36-236 is the standard (and recommended) introduction to statistical inference. Please note that students who complete36-235are expected to take36-236to complete their theory requirements. Students mostly do this through projects in specific courses, such as36-290,36-303, 36-490, 36-493, and/or36-497. 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. It is the language in which statistical models are stated, so an understanding of probability is essential for the study of statistical theory. In addition, Statistics majors gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration. 36-326is not offered every semester/year but can be substituted for 36-226and is considered an honors course. 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. Glenn Clune, Academic Program Manager 36-200 draws examples from many fields and satisfies the DC College Core Requirement in Statistical Reasoning. In this major students take courses focused on skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. To maximize your chances of success in the program, youshould consider which concentration area(s) you are best prepared for, based on youreducational background, work experience, and areas of interest as described in your Statement of Purpose. To satisfy the theory requirement take the following two courses: *It is possible to substitute36-218,36-219,36-225, or To apply to an Interdisciplinary degree program you need to apply via the online applicationfor the Ph.D. in CS and select the interdisciplinary program in the appropriate section of the online application. If students do not have at least five, they take additional advanced data analysis electives. 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 Rebecca Nugent, Department Head Mar 25 Greek Sing. However, the Statistics Director of Undergraduate Studies will provide advice and information to the student's advisor about the viability of a proposed substitution. statadvising@andrew.cmu.edu. Talented graduate students join the department from around the world, and add a unique dimension to the department's intellectual life. The courses cover similar topics but differ slightly in the examples they emphasize. This course is therefore recommended for students in the College. Browse all current Department of Statistics & Data Sciencecurriculums and courses. **All Special Topics are not offered every semester, and new Special Topics are regularly added. Pittsburgh, PA 15213 This schedule has more emphasis on statistical theory and probability. These core courses involve extensive analysis of real data with emphasis on developing the oral and writing skills needed for communicating results. Portugal Dual Ph.D. in CS Each semester comprises a minimum of 48 units. The Department Statistics does not provide approval or permission for substitution or waiver of another department's requirements. 36-236is the standard (and recommended) introduction to statistical inference. Where Am I in the Process? Current Computer Science Undergraduate Curriculum Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Economics and Statistics. Students who choose to take36-225will be required to take36-226afterward, they will not be eligible to take36-236. (36-235 Average SAT: 1510 The average SAT score composite at Carnegie Mellon is a 1510 on the 1600 SAT scale. Students in the College of Humanities and Social Sciences who wish to major or minor in Statistics are advised to complete both the calculus requirement (one Mathematical Foundations calculus sequence) and the Beginning Data Analysis course 36-200 by the end of their Freshman year. 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 The department gives students research experience through various courses focused on real world experiences and application. Students who choose to take36-225 instead will be required to take36-226 afterward, they will not be eligible to take 36-236. To have a shot at transferring into Carnegie Mellon, you should have a current GPA of at least 3.84 - ideally you're GPA will be around 3.99. 36-200 draws examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. The final authority in such decisions rests there. Many departments require Statistics courses as part of their Major or Minor programs. Carnegie Mellon University (CMU)'s average SAT score is 1465. This course is therefore recommended for students in the College. in Computer Science must take a minimum of 360 units in the following categories: Computer Science, Mathematics/Probability, Engineering and Natural Sciences, Humanities and Arts, Required Minor, Computing @ Carnegie Mellon and Free Electives. (i) In order to meet the prerequisite requirements, a grade of at least a C is required in36-235(or equivalent),36-236 Master of Data Analytics for Science is one of the best courses to choose from that offers in-depth learning in a Data Analytics and Quantitative Analysis. 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. is a rigorous Probability Theory course offered by the Department of Mathematics.). 36-235 is the standard introduction to probability, 36-219 is tailored for engineers and computer scientists, 36-218isa more mathematically rigorous class for Computer Science students and more mathematically advanced Statistics students (Statistics students need advisor approval to enroll),and21-325 is a rigorous Probability Theory course offered by the Department of Mathematics. 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 linear algebra requirement is a prerequisite for the course 36-401 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. These core courses involve extensive analysis of real data with emphasis on developing the oral and writing skills needed for communicating results. Amanda Mitchell,Academic Program Manager, Location: Baker Hall 129 Qualified students are also encouraged to participate in an advanced research project through 36-490 Undergraduate Research,36-493 Sports Analytics Capstone, or 36-497 Corporate Capstone Project. One of these courses is therefore recommended for students in the College. The completed form will be given to the Chair of the student's committee, e.g., at the B-exam. . Here, youll master skills in statistical theory, the interpretation and display of complex data, computing, and mathematics all of which adds up to an experience thats all kinds of gratifying. It is therefore essential to complete this requirement during your junior year at the latest! Sample program 2 is for students who have satisfied the basic calculus requirements and choose option 2 for their data analysis courses (see section #2). 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. Research in the department spans the gamut from pure mathematics to the hottest frontiers of science. Students can also pursue an independent study, or a summer research position. (or equivalent), and36-401. The objective of the course is to expose students to important topics in statistics and/or interesting applications which are not part of the standard undergraduate curriculum. Statistical Theory informs Data Analysis and vice versa. ***This is not an exhaustive list. . Learn more about our standardized test requirements. The comprehensive curriculum includes advanced analytics coursework in machine learning, structured and unstructured data analytics and predictive modeling. While these courses are not in Statistics, the concentration area must complement the overall Statistics degree. are intended only for students with a very strong mathematical background. , 36-225or 21-325 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. Location: Baker Hall 129 150-200 word essay describing how the proposed courses complement the Statistics degree. assessment test if an acceptable alternative to completing, *The Beginning and Intermediate Data Analysis sequence (i.e. The following sample program illustrates one way to satisfy the requirements of the Statistics and Machine Learning program. 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 (Mathematical Science Track). Students who elect Statistics (Neuroscience Track) as an additional major must fulfill all Statistics (Neuroscience Track) degree requirements. Students seeking transfer credit for those requirements from substitute courses (at Carnegie Mellon or elsewhere) should seek permission from their advisor in the department setting the requirement. Students should consider 36-326 Mathematical Statistics (Honors) as an alternative to 36-236 RON YURKO, Assistant Teaching Professor Ph.D., GEORGE T. DUNCAN, Professor of Statistics and Public Policy Ph.D., University of Minnesota; Carnegie Mellon, 1974, WILLIAM F. EDDY, John C. Warner Professor of Statistics Ph.D, Yale University; Carnegie Mellon, 1976, JOSEPH B. KADANE, Leonard J. In addition to the above, we assume that the applicant possesses (1) undergraduate-level mathematics, engineering, and science background depending on their particular field of undergraduate study; (2) comprehension skills to understand and appreciate the contexts in which complex engineering problems are solved; and (3) analytical, critical, and Depending on the department, xx-6xx courses may be either undergraduate senior-level or graduate-level, and xx-7xx courses and higher are graduate-level. The Statistics Concentration and the Operations Research and Statistics Concentration in the Mathematical Sciences Major (see Department of Mathematical Sciences) are administered by the Department of Mathematical Sciences with input from the Department of Statistics & Data Science. Carnegie Mellon accepts 7.26% transfer applicants, which is competitive. In that cohort, 48% submitted an SAT score and 22% included an ACT result in their application. 36-200 draws examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. The entire faculty, junior and senior, teach courses at all levels. This program is geared towards students interested in statistical computation, data science, or Big Data problems. *In each semester, "-----" represents other courses (not related to the major) which are needed in order to complete the 360 units that the degree requires. The requirements for the Major in Statistics (Mathematical Sciences Track) are detailed below and are organized by categories #1-#7. The Bachelor of Science in Statistics in the Dietrich College of Humanities and Social Sciences (DC) is a broad-based, flexible program that helps you master both the theory and practice of Statistics. Note that these courses require an application. Students must take two advanced Economics elective courses (numbered 73-300 through 73-495, excluding 73-374 ) and two (or three - depending on previous coursework, see Section 3) advanced Statistics elective courses (numbered 36-303, 36-311, 36-313,36-315, 36-318, 36-46x, 36-490, 36-493or 36-497). Students who choose to take36-225instead will be required to take36-226afterward, they will not be eligible to take36-236. Aug 2022 - Present9 months. 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. 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. Choose the path that fits you best. Majors in many other programs would naturally complement an Economics and Statistics Major, including Tepper's undergraduate business program, Social and Decision Sciences, Policy and Management, and Psychology. More news from the Center for Atmospheric Particle Studies . They leave with the passion, connections, credentials and lifelong friends who will help them change the world. Please note that students who complete36-235are expected to take36-236to complete their theory requirements. Decide which plan is best for you Additional Application Information These situations may have additional application requirements. One goal of the Statistics program is to give students experience with statistical research. ). The requirements for the Major in Statistics and Machine Learning are detailed below and are organized by categories. 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. Other courses emphasize examples in engineering and architecture (36-220 * Note: The concentration/track requirement is only for students whose primary major is statistics and has no other additional major or minor. If a waiver or substitution is made in the home department, it is not automatically approved in the Department of Statistics and Data Science. Youre not just one thing. Requirements The School of Computer Science requires the following for all applications: With respect to double-counting courses, it is departmental policy that students must have at least six courses [three Economics (73-xxx) and three Statistics (36-xxx)] that do not count for their primary major. Such courses offer one way to learn more about the Department of Statistics & Data Science and the field in general. Public Policy, Management & Data Analytics at Carnegie Mellon University Pittsburgh, Pennsylvania, United States 777 followers 500+ connections The following sample programs illustrate three (of many) ways to satisfy the requirements of the Statistics Major. An artist. The requirements for the Major in Statistics (Neuroscience Track) are detailed below and are organized by categories #1-#7. Three courses (3) from one area of concentration curriculum (36 units), Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units), Two (2) Electives: any graduate level course 600 and above in the School of Computer Science (24 units). The Department ofStatistics and Data Science also offers a series of workshops pertaining to resume preparation, graduate school applications, careers in the field, among other topics. 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. In many of these cases, the student will need to take additional courses to satisfy the Statistics and Machine Learning major requirements. 36-235 is the standard (and recommended) introduction to probability, 36-219 is tailored for engineers and computer scientists, 36-218isa more mathematically rigorous class for Computer Science students and more mathematically advanced (students need advisor approval to enroll), and 21-325 is a rigorous probability theory course offered by the Department of Mathematics. The Advanced Data Analysis courses draw on students' previous experience with data analysis and understanding of statistical theory to develop advanced, more sophisticated methods. This schedule has more emphasis on statistical theory and probability. in Statistics (Mathematical Sciences Track), Recommendations for Prospective PhD Students, Additional Major in Statistics (Mathematical Science Track), B.S. The final authority in such decisions rests there. **It is possible to substitute36-226or36-326(honors course) for36-236. **It is possible to substitute36-226 or36-326 for36-236. Students who elect Statistics as a second or third major must fulfill all Statistics degree requirements except for the Concentration Area requirement. Academic Requirements and Credit for College-level Work. Please note that students who take36-235are expected to take36-236to complete their theory requirements. 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. Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Statistics and Machine Learning. All MCDS students must complete 144 units of graduate study which satisfy the following curriculum: Professional Preparation a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. All three require the same total number of course credits split among required core courses, electives, data science seminar and capstone courses. 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. More events. If students do not have at least three ECON and three STA classes, they will need to take additional advanced data analysis or economics electives, depending on where the double-counting issue is. Minimum english score required TOEFL 100 IELTS 7.5 Duolingo 120 . While all Majors in Statistics are given solid grounding in computation, extensive computational training is really what sets the Major in Statistics and Machine Learning apart. Students should consider taking more than one course from the list of Machine Learning electives provided under the Computing section. is a rigorous probability theory course offered by the Department of Mathematics. Must take prior to 36-401 Modern Regression, if not, an additional Advanced Statistics Elective is required. All Special Topics are not offered every semester. The Department augments all these strengths with a friendly, energetic working environment and exceptional computing resources. Students are rigorously trained in fundamentals of engineering, with a strong bent towards the maker culture of learning and doing.
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