internship in Data Science",
"course_abstract": "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.",
"prereqs":[],
"coreqs": [],
"credits": 1,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 8,
"college": "CDS",
"department": "DS",
"course_number": 209,
"course_name": "Spark! Software Engineering Immersion",
"course_abstract": "Students will be introduced to all concepts required to work on a modern web development project. This course is intentionally taught with very little prerequisite knowledge to enable students to begin learning these skills earlier in their college path. Students begin by learning basic skills required to build a functioning web application. During the second half of the course, students will be allocated to teams and provided a choice of projects to develop over the course of the semester. Students will submit their final application as their final project on the last day of classes.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 9,
"college": "CDS",
"department": "DS",
"course_number": 210,
"course_name": "Programming for Data Science",
"course_abstract": "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. ",
"prereqs":["CDSDS110 OR equivalent"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 10,
"college": "CDS",
"department": "DS",
"course_number": 219,
"course_name": "Software Engineering Career Prep Practicum Workshop",
"course_abstract": "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.",
"prereqs":[],
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 11,
"college": "CDS",
"department": "DS",
"course_number": 280,
"course_name": "Spark! UX/UI Design",
"course_abstract": "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.",
"prereqs":[],
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 12,
"college": "CDS",
"department": "DS",
"course_number": 287,
"course_name": "Spark! Diversity and Equity in Data Science Workshop",
"course_abstract": "Designed to introduce students to the issues of diversity and equity in data science. The first half of the course focuses on the larger sociological implications of these inequalities - why they happen and why they matter. The second half dives into the steps of data collection, analysis, and dissemination on a practical level, identifying the problem points and potential solutions at each level of the process. Effective Spring 2023, this course fulfills a single unit in the following BU Hub area: The Individual in Community ",
"prereqs":[],
"coreqs": [],
"credits": 99,
"semesters": []
},
{
"course_id": 13,
"college": "CDS",
"department": "DS",
"course_number": 288,
"course_name": "Spark! Workshop on Translating Computing & Data Science Concepts and Technologies through Storytelling",
"course_abstract": "This course will cover the basics of storytelling as applied to complex technology concepts, products, and outputs. Students will learn how to define the basic elements of a story and to craft compelling narratives using words, images, and other artifacts as applied to computing and data science topics and products.",
"prereqs":[],
"coreqs": [],
"credits": 2,
"semesters": []
},
{
"course_id": 14,
"college": "CDS",
"department": "DS",
"course_number": 290,
"course_name": "Spark! Civic Tech Research Design Workshop",
"course_abstract": "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",
"prereqs":[],
"coreqs": [],
"credits": 2,
"semesters": []
},
{
"course_id": 15,
"college": "CDS",
"department": "DS",
"course_number": 291,
"course_name": "Spark! Exploring DEI in Tech",
"course_abstract": "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.",
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 16,
"college": "CDS",
"department": "DS",
"course_number": 292,
"course_name": "Spark! Civic Tech Toolkit Workshop",
"course_abstract": "This workshop will cover essential programs, tools, and frequently used data sets necessary to work effectively on civic tech projects enabling greater interdisciplinary engagement and contextual understanding of the tools in an applied context. Tools will include working with GIS/ geospatial programming languages, gaining familiarity with commonly used libraries and packages in R and Python, and leveraging data visualization tools like Tableau, Flourish, and PowerBI. Additionally, the course will allow students to learn about and engage with commonly used civic tech data sets: census/ ACS data, elections data, land use and housing, and development data, data about criminal legal systems, and more. Effective Spring 2023, this course fulfills a single unit in the following BU Hub area: The Individual in Community.",
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 17,
"college": "CDS",
"department": "DS",
"course_number": 293,
"course_name": "Spark! Civic Tech Toolkit Workshop",
"course_abstract": "",
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 18,
"college": "CDS",
"department": "DS",
"course_number": 299,
"course_name": "CDS Workshops (2 credits)",
"course_abstract": "DS299 workshops provide students the opportunity to develop elective skills and competencies in computing and data science. Each workshop focuses on a subset of skills and competencies necessary for students to engage in particular projects and real-world experiences. Participation in projects pursued within specific co-Labs may require completion of specific workshops. See CDS website for Spring 2022 course information: https://www.bu.edu/cds-faculty/academics/undergraduate/courses/",
"coreqs": [],
"credits": 2,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 19,
"college": "CDS",
"department": "DS",
"course_number": 310,
"course_name": "Data Mechanics",
"course_abstract": "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.",
"prereqs":["CDSDS110 AND CDSDS210"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 20,
"college": "CDS",
"department": "DS",
"course_number": 320,
"course_name": "Algorithms for Data Science",
"course_abstract": "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.",
"prereqs":["CDSDS110 or equivalent AND CDSDS122 or equivalent"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 21,
"college": "CDS",
"department": "DS",
"course_number": 340,
"course_name": "Introduction to Machine Learning and AI",
"course_abstract": "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.",
"prereqs":["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."],
"coreqs": ["CDSDS320 or equivalent can be taken as a co-requisite for this course."],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 22,
"college": "CDS",
"department": "DS",
"course_number": 380,
"course_name": "Data, Society and Ethics",
"course_abstract": "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. ",
"prereqs":["CDSDS110 AND CDSDS320"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 23,
"college": "CDS",
"department": "DS",
"course_number": 381,
"course_name": "Social Justice for Data Science",
"course_abstract": "Society is becoming increasingly digitized and datafied. Important decisions impacting criminal justice, housing, finance, labor, healthcare, and education are frequently determined by or are aided by artificially intelligent algorithmic technologies that are built and trained on large datasets. The rise in these technologies presents a challenge for social justice. Though often presented as neutral decision aids, these technologies often produce harmful predictions that operate to reinforce old legacies of racial, class, gender, and heteropatriarchal subordination. Datafication practices, computational techniques, legal doctrine, and policy play a key role in facilitating these disparate outcomes. This course will center on the complicated relationship between social justice and data science. The course will introduce students to the historical and current role of datafication and computation practices in social subordination. Students will leave the course having developed the skill set needed to identify and critically engage with the social justice challenges posed by these new technologies",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 24,
"college": "CDS",
"department": "DS",
"course_number": 453,
"course_name": "Crypto for Data Science",
"course_abstract": "CDS DS 453 investigates techniques for performing trustworthy data analyses without a trusted party, and for conducting data science without data. The first half of the course investigates cryptocurrencies, the blockchain technology underpinning them, and the incentives for each participant, while the second half of the course focuses on privacy and anonymity using advanced tools from cryptography. The course concludes with a broader exploration into the power of conducting data science without being able to see the underlying data.",
"prereqs":["DS-122 and DS-320, or equivalent."],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 25,
"college": "CDS",
"department": "DS",
"course_number": 457,
"course_name": "Law for Algorithms",
"course_abstract": "Algorithms - those information-processing machines designed by humans - reach ever more deeply into our lives, creating alternate and sometimes enhanced manifestations of social and biological processes. In doing so, algorithms yield powerful levers for good and ill amidst a sea of unforeseen consequences. This crosscutting and interdisciplinary course investigates several aspects of algorithms and their impact on society and law. Specifically, the course connects concepts of proof, verifiability, privacy, security, trust, and randomness in computer science with legal concepts of autonomy, consent, governance, and liability, and examines interests at the evolving intersection of technology and the law. Grades will be based on a combination of short weekly reflection papers and a final project, to be completed collaboratively in mixed teams of law and computer and data science students. This course will include attendees from the computer science faculty, students and scholars based at Boston University and UC Berkeley.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["2nd sem"]
},
{
"course_id": 26,
"college": "CDS",
"department": "DS",
"course_number": 471,
"course_name": "Spark! Technology Innovation Practicum",
"course_abstract": "",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 27,
"college": "CDS",
"department": "DS",
"course_number": 481,
"course_name": "Spark! Data Science for Good: Topics in Civic Tech",
"course_abstract": "This course enables students to tackle real world data challenges related to a more equitable and just society. Students will work in teams on projects addressing pressing societal challenges in the public sphere, provided by partners from the public sector. Course emphasizes teamwork, client/project management, data collection/engineering, analytics and/or software development, testing and delivery of technical artifacts, and research and presentation of final deliverables",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 28,
"college": "CDS",
"department": "DS",
"course_number": 482,
"course_name": "Responsible AI, Law, Ethics & Society",
"course_abstract": "Course page: https://learn.responsibly.ai/. Instructor: [email protected]. 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.",
"prereqs":["CDSDS100/CDSDS110 (Intro to data science OR equivalent) and CDSDS340 (intro to ML and AI OR equivalent)"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 29,
"college": "CDS",
"department": "DS",
"course_number": 488,
"course_name": "Spark! UX Design X-Lab Practicum",
"course_abstract": "This course gives students an opportunity to apply methods and practices of user experience design to real-world projects. Students work in teams to address needs of industry partners for applying interactive software to solve practical problems. Addresses all phases of the user experience design process from user research and discovery to design and validation, with a focus on mastering techniques and methods for learning about users, applying design thinking methods to conceive and iterate on solutions, and validating designs through user testing and feedback.",
"prereqs":["CDSDS280 OR equivalent"],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 30,
"college": "CDS",
"department": "DS",
"course_number": 490,
"course_name": "Directed Study in Computing & Data Sciences ",
"course_abstract": "Directed study in Computing & Data Sciences provides students the opportunity to complete directed research in a selected topic not covered in a regularly scheduled course under the supervision of a faculty member. Student and supervising faculty member arrange and document expectations and requirements. Examples include in-depth study of a special topic or independent research project.",
"prereqs":[],
"coreqs": [],
"credits": 99,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 31,
"college": "CDS",
"department": "DS",
"course_number": 499,
"course_name": "CDS Practicum Course",
"course_abstract": "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.",
"prereqs":["consent of instructor"],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 32,
"college": "CDS",
"department": "DS",
"course_number": 519,
"course_name": "Spark! Software Engineering X-Lab Practicum ",
"course_abstract": "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.",
"prereqs":["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"],
"coreqs": [],
"credits": 4,
"semesters": []
},
{
"course_id": 33,
"college": "CDS",
"department": "DS",
"course_number": 522,
"course_name": "Stochastic Methods for Algorithms",
"course_abstract": "Application of stochastic process theory to design and analyze algorithms used in statistics and machine learning, especially Markov chain Monte Carlo and stochastic optimization methods. Emphasizes connecting theoretical results to practice through combination of proofs, numerical experiments, and expository writing. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas: Writing-Intensive Course, Creativity/Innovation.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall"]
},
{
"course_id": 34,
"college": "CDS",
"department": "DS",
"course_number": 526,
"course_name": "Critical Reading in Biological Data Science",
"course_abstract": "The goal of this course is to provide students with a framework, skills, and knowledge to critically evaluate research in biological data science. Biological research is rarely unequivocal in its findings; students will learn to systematically identify the claims advanced in research papers and evaluate whether each claim is established beyond a reasonable doubt by supporting evidence. We will examine papers that both meet and fail this test. In today's biology, to properly examine a paper in this way it is increasingly important to engage with the data provided as supporting evidence, and to critically examine the computational approach. Students will work with published data and computational tools. Further, students will learn to identify the ideology implicit in each paper, to understand how ideology shapes both the research questions and approach, and to imagine the same research under an alternative mindset. Classes will be split into lectures on background material for each paper and group discussions. Students will work in small groups to write a report on each paper. Each student will work on a final project to produce a critical review of a broader topic in the field.",
"prereqs": ["CDSDS 120, 121, and 122 or equivalent; ENGBE 562 or equivalent or experience with computational biology"],
"coreqs":[],
"credits": 4,
"semesters": ["Fall"]
},
{
"course_id": 35,
"college": "CDS",
"department": "DS",
"course_number": 537,
"course_name": "Data Science for Conservation Decisions",
"course_abstract": "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.",
"prereqs": ["CASGE/EE 270 or equivalent; GE/EE 375 or equivalent; or consent of instructor."],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 36,
"college": "CDS",
"department": "DS",
"course_number": 539,
"course_name": "Spark! Data Science X-Lab Practicum",
"course_abstract": "",
"prereqs": ["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."],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 37,
"college": "CDS",
"department": "DS",
"course_number": 541,
"course_name": "Agent-Based Modeling of People, Health, and the Environment",
"course_abstract": "Agent-based models are an ideal tool for analyzing systems (cities, economies, road networks, forests) in which the outcomes of interest (illness rates, traffic problems, land use, violence) are shaped strongly by interactions among individual 'agents' (drivers, consumers, farmers, spouses) with each other and their environments. This course builds skills in the use of agent-based modeling using the NetLogo platform as a tool for analyzing complex human- environment problems. The course will emphasize the iterative model-building process -- forming research questions, building conceptual and then operational models, experimenting, and then refining -- to address current research problems. Students should expect to commit time outside of meetings to i) gaining comfort with NetLogo syntax; ii) reading current research in the areas of agent-based modeling and human environment and health problems; and iii) a team based approach to building models.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 38,
"college": "CDS",
"department": "DS",
"course_number": 549,
"course_name": "Spark! Machine Learning X-Lab Practicum ",
"course_abstract": "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.",
"prereqs": ["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 course"],
"coreqs":[],
"credits": 4,
"semesters": []
},
{
"course_id": 39,
"college": "CDS",
"department": "DS",
"course_number": 561,
"course_name": "Software Engineering Development on Modern Cloud Environments",
"course_abstract": "Most of today's organizations needing a technology solution look to satisfy their computing, storage and networking needs through one of the large public cloud providers. Unlike traditional environments where a company had to build its own infrastructure often at large time and monetary expense it can now rent what it needs at the click of a button. In this course we will provide hands on experience with one of the large public cloud platforms. In particular we will look into the different flavors of compute, storage and networking available, how best to use them to solve interesting problems, and how to do everything on a constrained budget. Students will get accounts and deliver project work on the public cloud while also learning some of the fundamental principles on how those different cloud systems work under the covers. It is recommended that students taking this class have learned the basic principles of Computer Systems such as those taught in DS210 and/or CS210.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 40,
"college": "CDS",
"department": "DS",
"course_number": 563,
"course_name": "Algorithmic Techniques for Taming Big Data",
"course_abstract": "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. ",
"prereqs": ["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"],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 41,
"college": "CDS",
"department": "DS",
"course_number": 574,
"course_name": "Algorithmic Mechanism Design",
"course_abstract": "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. ",
"prereqs": ["CDSDS122, CDSDS320, and CASMA581 or instructor approval"],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 42,
"college": "CDS",
"department": "DS",
"course_number": 587,
"course_name": "Data Science in Human Contexts",
"course_abstract": "Where do statistical and computational insights lose historic social contexts? What are the impacts of datafication on individuals and communities? How do social and technical systems reify or challenge social hierarchies? Through a survey of academic literature, community-produced knowledge and coverage of technology in the popular press, this course will explore these themes as they relate to labor and automation, surveillance and the legal system, social media governance, and digital inclusion.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 43,
"college": "CDS",
"department": "DS",
"course_number": 590,
"course_name": "CDS Research Initiation Seminar",
"course_abstract": "The first--year doctoral seminar is a required two--semester cohort--based course (4 credits) that must be taken during the first full academic year that a student enrolls in the PhD program in CDS. It is divided into two parts, each providing 2 credits. \"CDS Research Initiation Seminar\" is offered in the fall semester, and \"CDS Research Development Seminar\" is offered in the spring semester. The seminar serves three key purposes: 1. It introduces students to the scholarship of (and the rich set of research projects pursued by) the CDS faculty and their guests through colloquia pitched to a multidisciplinary audience. 2. It guides students through the challenging transition into the graduate program in CDS by introducing them to the variety of skills and capacities that are needed to succeed as a scholar. 3. It engenders a sense of community amongst the group of students entering the program as a cohort. 4 cr. Either sem.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 44,
"college": "CDS",
"department": "DS",
"course_number": 592,
"course_name": "Special Topics in Mathematical and Computational Sciences",
"course_abstract": "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. ",
"prereqs": ["CASMA242 or equivalent AND CASMA581 or equivalent AND experience writing scientific code"],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 45,
"college": "CDS",
"department": "DS",
"course_number": 593,
"course_name": "Special Topics in Data Science",
"course_abstract": "For Spring 2023 - Building AI Solutions for the Real World: a Case Study of Conversational Systems: Applying Artificial Intelligence (AI) technology in the real-world goes beyond implementing the latest and greatest neural network model to solve a prediction model. In this course, we look to explore how real- world constraints and considerations like ethics and liability affect the adoption of AI in real-world applications. We focus on conversational systems in various domains as the running use case throughout the course to explore various AI technologies from the shallow models of the 2000s to the more recent deep learning architectures. Upon completion of this course, students will be building different types of AI models, and gain a deeper understanding of what it takes to train, test, and deploy AI-based applications in real-world settings.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 46,
"college": "CDS",
"department": "DS",
"course_number": 594,
"course_name": "Special Topics in Economics, Management, and Information Sciences",
"course_abstract": "Coverage of a specific topic in relation to economics, management, and information sciences in data science. Topics vary semester to semester.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 47,
"college": "CDS",
"department": "DS",
"course_number": 595,
"course_name": "Special Topics in Physical and Engineering Sciences",
"course_abstract": "Coverage of a specific topic in relation to physical and engineering sciences in data science. Topics vary semester to semester. ",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 48,
"college": "CDS",
"department": "DS",
"course_number": 596,
"course_name": "Special Topics in Natural, Biological and Medical Sciences",
"course_abstract": "Coverage of a specific topic in relation to natural, biological and medical sciences in data science. Topics vary semester to semester.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 49,
"college": "CDS",
"department": "DS",
"course_number": 597,
"course_name": "Special Topics in Social and Behavioral Sciences",
"course_abstract": "Coverage of a specific topic in relation to social and behavioral sciences in data science. Topics vary semester to semester.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 50,
"college": "CDS",
"department": "DS",
"course_number": 598,
"course_name": "Special Topics in Media, Arts and Humanities",
"course_abstract": "Coverage of a specific topic in relation to media, arts and the humanities in data science. Topics vary semester to semester.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 51,
"college": "CDS",
"department": "DS",
"course_number": 599,
"course_name": "CDS Research Development Seminar",
"course_abstract": "The first--year doctoral seminar is a required two--semester cohort--based course (4 credits) that must be taken during the first full academic year that a student enrolls in the PhD program in CDS. It is divided into two parts, each providing 2 credits. \"CDS Research Initiation Seminar\" is offered in the fall semester, and \"CDS Research Development Seminar\" is offered in the spring semester. The seminar serves three key purposes: 1. It introduces students to the scholarship of (and the rich set of research projects pursued by) the CDS faculty and their guests through colloquia pitched to a multidisciplinary audience. 2. It guides students through the challenging transition into the graduate program in CDS by introducing them to the variety of skills and capacities that are needed to succeed as a scholar. 3. It engenders a sense of community amongst the group of students entering the program as a cohort. 4 cr. Either sem.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 52,
"college": "CDS",
"department": "DS",
"course_number": 644,
"course_name": "Machine Learning for Business Analytics",
"course_abstract": "The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern machine learning techniques through series a lectures and in- class team exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and evaluate predictive models.",
"prereqs":[],
"coreqs": [],
"credits": 3,
"semesters": ["Fall", "Spring"]
},
{
"course_id":53,
"college": "CDS",
"department": "DS",
"course_number": 653,
"course_name": "Crypto for Data Science",
"course_abstract": "This course investigates techniques for performing trustworthy data analyses without a trusted party, and for conducting data science without data. The first half of the course investigates cryptocurrencies, the blockchain technology underpinning them, and the incentives for each participant, while the second half of the course focuses on privacy and anonymity using advanced tools from cryptography. The course concludes with a broader exploration into the power of conducting data science without being able to see the underlying data.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 54,
"college": "CDS",
"department": "DS",
"course_number": 657,
"course_name": "Law for Algorithms",
"course_abstract": "Algorithms - those information-processing machines designed by humans - reach ever more deeply into our lives, creating alternate and sometimes enhanced manifestations of social and biological processes. In doing so, algorithms yield powerful levers for good and ill amidst a sea of unforeseen consequences. This crosscutting and interdisciplinary course investigates several aspects of algorithms and their impact on society and law. Specifically, the course connects concepts of proof, verifiability, privacy, security, trust, and randomness in computer science with legal concepts of autonomy, consent, governance, and liability, and examines interests at the evolving intersection of technology and the law. Grades will be based on a combination of short weekly reflection papers and a final project, to be completed collaboratively in mixed teams of law and computer and data science students. This course will include attendees from the computer science faculty, students and scholars based at Boston University and UC Berkeley.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 55,
"college": "CDS",
"department": "DS",
"course_number": 680,
"course_name": "AI Ethics",
"course_abstract": "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.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 56,
"college": "CDS",
"department": "DS",
"course_number": 682,
"course_name": "Responsible AI, Law, Ethics & Society",
"course_abstract": "The deployment of Artificial Intelligence systems in multiple domains of society raises fundamental challenges and concerns, such as accountability, liability, fairness, transparency and privacy. The dynamic nature of AI systems requires a new set of skills informed by ethics, law, and policy to be applied throughout the life cycle of such systems: design, development and deployment. It also involves ongoing collaboration among data scientists, computer scientists, lawyers and ethicists. 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 course aims to do so by bringing together students with diverse disciplinary backgrounds into teams that work together on joint tasks in an intensive series of in- class sessions. These sessions will include lectures, discussions, and group work. This unique course will bring together students from multiple institutions, each contributing undergraduate students from either computing and data science disciplines or from law and public policy disciplines.",
"prereqs": ["CDSDS100/CDSDS110 (Intro to data science OR equivalent) and CDSDS340 (intro to ML and AI OR equivalent)"],
"coreqs":[],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 57,
"college": "CDS",
"department": "DS",
"course_number": 690,
"course_name": "Directed Study in Computing & Data Sciences ",
"course_abstract": "Directed study in Computing & Data Sciences provides students the opportunity to complete directed research in a selected topic not covered in a regularly scheduled course under the supervision of a faculty member. Student and supervising faculty member arrange and document expectations and requirements. Examples include in-depth study of a special topic or independent research project.",
"prereqs":[],
"coreqs": [],
"credits": ,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 58,
"college": "CDS",
"department": "DS",
"course_number": 699,
"course_name": "Advanced Topics in Data Science",
"course_abstract": "Various advanced topics in data science that vary semester to semester. Please contact CDS for detailed descriptions.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 59,
"college": "CDS",
"department": "DS",
"course_number": 701,
"course_name": "Tools for Data Science",
"course_abstract": "This is a new course to be designed specifically for the MS DS program. Students will take this course in their first semester. The goal of the course is to give students exposure to, and practical experience in, formulating data science questions -- particularly learning how to ask good questions in a specific domain. The course will also cover methods of obtaining data and common methods of processing data from a practical standpoint. It will be organized around a semester-long group project in which students are organized into teams and engage with \"clients\" who bring data science questions from a particular domain. The course will include a formal presentation of results at the end of the semester.",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 60,
"college": "CDS",
"department": "DS",
"course_number": 900,
"course_name": "Graduate Internship in Computing & Data Sciences",
"course_abstract": "For graduate students in Computing & Data Science, this internship course gives students substantive practical experience in industry. This course may be taken with approval from the PhD program. Bi-weekly and final reports required.",
"prereqs":[],
"coreqs": [],
"credits": 1,
"semesters": []
},
{
"course_id": 61,
"college": "CDS",
"department": "DS",
"course_number": 990,
"course_name": "Computing & Data Sciences Lab Rotation",
"course_abstract": "Experience with translational or applied research pursued in an industrial or laboratory setting is important for in-the-field graduate training. To provide this training, graduate students may complete a lab rotation that provides them with the opportunity to (1) explore lab settings and industrial collaborations that may be relevant to their thesis work; (2) experience the diversity of applied and in-the-field research styles in computing and data sciences; and (3) expand their external network of potential research ",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 62,
"college": "CDS",
"department": "DS",
"course_number": 991,
"course_name": "Computing & Data Sciences Research Rotation ",
"course_abstract": "Experience with diverse research group projects and styles are an essential part of graduate training. To provide this training, graduate students are expected to complete a series of research rotations that provide them with the opportunity to (1) explore research groups where they may pursue future thesis work; (2) experience the diversity of sub-disciplines and research styles in computing and data sciences; and (3) expand their network of potential research collaborators. Before beginning a rotation, students are expected to discuss their plans with the faculty leadership of the BU research group ",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
},
{
"course_id": 63,
"college": "CDS",
"department": "DS",
"course_number": 992,
"course_name": "Computing & Data Sciences Research Rotation",
"course_abstract": "Experience with diverse research group projects and styles are an essential part of graduate training. To provide this training, graduate students are expected to complete a series of research rotations that provide them with the opportunity to (1) explore research groups where they may pursue future thesis work; (2) experience the diversity of sub-disciplines and research styles in computing and data sciences; and (3) expand their network of potential research collaborators. Before beginning a rotation, students are ",
"prereqs":[],
"coreqs": [],
"credits": 4,
"semesters": ["Fall", "Spring"]
}
]
}
|