Duke interdisciplinary data science

Duke interdisciplinary data science DEFAULT

The Duke University Master in Interdisciplinary Data Science (MIDS) is home for creative problem-solvers who want to use data strategically to advance society. We are cultivating a new type of quantitative thought leader who uses disruptive computational strategies to generate innovation and new insights.

MIDS combines rigorous computational and technical training with field knowledge and repeated practice in critical thinking, teamwork, communication, and collaborative leadership to generate data scientists who can add value to any field.

Other Requirements: an interview may be required in addition to all Graduate School requirements.

Application Terms Available
Fall

Application Deadline
February 15

Graduate School Application Requirements
See the Application Instructions page for important details about each Graduate School requirement.

  • Transcripts: Unofficial transcripts required with application submission; official transcripts required upon admission
  • Letters of Recommendation: 3 Required
  • Statement of Purpose: Required
  • Résumé: Required
  • GRE Scores: GRE General (Optional for )
  • English Language Exam: TOEFL, IELTS, or Duolingo English Test required* for applicants whose first language is not English
    *test waiver may apply for some applicants
  • GPA: Undergraduate GPA calculated on scale required
  • 1 page Essay on Leadership and Teamwork (See department guidance below)
  • 2 minute video (See department guidance below)

Department-Specific Application Requirements (submitted through online application)
MIDS is committed to the idea that data science is most successful when it is done in interdisciplinary teams. As part of your online application, you must upload a one-page, single spaced, essay on leadership and teamwork. The essay should address (i) your most significant leadership experiences, and (ii) conflicts and disagreements on teams you have been on either as a leader or team member, and how you helped resolve them.

Writing Sample
None required

Additional Components
Please prepare a 2-minute video in which you describe the problem you are most excited to solve using data science. When you are ready, record your video using the Video Essay tab in the application.

We strongly encourage you to review additional department-specific application guidance from the program to which you are applying: Departmental Application Guidance

APPLY

Sours: https://gradschool.duke.edu/academics/programs-degrees/master-interdisciplinary-data-science

DATA SCIENCE

The field of Interdisciplinary Data Science (IDS) deals with the theories, methodologies and tools of applying statistical concepts and computational techniques to various data analysis problems related to science, engineering, medicine, business, etc. The objective is to inspect, clean, transform and model data in order to discover useful information, suggest conclusions and support decision-making. It is an emerging topic that plays a critical role in almost every discipline of today’s science and technology and has become an indispensable component.

Interdisciplinary data science is a highly interdisciplinary field. Its methodologies are mostly derived from statistics theories. The computational algorithms for implementing these statistical methodologies are based upon numerical computation and optimization, and are often executed on a large-scale hardware platform composed of massive computing units and storage devices. When applying data analysis to a specific application problem, it further requires disciplinary knowledge and expertise. To accomplish these ambitious goals, there is an immediate need to “invent” a radically new degree program that can break down the traditional boundaries between disciplines and, consequently, facilitate fundamental breakthroughs and innovations.

Major Requirements

(Not every course listed is offered every semester, and the course list will be updated periodically. Please refer to the online Course Catalog for Courses offered in )

Divisional Foundation Courses

Option 1: only applicable to Class of who have taken INTGSCI &

Option 2: only applicable to Class of who have taken INTGSCI

Option 3: Applicable to Class of and any student who has not taken INTGSCI

Interdisciplinary Courses

Disciplinary Courses

Electives

Courses listed in the table below are recommended electives for the major and the course list will be updated periodically. Students can also select other courses in different divisions as electives.

Career Path

This major prepares graduates for advanced study in computer science, math, and statistics and for careers in fields such as science, engineering, health care, finance and economics as well as quantitative social science.

Mathematical Foundations 1

The fundamental concepts and tools of calculus, probability, and linear algebra are essential to modern sciences, from the theories of physics and chemistry that have long been tightly coupled to mathematical ideas, to the collection and analysis of data on complex biological systems. Given the emerging technologies for collecting and sharing large data sets, some familiarity with computational and statistical methods is now also essential for modeling biological and physical systems and interpreting experimental results. MF1 is an introduction to differential and integral calculus that focuses on the concepts necessary for understanding the meaning of differential equations and their solutions. It includes an introduction to a software package for numerical solution of ordinary differential equations.

Integrated Science 1

This course focuses on the concept of energy and its relevance for explaining the behavior of natural systems. The conservation of energy and the transformations of energy from one form to another are crucial to the function of all systems, including familiar mechanical devices, molecular structures and reactions, and living organisms and ecosystems. By integrating perspectives from physics, chemistry, and biology, this course helps students see both the elegant simplicity of universal laws governing all physical systems and the intricate mechanisms at play in the biosphere. Topics include kinetic energy, potential energy, quantization of energy, energy conservation, cosmological and ecological processes.

Mathematical Foundations 2

The fundamental concepts and tools of calculus, probability, and linear algebra are essential to modern sciences, from the theories of physics and chemistry that have long been tightly coupled to mathematical ideas, to the collection and analysis of data on complex biological systems. Given the emerging technologies for collecting and sharing large data sets, some familiarity with computational and statistical methods is now also essential for modeling biological and physical systems and interpreting experimental results. MF2 is an introduction to probability and statistics with an emphasis on concepts relevant for the analysis of complex data sets. It includes an introduction to the fundamental concepts of matrices, eigenvectors, and eigenvalues.

Integrated Science 2

This course focuses on the collective behavior of systems composed of many interacting components. The phenomena of interest range from the simple relaxation of a gas into an equilibrium state of well-defined pressure and temperature to the emergence of ever increasing complexity in living organisms and the biosphere. The course provides an overview of some fundamental differences between traditional disciplines as well as indications of how they complement each other some important contexts. Topics include thermodynamic (statistical mechanical) equilibrium, fundamental concepts of temperature, entropy, free energy, and chemical equilibrium, driven systems, fundamentals of biological and ecological systems.

Integrated Science 3

Integrated Science 3 emphasizes the physics and chemistry concepts of oscillating systems, waves, and fields, and includes applications to human perception. In addition to their fundamental importance to physics and chemistry proper, these ideas are essential for developing an awareness of the principles employed by engineers in the construction of the electrical and optical devices that are ubiquitous in modern civilization. Topics include harmonic oscillators, sound waves, light, and reaction-diffusion patterns.

Integrated Science 4

Integrated Science 4 has more of a chemistry/biology emphasis, with physics brought to bear as needed. It treats topics relevant to understanding organisms, biochemical engineering, and the environment. Topics include evolution, modern biology, ecosystems, hydrology, and climate.

Scientific Writing and Presentations II

Scientific Writing and Presentations cover some of the areas of scientific communication that a scientist needs to know and to master in order to successfully promote his or her research and career. Students will learn to recognize and construct logical arguments and become familiar with the structure of common publication formats. It will help students to advance their skills in communicating findings in textual, visual and verbal formats for a variety of audiences.

Scientific Writing and Presentations I

Scientific Writing and Presentations cover some of the areas of scientific communication that a scientist needs to know and to master in order to successfully promote his or her research and career. Students will learn to recognize and construct logical arguments and become familiar with the structure of common publication formats. It will help students to advance their skills in communicating findings in textual, visual and verbal formats for a variety of audiences.

Introduction to Programming and Data Structures

This course covers data and representations, functions, conditions, loops, strings, lists, sets, maps, hash tables, trees, stacks, graphs, object-oriented programming, programming interface and software engineering.

Principles of Machine Learning

This course covers maximum likelihood estimation, linear discriminant analysis, logistic regression, support vector machine, decision tree, linear regression, Bayesian inference, unsupervised learning, and semi-supervised learning.

Statistical Machine Learning

This course covers statistical inference, parametric method, sparsity, nonparametric methods, learning theory, kernel methods, computation algorithms and advanced learning topics.

Data Acquisition and Visualization

This course introduces the principles and methodologies for data acquisition and visualization, along with tools and techniques used to clean and process data for visual analysis. It also covers the practical software tools and languages such as Tableau, OpenRefine and Python/Matlab.

Interdisciplinary Data Analysis

This course covers interdisciplinary applications of data analysis for social science, behavioral modeling, health care, financial modeling, advanced manufacturing, etc. Students are expected to solve a number of practical problems by implementing data algorithms with R during their course projects.

Probability, Random Variables and Stochastic Processes

This course covers probability models, random variables with discrete and continuous distributions, independence, joint distributions, conditional distributions, expectations, functions of random variables, central limit theorem, stochastic processes, random walks, and Markov chains.

Advanced Linear Algebra

This course covers pseudo inverse, inner product, vector spaces and subspaces, orthogonality, linear transformations and operators, projections, matrix factorization, and singular value decomposition.

Numerical Analysis and Optimization

This course covers Gaussian elimination, LU factorization, Cholesky decomposition, QR decomposition, Newton-Raphson method, binary search, convex function, convex set, gradient method, Newton method, Lagrange dual, KKT condition, interior point method, conjugate gradient method, random walk, and stochastic optimization.

Algorithms and Databases

This course covers sorting, order statistics, binary search, dynamic programming, greedy algorithms, graph algorithms, minimum spanning trees, shortest paths, SQL, file organization, hashing, sorting, query, schema, transaction management, concurrency control, rash recovery, distributed database, and database as a service.

Bayesian and Modern Statistics

This course covers Bayesian inference, prior and posterior distributions, multi-level models, model checking and selection, and stochastic simulation by Markov Chain Monte Carlo.

Computer Vision

This course covers image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, and object recognition.

Search Engines

This course covers Boolean retrieval, dictionary, index, vector space model, score, query, XML, language model, text classification, clustering, and web search.

Artificial Intelligence

This course covers uninformed search, informed search, constraint satisfaction, classical planning, neural network, deep learning, hidden Markov model, Bayesian network, Markov decision process, reinforcement learning, active learning and game theory.

Introduction to Data Science

As an introductory course in data science, this course will show students not only the big picture of data science but also the detailed essential skills of loading, cleaning, manipulating, visualizing, analyzing and interpreting data with hands on programming experience.

Image Data Science

This course introduces the logical structure of digital media and explores computational media manipulation. The course uses the Python programming language to explore media manipulation and transformation. Topics include spatial and temporal resolution, color, texture, filtering, compression and feature detection.

Computer Vision

This course covers image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, and object recognition.

Prerequisite(s): STATS Principles of Machine

Sours: https://undergrad.dukekunshan.edu.cn/en/data-science
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DCHI

The Master in Interdisciplinary Data Science—Concentration in Biomedical Informatics is a full-time two-year education program for students who want to work in biomedical informatics where strong data science skills are needed to address future biomedical informatics challenges. Students will be exposed to the interdisciplinary application of data science while developing skills applied to real-world problems in healthcare. A recent publication by MA Meyer titled, &#;Healthcare Data Scientist Qualifications, Skills, and Job Focus: A Content Analysis of Job Postings&#; focuses on the competitive market for data scientists in the healthcare field. MIDS connects technical learning and expertise with the many domains in need of data insights. The true power of data in the twenty-first century lies in that connection and this program is developed around the belief that harnessing this power requires both interdisciplinary training and experience.The required core courses for all MIDS students during Year 1 are centered on marshaling, analyzing, and visualizing data. They create a shared language for all students and a common frame of reference for data-driven projects. The core courses reflect the multiple quantitative disciplines that contribute skill sets to data science.  Students acquire the knowledge and skills needed to become informatics professionals through an additional set of courses during Years 1 and 2, culminating with a year-long capstone project synthesizing the student’s informatics knowledge in a real-world setting project.Students enroll as full-time students. They can earn their degree after completing 45 course credit hours.CORE MIDS COURSES
  • Data to Decision
  • Modeling and Representation of Data
  • Introduction to Text Analysis
  • Principles of Machine Learning
  • Data Management Systems
  • Data Logic, Visualization, and Storytelling
  • Data Science Ethics

BMI COURSES

COMPONENTS OF PROGRAM

  • Online summer review in statistics, linear algebra, and programming to assure students are on the same playing field at the beginning of the program
  • Pre-orientation bootcamp to introduce you to your classmates and the technical and professional practices we will be using throughout the program
  • Seven required core courses that cover critical topics in statistical modeling, machine learning, programming, data wrangling, text analysis, database systems, data visualization, data regulations and ethics, and data interpretation
  • Up to eight additional electives in biomedical informatics to deepen and broaden knowledge in the topics students are most passionate about
  • Data science seminar that hosts outside speakers to discuss the latest developments and issues in data science
  • Informatics research seminars that host speakers to discuss research projects using informatics applications
  • One-year capstone project with Duke’s world-class research faculty and outside partners
  • Structured training to develop communication, teamwork, and leadership skills
  • Career development events, workshops, and mentoring

For more information:
MIDS Website,
[email protected],
()

Sours: https://dukeinformatics.org/education/health-informatics-programs-available-at-duke/duke-to-begin-a-master-in-interdisciplinary-data-science-mids-concentration-in-biomedical-informatics/

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Science data duke interdisciplinary

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5 Facts about the Duke Master in Interdisciplinary Data Science Program

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