Data Science
The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.
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CDS DS 100: Data Speak Louder Than Words
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. -
CDS DS 109: Data Science First Year Seminar
Through activities and discussions, DS109 is designed as an introduction to academic and personal success at BU as a Data Science Major. Topics includes advising, curriculum planning, time management, personal finance, wellness and safety, building academic pathways, and understanding available resources. -
CDS DS 110: Introduction to Data Science with Python
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. -
CDS DS 120: Foundations of Data Science
Undergraduate Prerequisites; Undergraduate Corequisites: CDSDS 100 OR CDSDS 110. - The first in a 3-course sequence (with CDSDS 121 and CDSDS 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. -
CDS DS 121: Foundations of Data Science 2
Undergraduate Prerequisites: CDSDS 120 OR equivalent; Undergraduate Corequisites: CDSDS 110 OR equivalent. - DS 121 is the second in the three-course sequence (CDSDS 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. -
CDS DS 122: Foundations of Data Science 3
Undergraduate Prerequisites: CDSDS120 OR equivalent and corequisite of CDSDS110 - DS DS 122 is the third in a three-course sequence (with CDSDS 120 and CDSDS 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. -
CDS DS 200: Undergraduate Internship in Data Science
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. This course may be repeated for credit with CDS approval. -
CDS DS 210: Programming for Data Science
This course builds on DS110 by expanding on programming language, systems, and algorithmic concepts introduced in the prior course. The course begins by exploring the different types of programming languages and introducing students to important systems level concepts such as computer architecture, compilers, file systems, and using the command line. It then moves to introducing a high performance language (Rust) and how to use it to implement a number of fundamental CS data structures and algorithms (lists, queues, trees, graphs etc). Then it covers how to use Rust in conjunction with external libraries to perform data manipulation and analysis. Students are expected to propose and complete an independent project on a large dataset using Rust. 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. -
CDS DS 219: Software Engineering Career Prep Workshop
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. -
CDS DS 280: Spark! UX/UI Design
User experience (UX) design encompasses the holistic journey of the end-users interactions with a company, its services, and its products. UX designers focus on maximizing usability, accessibility, and the overall user experience. The course will cover the basic steps of the UX process starting with the discovery of user insights and leading to a problem definition based around personas, journey maps, and user stories. Students will then design an application that responds to this problem by creating low-fidelity wireframes and evolving them into high fidelity prototypes for user testing. Through this process, students will complete a series of activities using Figma or similar design tools. While this course involves developing design artifacts, the foundational learning outcome is focused on the process of creating a design that responds to the needs of real people as identified through user research. -
CDS DS 290: Spark! Civic Tech Research Design Workshop
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. Students must register for both DS290 and DS292. -
CDS DS 291: Spark! Exploring DEI in Tech
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. -
CDS DS 292: Spark! Civic Tech Toolkit Workshop
Undergraduate Prerequisites: (CDSDS 290) - This workshop introduces students to a variety of data sources and methodologies applicable for studying questions around civic life in a variety of settings e.g., elections, housing, policing, economic development, and more. While we will primarily focus on data used in the United States (including U.S. Census data, voter files, and other public records), these skills are applicable to civic research globally. We will also learn how to conduct analysis from start (a vague question or assignment) to finish (a rigorous and careful analysis that acknowledges its limitations). This course is taught using both R and Python; students must possess the ability to manipulate and analyze data in one of these languages. This course fulfills a single unit in the following BU Hub area: The Individual in Community. -
CDS DS 299: CDS Workshops
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/ -
CDS DS 310: Data Mechanics
Undergraduate Prerequisites: 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. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas Quantitative Reasoning II, Critical Thinking. -
CDS DS 320: Algorithms for Data Science
Undergraduate Prerequisites: CDSDS121 or equivalent AND CDSDS210 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 and AI
Undergraduate Prerequisites: CDSDS122, CDSDS320 - DS 340 covers the most important concepts and algorithms in AI and machine learning, ranging from search to deep neural networks, with an eye toward conceptual understanding and building a final project. Important topics include varieties of search (for lookahead), probabilistic reasoning, gradient descent applied to neural networks, applying regularization, reinforcement learning, the role of embeddings in natural language processing, and the role of attention in transformer architectures (eg, BERT and GPT4). Applications include image classification, sentiment analysis, game playing, and recommender systems, as well as a cursory introduction to generative AI. A background in Python programming is necessary, while multivariable calculus, linear algebra, and probability allow a deeper understanding of the material. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Quantitative Reasoning II, Critical Thinking. -
CDS DS 380: Data, Society, and AI Ethics
This course develops students' ability to critically examine and question the interplay between artificial intelligence (AI), 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 381: Social Justice for Data Science
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. -
CDS DS 453: Crypto for Data Science
Undergraduate Prerequisites: DS-122 and DS-320, or equivalent. - 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.
