Such courses offer one way to learn more about the Department of Statistics & Data Science and the field in general. After completing the common MCDS core courses in the first semester, you can pick from three concentrations: Systems, Analytics, and Human-Centered Data Science. Refer to the Machine Learning Master's Curriculumfor full information. If students do not have at least three, they need to take additional advanced electives. Any 36-300 or 36-400 level course in Data Analysis that does not satisfy any other requirement for the Economics and Statistics Major may be counted as a Statistical Elective. A maker. They are on a rotation and new Special Topics are regularly added. Students should carefully check the course descriptions to determine if additional prerequisites are necessary. Glenn Clune, Academic Program Manager PhD Requirements - Machine Learning - CMU - Carnegie Mellon University Requirements for the Machine Learning PhD program The Machine Learning Department at Carnegie Mellon University is ranked as #1 in the world for AI and Machine Learning, we offer Undergraduate, Masters and PhD programs. A writer. At the same time, the faculty is firmly dedicated to undergraduate education. It provides a powerful and wide-ranging set of tools for dealing with uncertainty. (36-235 (Note: A score of 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement). The program is geared toward students interested in statistical computation, data science, and "big data" problems. The Beginning Data Analysis courses give a hands-on introduction to the art and science of data analysis. ). Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc. The first schedule uses calculus sequence 2. During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar. Many of our students have also gone on to graduate study at some of the top programs in the country including Carnegie Mellon, the Wharton School at the University of Pennsylvania, Johns Hopkins, University of Michigan, Stanford University, Harvard University, Duke University, Emory University, Yale University, Columbia University, and Georgia Tech. Current Computer Science Undergraduate Curriculum The ACT range was 33-35. They should be able to understand technical concepts and be competent in the following areas: General mathematics including calculus and linear algebra Basic statistical concepts and methods The following sample program illustrates one way to satisfy the requirements of the Statistics and Machine Learning program. Social Media Directory, Engineering Statistics and Quality Control, Experimental Design for Behavioral & Social Sciences, Introduction to Statistical Research Methodology, Statistics of Inequality and Discrimination, Special Topics: Statistical Methods in Epidemiology, Special Topics: Methods of Statistical Learning, Special Topics: Multilevel and Hierarchical Models, Special Topics: Applied Multivariate Methods, Special Topics: Conceptual Foundations of Statistical Learning, Special Topics: Statistical Methods in Finance, Special Topics: Statistical Genomics and High Dimensional Inference, Fundamentals of Programming and Computer Science, Introduction to Machine Learning (Undergrad), Introduction to Machine Learning (SCS Majors), Research Methods in Developmental Psychology, Introduction to Parallel Distributed Processing, *Students who place out of 73-102 based on the economics placement exam will receive a pre-req waiver for 73-102 and are waived from taking 73-102, Professional Communication for Economists, Machine Learning with Large Datasets (Undergraduate), Machine Learning for Text and Graph-based Mining, Artificial Intelligence: Representation and Problem Solving, Total number of units required for the minor. The B.S. Browse all current Department of Statistics & Data Sciencecurriculums and courses. For all these reasons, Statistics & Data Science students are highly sought-after in the marketplace. are intended only for students with a very strong mathematical background. (Note: A score of 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement). With respect to double-counting courses, it is departmental policy that students must have at least three statistics courses (36-xxx) that do not count for their primary major. Throughout the sections of this catalog, we describe the requirements for the Major in Statistics and the different categories within our basic curriculum, followed by the requirements for the Major in Economics and Statistics, the Major in Statistics and Machine Learning, and the Minor in Statistics. In addition, Statistics and Machine Learning majors gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration. This course applies data science techniques in the context of software . 36-200 draws examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. More Post-Graduation Stats(opens in new window). Browse all current Department of Statistics & Data Sciencecurriculums and courses. In order to get a minor in Statistics a student must satisfy all of the following requirements: Complete one of the following two sequences of mathematics courses at Carnegie Mellon, each of which provides sufficient preparation in calculus: Note: Passing the Mathematical Sciences 21-120 assessment test if an acceptable alternative to completing 21-120. Please note that students who complete36-235are expected to take36-236to complete their theory requirements. . The schedule uses calculus sequence 2, andan advanced data analysis elective (to replace the beginning data analysis course). Before graduation, students are encouraged to participate in a research project under faculty supervision. **It is possible to substitute36-226or36-326(honors course) in place of36-236. Other courses emphasize examples inengineering and architecture (36-220) and the laboratory sciences (36-247). Doctoral Programs In the School of Computer Science, we believe that Ph.D. students thrive in a flexible environment that considers their background and experience, separates funding from advising, and encourages interdisciplinary exploration. More events. or 36-236 Within the MISM program, the Business Intelligence & Data Analytics (BIDA) pathway goes deeper into the emerging field of analytics, forging experts that change the way companies do business around the world.. Note: Taking/having credit for both 21-111 and 21-112 is equivalent to 21-120. Moreover, statisticians must learn to collaborate effectively with people in other fields and, in the process, to understand the substance of these other fields. Other courses emphasize examples in engineering and architecture (36-220 412-268-2000. The theory of probability gives a mathematical description of the randomness inherent in our observations. 36-200 draws examples from many fields and satisfies the DC College Core Requirement in Statistical Reasoning. Mar 6 - Dec 1 Inventing Shakespeare: Text, Technology, and the Four Folios Exhibit. Are you ready for Carnegie Mellon? Carnegie Mellon accepts 7.26% transfer applicants, which is competitive. Public Policy, Management & Data Analytics at Carnegie Mellon University Pittsburgh, Pennsylvania, United States 777 followers 500+ connections The Department Statistics does not provide approval or permission for substitution or waiver of another department's requirements. The Beginning Data Analysis courses give a hands-on introduction to the art and science of data analysis. is tailored for engineers and computer scientists, 36-218is a more mathematically rigorous class for Computer Science students and more mathematically advanced (students need advisor approval to enroll), and 21-325 (i) In order to be in good standing and to continue with the minor, a grade of at least a C is required in 36-235 There are many ways to get involved in Statistics at Carnegie Mellon: Statistics consists of two intertwined threads of inquiry: Statistical Theory and Data Analysis. The second schedule is an example of the case when a student enters the Minor through 36-235 and 36-236 (and therefore skips the beginning data analysis course). ELI BEN-MICHAEL, Assistant Professor (Joint Faculty with Heinz College), ZACHARY BRANSON, Assistant Teaching Professor Ph.D. in Statistics, Harvard University; Carnegie Mellon, 2019, DAVID CHOI, Assistant Professor of Statistics and Information Systems Ph.D., Stanford University; Carnegie Mellon, 2004, ALEXANDRA CHOULDECHOVA, Assistant Professor of Statistics and Public Policy Ph.D. , Stanford University; Carnegie Mellon, 2014, REBECCA DOERGE, Dean of Mellon College of Science, Professor of Statistics PhD, North Carolina State University; Carnegie Mellon, 2016, PETER FREEMAN, Associate Teaching Professor; Director of Undergraduate Studies Ph.D. , University of Chicago; Carnegie Mellon, 2004, MAX G'SELL, Associate Professor Ph.D., Stanford University ; Carnegie Mellon, 2014, CHRISTOPHER R. GENOVESE, Professor of Statistics Ph.D., University of California, Berkeley; Carnegie Mellon, 1994, JOEL B. GREENHOUSE, Professor of Statistics Ph.D., University of Michigan; Carnegie Mellon, 1982, AMELIA HAVILAND, Professor of Statistics and Public Policy Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003, JIASHUN JIN, Professor of Statistics Ph.D., Stanford University; Carnegie Mellon, 2007, BRIAN JUNKER, Professor of Statistics Ph.D., University of Illinois; Carnegie Mellon, 1990, ROBERT E. KASS, Maurice Falk Professor of Statistics & Computational Neuroscience Ph.D., University of Chicago; Carnegie Mellon, 1981, EDWARD KENNEDY, Associate Professor Ph.D., University of Pennsylvania; Carnegie Mellon, 2016, ARUN KUCHIBHOTLA, Assistant Professor PhD, University of Pennsylvania; Carnegie Mellon, 2020, MIKAEL KUUSELA, Assistant Professor PhD, Ecole Polytechnique Federale de Lausanne; Carnegie Mellon, 2018, ANN LEE, Professor, Co-Director of PhD program Ph.D., Brown University; Carnegie Mellon, 2005, JING LEI, Professor Ph.D., University of California, Berkeley; Carnegie Mellon, 2011, ROBIN MEJIA, Assistant Research Professor PhD, UC Berkeley; Carnegie Mellon, 2018, DANIEL NAGIN, Teresa and H. John Heinz III Professor of Public Policy Ph.D., Carnegie Mellon University; Carnegie Mellon, 1976, MATEY NEYKOV, Associate Professor Ph.D., Harvard University; Carnegie Mellon, 2017, NYNKE NIEZINK, Assistant Professor Ph.D., University of Groningen; Carnegie Mellon, 2017, REBECCA NUGENT, Department Head, Stephen E. and Joyce Fienberg Professor of Statistics & Data Science Ph.D., University of Washington; Carnegie Mellon, 2006, AADITYA RAMDAS, Assistant Professor PhD, Carnegie Mellon; Carnegie Mellon, 2018, ALEX REINHART, Assistant Teaching Faculty Ph.D., Carnegie Mellon University; Carnegie Mellon, 2018, ALESSANDRO RINALDO, Associate Dean for Research, Professor Ph.D., Carnegie Mellon; Carnegie Mellon, 2005, KATHRYN ROEDER, UPMC Professor of Statistics and Life Sciences Ph.D., Pennsylvania State University; Carnegie Mellon, 1994, CHAD M. SCHAFER, Professor Ph.D., University of California, Berkeley; Carnegie Mellon, 2004, TEDDY SEIDENFELD, Herbert A. Simon Professor of Philosophy and Statistics Ph.D., Columbia University; Carnegie Mellon, 1985, COSMA SHALIZI, Associate Professor Ph.D., University of Wisconsin, Madison; Carnegie Mellon, 2005, VALERIE VENTURA, Professor, Co-Director of PhD program Ph.D., University of Oxford; Carnegie Mellon, 1997, ISABELLA VERDINELLI, Professor in Residence Ph.D., Carnegie Mellon University; Carnegie Mellon, 1991, LARRY WASSERMAN, UPMC Professor of Statistics Ph.D., University of Toronto; Carnegie Mellon, 1988. Typical Schedule Although each department maintains its own course numbering practices, typically, the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. The Mathematical Foundations total is then 48-49 units. 36-200 and 36-202, or equivalents as listed above) can be replaced with anadditionalAdvanced Analysis and Methodology course, shown below in Sequence 2. Location: Baker Hall 129 However, the Statistics Director of Undergraduate Studies will provide advice and information to the student's advisor about the viability of a proposed substitution. ) and the laboratory sciences (36-247 A critical part of statistical practice is understanding the questions being asked so that appropriate methods of analysis can be used. - Conducting market research to identify customer needs and trends. Course Requirements. Students in this major are trained to advance the understanding of economic issues through the analysis, synthesis and reporting of data using the advanced empirical research methods of statistics and econometrics. For example, students intending to pursue careers in the health or biomedical sciences could take further courses in Biology or Chemistry, or students intending to pursue graduate work in Statistics could take further courses in advanced Mathematics. With respect to double-counting courses, it is departmental policy that students must have at least six courses (three Computer Science/Machine Learning and three Statistics) that do not count for their primary major. for 36-235 The requirements for the B.S. The final authority in such decisions rests there. Note: Students who enter the program with 36-235 or 36-236 should discuss options with an advisor. Jointly administered by the Department of Statistics & Data Science and the Undergraduate Economics Program, the major's curriculum provides students with a solid foundation in the theories and methods of both fields. *It is possible to substitute36-218, 36-219, 36-225,or 21-325 for 36-235. 36-236is the standard introduction to statistical inference. Leading-Edge Curriculum The online MSBA can be completed in 20 months and provides students with leading-edge knowledge, skills and experiential training in these areas: Methodology including machine learning and optimization Software Engineering including large-scale data management and programming in R and Python is the standard (and recommended) introduction to probability, 36-219 and data science to prevent, identify, diagnose, and rectify problems or outages that occur in our data processing pipelines. Each Carnegie Mellon course number begins with a two-digit prefix that designates the department offering the course (i.e., 76-xxx courses are offered by the Department of English). All courses used for satisfying Data Science Minor requirements must be numbered 5000 or higher with at least 6 credit hours numbered 6000 or higher. With respect to double-counting courses, it is departmental policy that students must have at least five statistics courses that do not count for their primary major. The completed form will be given to the Chair of the student's committee, e.g., at the B-exam. Were ready for you, too. **It is possible to substitute36-226or36-326(honors course) for36-236. In many of these cases, the student will need to take additional courses to satisfy the Statistics and Machine Learning major requirements. (i) In order to meet the prerequisite requirements, a grade of at least a C is required in36-235(or equivalent),36-236 It is the language in which statistical models are stated, so an understanding of probability is essential for the study of statistical theory. The theory reduces statistical problems to their essential ingredients to help devise and evaluate inferential procedures. 36-236is the standard (and recommended) introduction to statistical inference. In that cohort, 48% submitted an SAT score and 22% included an ACT result in their application. and take two of the following courses (one of which must be 400-level): *It is possible to substitute36-218,36-219 Carnegie Mellon University and ETH Zurich are two of the world's top universities for computer science, ranked 9th and 52nd in the QS World University Rankings 2023, respectively. Email: statadvising@andrew.cmu.edu Keep in mind that the program is flexible and can support other possible schedules (see footnotes below the schedule). . Please make sure to consult the Undergraduate Statistics Advisor prior to pursuing courses for the concentration area. In this major students take courses focused on skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. This is particularly true if the other major has a complex set of requirements and prerequisites or when many of the other major's requirements overlap with the requirements for a Major in Statistics and Machine Learning. * Note: The concentration/track requirement is only for students whose primary major is statistics and has no other additional major or minor. There is a variety of research projects in the department as well, and students who would like to pursue working on a project with faculty will need to contact that faculty directly to discuss that possibility. In 1967, it became the current-day Carnegie Mellon University through its merger with the Mellon . 36-200 draws examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. The MCDS program is designed for students with a degree in computer science, computer engineering or a related degree from a highly ranked university. assessment test if an acceptable alternative to completing, *The Beginning and Intermediate Data Analysis sequence (i.e. Students interested in pursuing a PhD in Statistics or Biostatistics (or related programs) after completing their undergraduate degree are strongly recommended to pursue the Mathematical Statistics Track. Students who choose to take36-225instead will be required to take36-226afterward, they will not be eligible to take36-236. Portugal Dual Ph.D. in CS Other courses may qualify as well; consult with the Statistics Undergraduate Advisor. Research in the department spans the gamut from pure mathematics to the hottest frontiers of science. Make sure to consult your Statistics Minor advisor regarding double counting. Although 21-240 Matrix Algebra with Applications is recommended for Statistics majors, students interested in PhD programs should consider taking 21-241 Matrices and Linear Transformations or 21-242 Matrix Theory instead. Advanced mathematics courses are encouraged. Faculty, graduate students, and undergraduates interact regularly. . Students should consider taking more than one course from the list of Machine Learning electives provided under the Computing section. This program is geared towards students interested in statistical computation, data science, or Big Data problems. While not required, students are strongly encouraged to take advantage of professional development opportunities and/or coursework. The Beginning Data Analysis courses give a hands-on introduction to the art and science of data analysis. The program can be tailored to prepare you for later graduate study in statistics, or to complement your interests in almost any field, including psychology, physics, biology, history, business, information systems and computer science. World-renowned for its contributions to statistical theory and practice, the Department of Statistics & Data Science is where imaginatively logical problem-solvers work collaboratively across disciplines, applying statistical tools to real-world challenges. The following sample program illustrates one way to satisfy the requirements of the Economics and Statistics Major.

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