Theoretical foundations of data science. 5% of extra credit to everyone’s overall grade.

Theoretical foundations of data science Accompanying big data is the rise of dimensionality and high dimensionality characterizes many contemporary sta-tistical problems, from sciences and engineering to social science and humani-ties. If you have a question about anything to do with the course — if you're stuck on a homework problem, want clarification on the logistics, or just have a general question about data science — you can make a post on Campuswire. This is how data looks like (first few rows and columns): PNG Diffusion Maps example (PDF) (2D data in CSV) Sequential circuits: theory of finite state automata. Discover more from: Theoretical Foundations of Data Science I DSC 40A University of California San Diego 20 Documents Go to course Abstract In today's data-driven era, data serves as the lifeblood of any organization and society at large. Data scientists must be able to convey complex results in a way that influences decision-making and drives action. Jun 1, 2017 · A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. His research interests are broadly in machine learning, optimization, and learning structured data (e. The sequence DSC 40A-B introduces the theoretical foundations of data science and covers the following topics: mathematical language for expressing data analysis problems and solution strategies, probabilistic reasoning, mathematical modeling of data, and algorithmic problem solving. The theory of computation plays a crucial role in providing solid foundations for all areas of Computer Science, including systems, artificial intelligence, security, and circuit design. This repo covers linear algebra, calculus, probability, statistics, and optimization, providing practical implementations and theoretical insights. Le01 - Professor Justin Eldridge notes Course: Theoretical Foundations of Data Science I (DSC 40A) Students shared 21 documents in this course University: University of California San Diego AI Chat Info More info Download AI Quiz Lecture 1 1 A Brief Histo ry of Data Science Mar 25, 2024 · A theoretical framework provides a structure for research by linking the study to existing theories, concepts, or models. The interpretation of data, the methodology, the choice of the research question, and other research activities should not be based on “common sense,” everyday intuitions, or subjective feelings, but on empirical findings and scientific models. Please continue to consult the Schedule of Classes each quarter for the most updated information. Results should significantly advance current understanding of data science, by algorithm development, analysis, and/or computational implementation which demonstrates behavior and applicability of the algorithm. Data Science as a scientific discipline is influenced by informatics, computer science, mathematics, operations research, and statistics as well as the applied sciences. Throughout this chapter, we highlight the Abstract. This This class focuses on theoretical foundations of data science. We first overview classical information-theoretic problems and solutions. student positions in theoretical foundations of data storage 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. 5% of extra credit to everyone’s overall grade. Jan 23, 2020 · This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Expository Jun 11, 2022 · While there are many resources covering the theoretical foundations of data science concepts, few demonstrate why having these foundations is practically important. Image by author. A study’s theory describes the theoretical foundations of the research and consists of the big-T theory (ies) that guide the investigation. Learn the theoretical and practical foundations of data science through key concepts, such as exploratory data analysis, statistical inference, supervised and unsupervised machine learning, scaling and distributed computing, and network analysis. Foundations of Data Science This book provides an introduction to the mathematical and algorithmic founda-tions of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Dec 2, 2024 · The immense success of ML systems relies heavily on large-scale, high-quality data. Theoretical frameworks are crucial in ensuring the study is grounded in established knowledge and contributes meaningfully to the field. We describe the foundations of machine learning, both learning from given training examples, as well as the theory of Vapnik-Chervonenkis dimension, which tells us how many training examples su ce for learning. ’ The chapter discusses the foundations of In this class we will explore theoretical foundations for deep learning, emphasizing the following themes: (1) Approximation: What sorts of functions can be represented by deep networks, and does depth provably increase the expressive power? (2) Optimization: Essentially all optimization problems we want to solve in practice are non-convex. edu Lecture: Tu/Th 11AM-12:30PM, Center 109 Lecture Recordings Assignment Solutions If you have a question about anything to do with the course — if you're stuck on a homework problem, want clarification on the logistics, or just have a general question about data science — you can make a post on Campuswire. Deep learning has emerged as a transformative technology in data science, revolutionizing various domains through its powerful capabilities. Topics include empirical risk minimization, optimization, regression, classification, and discrete probability. Compilers: theory of context free grammars. Uncover how these foundations can enhance your Theoretical Foundations of Data Science I 🧠 DSC 40A, Fall 2021 at UC San Diego Suraj Rampure he/him rampure@ucsd. We consider statistical models for such tasks and aim to design efficient (polynomial-time) algorithms that achieve the strongest possible rigorous (statistical) guarantees under these models. Welcome to DSC 40B in Spring 2021! This page should answer most of the questions you might have about how the course is run. All the topics listed here will be covered, and some other topics may be added. Theoretical Foundations of Data Science I DSC 40A, Fall 2025 at UC San Diego This HDSI area focuses on establishing the mathematical and theoretical foundation of data science, understanding the fundamental limits of data acquisition, data processing, modeling, and inference from data. DSC 40B – Theoretical Foundations of Data Science II This Week Weighted Shortest Paths Lecture 23 — Dijkstra Algorithm 📖 Reading None 🎞️ Slides pdf May 20, 2021 · Welcome to DSC 40B in Spring 2021! This page should answer most of the questions you might have about how the course is run. This course provides theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to 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). Topics include foundations of deep learning, reinforcement learning, machine learning and logic, network inference, high-dimensional data analysis, trustworthiness and reliability, fairness, and data science with strategic agents. The IFDS Workshop on Theoretical Foundations of Information and technology allow us to collect big data of unprecedented size and complexity. Potential applications include algorithm design and quantum computation. D. Importance: Even the best data insights are useless if they cannot be communicated effectively. It’s about analytics and applications, and a scientific approach to using data based on well-founded theory and sound business judgment. Understand the foundations of probability and its relationship to statistics and data science. Mar 3, 2022 · Entry requirements 120 credits including 30 credits in mathematics och 10 credits in computer science. This definition is moderately broad, and that’s because one must say data science is a moderately broad field! Jul 29, 2022 · The transdisciplinary and multi-institution Institute for Data, Econometrics, Algorithms, and Learning received a five-year, $10 million NSF TRIPODS Phase II award to continue advancing the theoretical foundations of data science. This pathway will Theory focuses on the theoretical foundations of computer science and frequently relies on rigorous mathematical proofs. As such, it incorporates skills from computer science Feb 4, 2022 · One of the biggest decisions that you’ll make when writing your dissertation is the theoretical foundation of your research project. The high demand for data has led to many paradigms that involve selling, exchanging, and sharing data, motivating the study of economic processes with data as an asset. Whereas 5. This book is about the science and art of data analytics. The emergence of the web and social networks as central aspects of daily life presents both opportunities and challenges for theory. This helps us work with both structured and unstructured data well. We also incorporate adversarial Machine learning is a striking example. Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience. Students practice creative problem-solving while learning how to Studying DSC 40A Theoretical Foundations of Data Science I at University of California San Diego? On Studocu you will find 17 lecture notes, 11 practice materials, Apr 13, 2023 · A detailed analysis of key foundations of math for data science based on topics like linear algebra, probability theory, statistics, calculus, & optimization. Introduction Theoretical Computer Science (TCS) is the subdiscipline of Computer Science that studies computational and algorithmic processes and interactions. This is a tentative list of topics that will be covered in class. DSC 40A/R, the first course in the sequence, exposes students to the mathematical theory underlying fundamental topics in machine learning. DSC 40B – Theoretical Foundations of Data Science II 📜 Syllabus Welcome to DSC 40B in Fall 2022! This page should answer most of the questions you might have about how the course is run; check out the frequently asked questions for answers to some common ones. This foundation is really important because it helps us know how computers can be used to do all sorts of cool stuff, like playing Aug 20, 2023 · It offers a bridge between theory and real-world application, empowering you to harness the true potential of data to fuel innovation and ignite change. Jul 29, 2022 · Data science is an expanding field that requires the expertise of computer scientists, engineers, mathematicians, and statisticians to handle the complex analysis of ever-larger data sets. About A comprehensive collection of Python code, notes, and resources exploring the mathematical foundations essential for data science and artificial intelligence. This material has been published by Cambridge University Press as Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravi Kannan. Nov 6, 2025 · Provides guidelines on how to prepare, organize, and write a college-level research paper in the social and behavioral sciences, along with offering practical strategies for writing effectively and with confidence. It also explores the design and analysis of algorithms, data structures, and programming languages. Jul 14, 2022 · An essential characteristic of good scientific research is a sound theoretical foundation. You can check when the Schedule of Classes becomes available here. Theoretical Foundations of Data Science I DSC 40A, Spring 2024 at UC San Diego IMPORTANT: Course offerings are tentative and subject to change and/or cancellation. In the 1970’s, the study of algorithms was added as an important Course Catalog Description: Theoretical Foundations of Computer Science. This group aims to develop efficient and effective algorithms for all aspects of data analysis with rigorous guarantees. g. Offered by University of Colorado Boulder. The course links mathematics and computational thinking The purpose of this chapter is to set the stage for the book and for the upcoming chapters. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals Transdisciplinary Research In Principles Of Data Science (TRIPODS) brings together the statistics, mathematics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities focused on core algorithmic, mathematical, and statistical principles. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, and summarizing these findings. This puzzle helps us understand how computers think and solve problems. 5 Developing a theoretical framework Social work researchers develop theoretical frameworks based on social science theories and empirical literature. It combines knowledge from statistics, mathematics, computer science, and domain expertise. In the 1970’s, the study of algorithms was added as an important component That is what this book is about, it’s about theories and models, with or without data, big or small. , time-series or graph data), with a focus on theoretical understanding of the optimization and generalization in deep learning problems. Dec 7, 2019 · As a data scientist, it is essential that you understand the theoretical and mathematical foundations of data science in order to be able to build reliable models with real-world applications. Data affect… Data Theory is necessary because this asounding growth in Data Science has outstripped the rigorous understanding of the statistical and mathematical foundations that underlie these methods. Mar 14, 2024 · Dive into the four mathematical foundations essential for data science: Statistics, Linear Algebra, Differential Calculus, and Discrete Mathematics. The The first in a 3-course sequence (with CDS DS 121 and CDS DS 122) that introduces students to theoretical foundations of Data Science. Participation in Linear Algebra for Data Science or Linear Algebra II. Horses. While we will cover some applications and examples in Python, this class is not an applied, coding-based class. This paper explores the theoretical foundations, practical applications, and comparative analysis of deep learning models. Example 1 : Univariate Feature Selection The problem The sequence DSC 40A/R-B/R introduces the theoretical foundations of data science. With more jobs in data science, knowing the basics is more important The constant model plays a foundational role in data science. Participation in Introduction to Data Science. Enriches readers’ broad appreciation of the connections from theoretical and mathematical research to empirical studies and application case studies Stimulates readers’ insight into the scientific foundations of data visualization Inspires readers to make ground-breaking contributions to the discipline of visualization. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. In the course "Foundations of Data Visualization", you will build a solid foundation in data Enroll for free. What frameworks can be used to analyze such problems Data science is an interdisciplinary field [10] focused on extracting knowledge from typically large data sets and applying the knowledge from that data to solve problems in other application domains. “Theory” and “theoretical foundations” are Apr 7, 2023 · Theoretical Foundations of Data Science I 🥑 DSC 40A, Spring 2023 at UC San Diego 🪑 Seating assignments for the final exam are now posted. The theoretical framework is the foundation from which all knowledge is constructed (metaphorically and literally) for a research study. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. The theoretical foundations section discusses key neural network architectures such as Convolutional Neural Networks (CNNs Nov 6, 2022 · The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. fellowship. events At KTH we study the foundations of data science, with emphasis on developing the underlying theory, designing novel computational methods, and exploring applications in software engineering, language technology, natural sciences, and social-network analysis, among others. Learning outcomes On the completion of the course the student should be able to: formulate Aug 18, 2025 · Foundations of Computer Science Foundations of Computer Science is intended for students who wish to develop state-of-the-art knowledge of the theoretical foundations of Computer Science. These models tackle real-world problems. This pre-publication version is free to view and download for personal use only. Data Science emerges as an indispensable force driving innovation and informed decision-making. Sep 29, 2022 · This chapter first defines data science, its primary objectives, and several related terms. Jul 26, 2025 · Data science means different things for different people, but at its core, data science is using data to answer questions. We describe the foundations of machine learning, both algorithms for optimizing over given training examples, as well as the theory for understanding when such optimization can be expected to lead to good performance on new, unseen data, including important measures such as Vapnik-Chervonenkis dimen-sion. Proficiency in English equivalent to the Swedish upper secondary course English 6. Training the constant model amounts to answering the question: “What is the best single value to represent or summarize all of my data?” Theoretical Foundations of Data Science I 🧠 DSC 40A, Winter 2022 at UC San Diego Janine Tiefenbruck she/her jlobue@ucsd. Whether you're a budding data scientist, a curious analyst, or an inquisitive mind, the revelation of these foundational principles will undoubtedly kindle your intellectual fire. Theoretical Foundations of Data Science Home / Theoretical Foundations of Data Science FOUNDATIONS OF DATA SCIENCE INSTITUTE We bring together a large and diverse team of researchers and educators from UC Berkeley, MIT, Boston University, Bryn Mawr College, Harvard University, Howard University, and Northeastern University, with the aim of advancing the theoretical foundations for the field of data science. 3 days ago · KTH Royal Institute of Technology in Stockholm, Sweden is advertising two Ph. This course will provide an introduction to the mathematical and algorithmic foundations of data science. Learning the basics of data science is key for making reliable models. The importance of utilizing a theoretical framework in a dissertation study cannot be stressed enough. This introductory chapter lays the foundation for our comprehensive exploration in the book, ‘Fundamentals of Data Science - Theory and Practice. Most of the topics listed here will be covered, and some other topics may be added. Assignments are data-intensive, and require significant programming and statistical analysis. However, data differs from classical economic assets in terms of free duplication: there is no concept of limited supply since it can be Preprint here. The pillars of data science—data engineering, mathematics and statistics, domain knowledge, machine learning, data visualization, programming, ethics and privacy, and communication Machine learning is a striking example. May 24, 2024 · Explore the foundational Theoretical Concepts in Data Science with our comprehensive overview, essential for professionals and enthusiasts alike. Applications to real data: A comparison of various correlation measures (PDF) (data in CSV) (data in R) (R code) Applications of random matrix theory (PDF) (data in CSV: SP500 2012-2014) (data in CSV: SP1500 2013-2019). Jul 7, 2025 · Mathematics is a fundamental component of Data Science, providing the theoretical foundations for many data analysis and Machine learning techniques. Cryptography: theory of computational complexity. Computer science as an academic discipline began in the 1960’s. Enroll for free. In courses in the theoretical foundations of data science, students will analyze the mathematical underpinnings of data analysis techniques in a quest to answer: Why do they work? When are they applicable? What are their limitations? And can they be improved? In studying the application of these Home Team News Education Research Events Foundations of Fairness and Accountability Workshop Theoretical Exploration of Foundation Model Adaptation Methods The Uneasy Relation Between Deep Learning and Statistics Opportunities Outreach Rising Stars in Data Science 2024 – Applications and Info Session EnCORE IPAM Workshop on Computational He is a recipient of Bloomberg Data Science Ph. It serves as a lens through which the research problem is examined, offering a foundation for understanding and analyzing data. Supports Open Access Foundations of Data Science (FoDS) invites submissions focusing on advances in mathematical, statistical, and computational methods for data science. Work in this field is mathematical rather than empirical, and offers a unique perspective that excels at exposing and answering key questions about the possibilities and limitations of computation, broadly construed. Topics include: propositional and predicate logic with applications to logic programming, database querying, and program verification; induction and its Oct 11, 2024 · What is Theoretical Computer Science?: More Than Just a Theory! Imagine theoretical computer science (TCS) as a big, giant puzzle. We then discuss emerging applications of information-theoretic methods in various data-science problems and, where applicable, refer the reader to related chapters in the book. Note that our final is in Center 115, not our regular classroom! Also, if at least 85% of the class fills out both the End of Quarter Survey and SETs (Student Evaluations of Teaching), we will add 0. Many research papers have come across that probability and statistics are the Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. It’s like the foundation of a house, but for computers. It continues by describing the evolution of data science from the fields of statistics, operations research, and computing. Another important domain-independent technique is based on Markov chains. DS 122 covers topics in probability (including common probability distributions, conditional probability, independence Cambridge Core - Pattern Recognition and Machine Learning - Foundations of Data Science We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. IMT 573 – Data Science I: Theoretical Foundations; Prerequisites: either Q METH 201, IMT 570, or equivalent college coursework; either CSE 142, IMT 501, or equivalent college coursework Majors in Data Science at UCSD can expect a curriculum that balances theory and practice. Students are expected to have college-level statistics and programming experience (R and Apr 3, 2024 · Data Science Theory is at the heart of a growing field. In particular we discuss fundamental ideas and techniques that come from probability, information theory as well as signal processing. Sep 23, 2022 · Introduces technically focused theoretical foundations of “Data Science. edu Course Outline This class focuses on theoretical foundations of data science. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. It provides overarching perspectives, explanations, and predictions about the social problem and Offered by Johns Hopkins University. Prerequisite: CSc 2010 with grade C or higher. Data compression: theory of information. This article gives four examples of data science "gotchas" that can be avoided by understanding theory. A breakdown of the fundamental math field Dec 20, 2013 · This program will combine viewpoints and techniques from the theory of computation, statistics, and related areas with the aim of laying the theoretical foundations of the emerging field of Big Data. Data Theory at UCLA the mathematical and statistical foundations of data science a collaboration between the Departments of Statistics and Data Science and Mathematics Promoting research into fundamental ideas of Statistics and Mathematics Preparing students for leadership roles in data science in industry and academia Why Data Theory is important The sequence DSC 40A-B introduces the theoretical foundations of data science. Subareas The enhanced ability to observe, collect, and store data in the natural sciences, in commerce, and in other elds calls for a change in our understanding of data and how to handle it in the modern setting. We propose a partition of the Welcome to databasetheory. This is a tentative list of topics (with links to lecture notes) that will be covered in class. DSC 40A, the first course in the sequence, exposes students to the mathematical theory underlying fundamental topics in machine learning. If you don't find what you're looking for here, feel free to make a post on Ed. This course covers the basic theoretical foundations required to study various sub-disciplines in computer science. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decompo-sition, the theory of random walks and Dec 14, 2018 · Taking inspiration from the areas of algorithms, statistics, and applied mathematics, this program aims to identify a set of core techniques and principles for modern Data Science. The course includes a complete set of homework assignments, each containing a theoretical element and implementation challenge with support code in Python, which is rapidly becoming the prevailing programming language for data science and machine learning in both academia and industry. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, algorithms for clustering, probabilistic IFDS workshop brings together experts to help make AI more reliable, robust, and trustworthy The Institute for Foundations of Data Science (IFDS) brought AI and machine learning experts from across the nation to the University of Washington to discuss and explore the mathematical, statistical, and algorithmic underpinnings of modern AI systems. org, your friendly stop over for all things database-theoretical :) Aim of this site This site is one of the many initiatives that are coming out of a Dagstuhl Perspectives Workshop on the Foundations of Data Management and is to collect resources, including DB theory conference info (ICDT, PODS) Info on Ron Fagin event (held at PODS 2016) Links to textbooks. Talks and travel - 2025 SfN, San Diego (Nov 15-18 2025) TAG-DS, UC San Diego (Oct 5-6 2024) NeurIPS, San Diego (Dec 1-2 2025) Teaching Fall 2025: DSC 40A Theoretical Foundations of Data Science I Winter 2026: DSC 120 Signal Processing for Data Analysis Winter 2026: DSC 205 Geometry of Data Theoretical Computer Science Theoretical computer science is the branch of computer science that studies the foundations of computation, including its limits and possibilities. ” Provides an overview of key concepts, focusing on foundational concepts such as exploratory data analysis and statistical inference. Data science is transforming business. We have 2 PhD positions available in Theoretical Computer Science@KTH ! Please share widely and help us out 😀 (deadline Nov 27th) ------ KTH Royal Institute of Technology in Stockholm, Sweden Aug 9, 2019 · This program will bring together researchers from academia and industry to develop empirically-relevant theoretical foundations of deep learning, with the aim of guiding the real-world use of deep learning. ztsmh pmqggti gnus nuzzowx tblf asfqavi uynrvzd joufy riwroh oael vavvb zwtrw zdlxyd ket nzpbkvn