University of Michigan Data Science Ranking

Last Updated on August 2, 2022

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We at Collegelearners have all the information that you need about best colleges for data science undergraduate.

What Does a Data Scientist Do? | Role & Responsibilities

master of applied data science university of michigan review

Data Science is often viewed as the confluence of (1) Computer and Information Sciences (2) Statistical Sciences, and (3) Domain Expertise. These three pillars are not symmetric: the first two together represent the core methodologies and the techniques used in Data Science, while the third pillar is the application domain to which this methodology is applied. In this program, core data science training is focused on the first two pillars, along with practice in applying their skills to address problems in application domains.

MS in Data Science from University of Michigan-Dearborn- Fees,  Requirements, Ranking, Eligibility, Scholarship

We characterize the required Data Science skills in two categories: statistical skills, such as those taught by the Statistics and Biostatistics departments, and computational skills, such as those taught by the Computer Science and Engineering Division and the School of Information. The design of the program is to require every student to receive balanced training in both areas. To create an academic plan that achieves this balance, and to foster a greater sense of shared community, we do not intend to offer any sub-plans or tracks within the proposed degree program. Rather, we will expect graduates of this program to understand data representation and analysis at an advanced level. 

With the MS in Data Science all students will be able to: identify relevant datasets, apply the appropriate statistical and computational tools to the dataset to answer questions posed by individuals, organizations or governmental agencies, design and evaluate analytical procedures appropriate to the data, and implement these efficiently over large heterogeneous data sets in a multi-computer environment.

University Of Michigan Data Science Ranking

The University of Michigan-Ann Arbor has 43,400 enrolled students and was ranked #12 in the world by the Times of London and #1 among public universities. U-M’s campus consists primarily of historic brick-and-stone buildings located in Ann Arbor, approximately 45 minutes west of Detroit, Michigan. U.S. News and World Report ranked U-M #28 among national universities in its 2018 edition of Best Colleges. Students at U-M may join one of more than 1,500 student organizations, including 62 Greek organizations. Whether students live on- or off-campus, they enjoy cultural pursuits including music and theatre. U.S. News and World Report says that the town of Ann Arbor is a top university community offering diverse food culture and nightlife.

MS in Data Science

About the Program

The Data Science master’s degree program is designed as a 30-credit hour interdisciplinary graduate program. The curriculum consists of required core courses and technical electives, providing opportunities to build knowledge and professional skills in various Data Science areas that are highly demanded in the current job market. Four concentrations are recommended (not mandatory) for students with different interests in Data Science: Computational Intelligence Concentration, Applications Concentration, Business Analytics Concentration, and Big Data Informatics Concentration.

This program provides students with opportunities to choose from a variety of courses offered by relevant departments from all four colleges at UM-Dearborn to fulfill students’ specific career objectives in Data Science. These courses have access to a wide variety of computing and other resources across different units at the university. 

The program may be completed entirely on campus or through a combination of on-campus and online courses. 

The University of Michigan’s School of Information (#1 ranked in Information Systems, U.S. News & World Report 2018) has a long-established partnership with Coursera, and its faculty are experts at teaching online. More than one million learners have taken courses online from UMSI faculty since launching on Coursera.

University of Michigan--Ann Arbor - Best Science Schools - US News


The University of Michigan’s School of Information prepares students to be leaders in the field. Graduates from the on-campus Master of Science in Information program have a 98%+ job placement rate and go on to become data scientists at places like Google, Facebook, and Amazon.

Data Science and Analytics at University of Michigan – Ann Arbor

The Master of Science in Information (MSI) degree at UMSI includes areas of interest in Data Science/Data Analytics/Computational Social Science, Digital Archives and Library Science/Preservation, and User Experience (UX) Research and Design/Human-Computer Interaction (HCI)/Social Computing. Students may also select a Master of Health Informatics program focused on data science in healthcare environments.

university of michigan world ranking

University of Michigan–Ann Arbor is ranked #17 in Best Global Universities. Schools are ranked according to their performance across a set of widely accepted indicators of excellence.

Global Universities Rankings

  • #17inBest Global Universities (tie)

Subject Rankings

  • #9inArts and Humanities
  • #13inBiology and Biochemistry
  • #57inBiotechnology and Applied Microbiology (tie)
  • #14inCardiac and Cardiovascular Systems
  • #26inCell Biology
  • #67inChemical Engineering
  • #40inChemistry
  • #54inCivil Engineering
  • #12inClinical Medicine
  • #10inComputer Science
  • #10inEconomics and Business
  • #88inElectrical and Electronic Engineering
  • #23inEndocrinology and Metabolism
  • #51inEnergy and Fuels (tie)
  • #19inEngineering
  • #33inEnvironment/Ecology
  • #16inGastroenterology and Hepatology
  • #66inGeosciences
  • #33inImmunology
  • #12inInfectious Diseases
  • #44inMaterials Science
  • #21inMathematics
  • #29inMechanical Engineering
  • #43inMicrobiology (tie)
  • #20inMolecular Biology and Genetics
  • #71inNanoscience and Nanotechnology (tie)
  • #44inNeuroscience and Behavior (tie)
  • #9inOncology
  • #19inPharmacology and Toxicology
  • #27inPhysics
  • #75inPlant and Animal Science (tie)
  • #13inPsychiatry/Psychology
  • #24inPublic, Environmental and Occupational Health
  • #38inRadiology, Nuclear Medicine and Medical Imaging
  • #6inSocial Sciences and Public Health
  • #30inSpace Science
  • #8inSurgery (tie)
CMU will offer master's degree in engineering
Massachusetts Institute of Technology (MIT)Cambridge, MA, United States111
University of OxfordOxford, ENG, United Kingdom254
Stanford UniversityStanford, CA, United States322
University of CambridgeCambridge, ENG, United Kingdom377
Harvard UniversityCambridge, MA, United States533
California Institute of Technology – CaltechPasadena, CA, United States645
Imperial College LondonLondon, ENG, United Kingdom789
ETH Zurich – Swiss Federal Institute of TechnologyZürich, Switzerland866
University College London (UCL)London, ENG, United Kingdom8108
University of ChicagoChicago, IL, United States10910
National University of SingaporeSingapore, Singapore111111
Nanyang Technological UniversitySingapore, Singapore121311
University of PennsylvaniaPhiladelphia, PA, United States131615
Swiss Federal Institute of Technology in LausanneLausanne, Switzerland141418
Yale UniversityNew Haven, CT, United States141717
The University of EdinburghEdinburgh, SCT, United Kingdom162020
Tsinghua UniversityBeijing, China171516
Peking UniversityBeijing, China182322
Columbia UniversityNew York City, NY, United States191918
Princeton UniversityPrinceton, NJ, United States201213
Cornell UniversityIthaca, NY, United States211814
The University of Hong KongHong Kong, Hong Kong (SAR)222225
The University of TokyoTokyo, Japan232422
University of MichiganAnn Arbor, MI, United States232121

university of michigan math ranking

In College Factual’s most recent rankings for the best schools for math majors, U-M came in at #18. This puts it in the top 5% of the country in this field of study. It is also ranked #1 in Michigan.

Ranking TypeRank
Most Popular Bachelor’s Degree Colleges for Mathematics7
Most Popular Doctor’s Degree Colleges for Mathematics8
Best Mathematics Doctor’s Degree Schools14
Best Mathematics Bachelor’s Degree Schools16
Most Popular Master’s Degree Colleges for Mathematics18
Best Mathematics Master’s Degree Schools20
Most Focused Doctor’s Degree Colleges for Mathematics49
Best Value Doctor’s Degree Colleges for Mathematics106
Most Focused Master’s Degree Colleges for Mathematics134
Best Value Master’s Degree Colleges for Mathematics218
Most Focused Bachelor’s Degree Colleges for Mathematics248
Best Value Bachelor’s Degree Colleges for Mathematics301
MS in Data Science from University of Michigan-Dearborn- Fees,  Requirements, Ranking, Eligibility, Scholarship

Popularity of Math at U-M

During the 2019-2020 academic year, University of Michigan – Ann Arbor handed out 215 bachelor’s degrees in mathematics. Due to this, the school was ranked #5 in popularity out of all colleges and universities that offer this degree. This is an increase of 36% over the previous year when 158 degrees were handed out.

In 2020, 20 students received their master’s degree in math from U-M. This makes it the #22 most popular school for math master’s degree candidates in the country.

In addition, 17 students received their doctoral degrees in math in 2020, making the school the #9 most popular school in the United States for this category of students.

masters in data science ranking

  1. MIT’s Master of Business Analytics. The program is tailored for current students or recent college graduates who plan to pursue a career in the data science industry, as well as those seeking career advancement or change, especially engineers, mathematicians, physicists, computer programmers, and other high-tech professionals. (12-month program, 57,350 USD for the entire program’s tuition, CS Rank: 1)
  2. Stanford’s MS in Statistics: Data Science. The Data Science track develops strong mathematical, statistical, computational and programming skills through the general master’s core and programming requirements, in addition to providing fundamental data science education through general and focused electives requirement from courses in data sciences and related areas. (24-month program, $32,658 per year, CS Rank: 2)
  3. Carnegie Mellon’s Master of Computational Data Science. This program equips you with the skills and knowledge you need to develop the layers of technology involved in the next generation of massive information system deployments and analyze the data those systems generate. (16-month program, $75,484 entire program, CS Rank: 3)
  4. Harvard’s Master of Science in Data Science. The program will offer strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  The program will also focus on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science. (18-month program, $44,816 full tuition, CS Rank: 6)
  5. U. of Toronto’s Master of Science in Applied Computing (Data Science Concentration) is offered jointly by Depts of CS and Statistical Sciences and teaches Data science at the interface between computer science and statistics. (24-month program, $11,320 citizens, $27,590 International, per year, CS Rank: 10)
  6. University of Washington’s Master of Science in Data Science. This master’s program gives you the technical skills to extract knowledge from large, noisy, and heterogeneous datasets — big data — to provide insights that people and organizations can use. In this program, you’ll build deep expertise in managing, modeling and visualizing big data to meet the growing needs of industry, government, nonprofit and research organizations today. (17.5-month program, $46,125 full tuition, CS Rank: 16)
  7. University of British Columbia’s Master of Data Science. A professional program developed by the combined expertise of the UBC departments of Computer Science and Statistics to help meet this need and give students a fast track to a great career. Utilizing descriptive and prescriptive techniques, students extract and analyze data from both unstructured and structured forms and then communicate the findings of those analyses in ways to enable organizations to make informed decisions based on data. (10-month program, $24,033 Residents; $32,675 International, CS Rank: 23)
  8. The University of Texas at Austin’s Master of Science in Business Analytics.  A 10-month program that will show you how to harness vast amounts of data and use it to build better business. (10-month program, $43,000 In-state, $48,000 Out-of-state for full program, CS Rank: 26)
  9. Georgia Tech’s MS in Analytics. An interdisciplinary degree program that leverages the strengths of Georgia Tech in statistics, operations research, computing, and business by combining the world-class expertise of the Scheller College of Business, the College of Computing, and the College of Engineering. By blending the strengths of these nationally ranked programs, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into analytics problems. (24-month program, online option available, $43,416 In-state, $59,940 Out-of-state, $9,900 Online, CS Rank: 28)
  10. Columbia University’s Master of Science in Data Science. This program allows students to apply data science techniques to their field of interest, building on four foundational courses. Our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. (18-month program, $58,080 full tuition, CS Rank: 29)
10 Analytics / Data Science Masters Program | Top Universities in US

university of michigan ann arbor acceptance rate

Who is this degree for

This degree is designed for candidates who want to take on real-world data challenges and gain a comprehensive understanding of how data is collected, processed, analyzed, visualized, and reported across a variety of industries. Applicants should be proficient with statistics and Python. Students will learn how to apply these skills in ways that help organizations become more effective, strategic, ethical, and successful.

We are interested in applicants with a history of academic and/or professional success, who demonstrate creativity and a commitment to sophisticated problem solving, and who exhibit persistence, leadership, and initiative.

Applicants for the degree program must have:

  • An undergraduate degree
  • Strong knowledge of Python programming language basic and introductory statistics (Applicants will take two assessments)
  • TOEFL exam for non-native speakers of English
  • No Graduate Record Exam (GRE) or other exams required

Note: if you have completed a three-year bachelor’s degree, please email [email protected] to inquire if this would be recognized as an equivalent degree.

Application Process

Applications for the Winter 2021 cohort are now closed.

You must submit an application linked through the University of Michigan School of Information website, and include the following with your application:

  • Transcript(s)
  • Resume
  • Short essays (3)
  • At least one letter of recommendation
  • Students whose primary language is not English will need to submit a TOEFL score

Our aim is to learn about your personal motivation and skills, and to understand how the program will help you achieve your professional goals.

Application requirements


  • One scanned or electronic transcript uploaded to the online application or mailed directly to us for all undergraduate and/or graduate programs from which you have degree(s).
  • We will accept unofficial transcripts for application review. Official transcripts will be required later if you are admitted.


  • A copy of your current resume/CV.


The MADS Program requires three short essays to get to know our applicants in more depth. Typically, we inquire about personal background, programming experience, and interest in data science and the UMSI MADS program. Example questions we’ve asked in the past include:

  • Why are you interested in the Master of Applied Data Science degree with UMSI, and what do you hope to gain with this degree?
  • Describe a data-oriented problem you’ve solved, or problem you’d like to solve. What steps did you take, or could be taken, to identify, design, and implement a solution?
  • What do you believe is the greatest potential value and greatest potential harm of data science?


Python and Statistics Proficiency Requirements

After applying to the Master of Applied Data Science program applicants are required to demonstrate their foundational Python fluency (roughly equivalent to UMSI’s Python 3 Programming MOOC) and basic statistics knowledge. Applicants may do this in one of two ways:

1: Take a combined Python and Statistics Assessment

  • We expect this assessment can be completed in about 1.5 hours and contains questions similar to the practice assessments applicants are encouraged to take as they prepare for the assessment. Generally, applicants take at least two weeks to prepare and successfully complete this assessment.

2: Submit a certificate of completion of the Python 3 Programming MOOC and take a Statistics Assessment

  • If applicants have already completed the entire Python 3 Programming specialization that is offered on Coursera by the time of application, applicants will only be tested on introductory statistics material. Please upload your Python 3 Programming specialization certificate at the time of application submission.

We strongly encourage applicants to take the practice assessments hosted in the assessment course shells to understand the foundational level of proficiency in Python and statistics that are necessary to be successful in the program. We also provide links to free or low-cost online courses and resources for those who do not already have the necessary experience in these areas, or those looking to refresh their knowledge.

University of Michigan--Ann Arbor - Best Science Schools - US News

Non-Native English Speaker Assessment

We require the Test of English as a Foreign Language (TOEFL) if English is your non-primary language. A score of at least 100 on the Internet-based test (IBT) is required. Applicants holding a Bachelor’s or Master’s degree are exempt from taking an English proficiency examination if one or more of the following criteria is met:

  • You are a native English speaker;
  • You completed all of your undergraduate and/or graduate education and earned a degree at an institution where all classes are taught exclusively in English;
  • You are a current University of Michigan student.

If, for example, an applicant completed two years of an undergraduate degree program at a non-English speaking institution and then transferred to an English speaking institution, they do not meet the criteria for exemption. Similarly, being a U.S. citizen or a U.S. permanent resident does not automatically exempt an applicant from taking TOEFL. If the applicant’s first language is not English, the applicant must meet the exemption or submit their TOEFL score.

No Additional Tests Required!

We do not require the GRE or any other additional tests as part of our admissions process.

Letters of Recommendation

  • One letter of recommendation from people who can best speak to your ability to be successful in the program. Either academic or professional references are welcome.

Application Fees

  • $75.00 USD for U.S. citizens and permanent residents
  • $90.00 USD for international applicants applies.

University Of Michigan Data Science Courses

Students must take the following core courses (unless waived by the course review process):

  • MATH 403: Introduction to Discrete Mathematics
  • EECS 402: Programming for Scientists and Engineers
  • EECS 403: Data Structures for Scientists and Engineers

1 of the following

  • BIOSTATS 601: Probability and Distribution
  • STATS 425: Introduction to Probability
  • STATS 510: Probability and Distribution

1 of the following

  • BIOSTATS 602: Biostatistical Inference
  • STATS 426: Introduction to Theoretical Statistics
  • STATS 511: Statistical Inference

All Students must take the following core courses:

EECS 409: Data Science Colloquium

Expertise in Data Management and Manipulation

1 of the following

  • EECS 484: Database Management Systems
  • EECS 584: Advanced Database Systems

1 of the following

  • EECS 485: Web Systems
  • EECS 486: Information Retrieval and Web Search
  • EECS 549/SI 650: Information Retrieval
  • SI 618: Data Manipulation Analysis
  • STATS 507: Data Science Analytics using Python

Expertise in Data Science Techniques

1 of the following:

  • BIOSTAT 650: Applied Statistics I: Linear Regression
  • STATS 500: Statistical Learning I: Linear Regression
  • STATS 513: Regression and Data Analysis

1 from the following:

  • STATS 415: Data Mining and Statistical Learning
  • STATS 503: Statistical Learning II: Multivariate Analysis
  • EECS 545: Machine Learning
  • EECS 476: Data Mining
  • EECS 576: Advanced Data Mining
  • SI 670: Applied Machine Learning
  • SI 671: Data Mining: Methods and Applications
  • BIOSTAT 626: Machine Learning for Health Sciences


  • STATS 504: Principles and Practices in Effective Statistical Consulting
  • STATS 750: Directed Reading
  • EECS 599: Directed Study
  • SI 599-00X: Computational Social Science
  • SI 691: Independent Study
  • SI 699-004: Big Data Analytics
  • BIOSTAT 610: Reading in Biostatistics
  • BIOSTAT 629: Case Studies for Health Big Data
  • BIOSTAT 698: Modern Statistical Methods in Epidemiologic Studies
  • BIOSTAT 699: Analysis of Biostatistical Investigations


Select 1 from each competency. Students may not double-count a course in multiple categories. Electives group must include at least two advanced graduate courses. 

Principles of Data Science

BIOSTAT 601 (Probability and Distribution Theory) | BIOSTAT 602 (Biostatistical Inference) | BIOSTAT 617 (Sample Design) | BIOSTAT 626 (Machine Learning for Health Sciences) | BIOSTAT 680 (Stochastic Processes) | BIOSTAT 682 (Bayesian Analysis) | EECS 501 (Probability and Random Processes) | EECS 502 (Stochastic Processes)  EECS 505 (Computational Data Science and Machine Learning) | EECS 551 (Matrix Methods for Signal Processing, Data Analysis and Machine Learning) | EECS 553 (Theory and Practice of Data Compression) | EECS 564 (Estimation, Filtering, and Detection) | SI 670 (Applied Machine Learning) | STATS 451 (Introduction to Bayesian Data Analysis) | STATS 470 (Introduction to Design of Experiments) | STATS 510 (Probability and Distribution Theory) | STATS 511 (Statistical Inference) | STATS 551 (Bayesian Modeling and Computation)

Data Analysis

BIOSTAT 645 (Time series) | BIOSTAT 651 (Generalized Linear Models) | BIOSTAT 653 (Longitudinal Analysis) |BIOSTAT 665 (Population Genetics) | BIOSTAT 666 (Statistical Models and Numerical Methods in Human Genetics) | BIOSTAT 675 (Survival Analysis) | BIOSTAT 685 (Non-parametric statistics) | BIOSTAT 695 (Categorical Data) | BIOSTAT 696 (Spatial statistics) | EECS 556 (Image Processing) | EECS 559 (Advanced Signal Processing) | EECS 659 (Adaptive Signal Processing) | STATS 414 (Topics in Applied Data Analysis | STATS 501 (Statistical Analysis of Correlated Data) | STATS 503 (Statistical Learning II: Multivariate Analysis) | STATS 509 (Statistics for Financial Data) | STATS 531 (Analysis of Time Series) | STATS 600 (Linear Models) | STATS 601 (Analysis of Multivariate and Categorical Data) | STATS 605 (Advanced Topics in Modeling and Data Analysis) | STATS 700 (Topics in Applied Statistics) 


BIOSTAT 607 (Basic Computing in Data Analytics) | BIOSTAT 615 (Statistical Computing) | BIOSTATS 625 (Computing with Big Data) | EECS 481 (Software Engineering) | EECS 485 (Web Systems) | EECS 486 (Information Retrieval and Web Search) | EECS 490 (Programming Langiages) | EECS 493 (User Interface Development) | EECS 504 (Computer Vision) |EECS 542 (Advanced Topics in Computer Vision) | EECS 549/SI 650 (Information Retrieval) | EECS 548/SI 649 (Information Visualization) | EECS 586 (Design and Analysis of Algorithms) | EECS 587 (Parallel Computing) | EECS 592 (Artificial Intelligence) | EECS 595/SI 561 (Natural Language Processing) | SI 608 (Networks) | SI 618 (Data Manipulation and Analysis | SI 630 (Natural Language Processing (Algorithms and People) | SI 671 (Data Mining: Methods and Applications) | STATS 406 (Computational Methods in Statistics and Data Science) | STATS 507 (Data Science Analytics using Python) | STATS 506 (Computational Methods and Tools in Statistics) | STATS 606 (Statistical Computing) | STATS 608 (Monte Carlo Methods and Optimization Methods in Statistics) 

Program Notes

·         At least 25 units of graduate-level coursework must be completed during residency in the Data Science program. Of these 25, 18 must be at the advanced graduate level (500 level or above in LSA, UMSI, and CoE, and 600 level or above in SPH).

·         Expertise in Data Science Techniques part 1 can be fulfilled by STATS 413 if taken before program start.

·         Expertise in Data Science Techniques part 2 can be fulfilled by EECS 445 if taken before program start.

·         Program requirements may be fulfilled by

               -Completing the above courses with a B- or better

               -Having taken an approved equivalent class in prior education with a B- or better

               -Through passing tests explicitly devised for proving such competence (i.e. professional certifications).

Accelerated Master’s Degree Program

The AMDP option will award a Master’s degree in Applied Statistics for highly motivated students working towards their Bachelor’s degree in their senior year, plus one more year of graduate study in Statistics.

The Applied Master’s program in Statistics emphasizes statistical theory, modeling, computing and data analysis with a modern curriculum. Some of the students go on to pursue doctoral degrees in statistics, biostatistics, computer science, economics, and other fields, while many others are employed by the private sector upon graduation from the program.

university of michigan data science master’s Application requirements

Students must be enrolled at the University of Michigan for a bachelor’s degree with a strong quantitative background with a UGPA > 3.2. Majors in Statistics and in Data Science at the University of Michigan will prepare the students well for the AMDP option.

We require applicants to complete the following courses by the end of their junior year: STATS 250/280, STATS 306, and [(STATS 425 and STATS 426) or STATS 412], or equivalents of these courses.

Applicants must show sufficient proficiency in computer programming. This may be satisfied by courses taken, such as EECS 182 or EECS 183, or a letter of support from a former employer or advisor.

Students must get approval from their current primary faculty advisor before applying. The GRE is not required.

Application process

  • Students typically apply in the 2nd semester of their junior year.
  • Applicants must complete a Rackham AMDP application form.
  • Complete applications are due April 1.
  • As part of the application process, prospective students must provide:

1) At least one letter of support from a course instructor

2) An academic statement of purpose, explaining the reason for interest in the Applied Statistics Master’s Program, as well as a personal statement of purpose

3) A plan for the course of study to complete their undergraduate and Master’s degrees

4) A current undergraduate transcript

• Statistics MS Admissions Committee will review the applications.

Acceptance notification:

• Applicants will be notified by May 15 of the junior year regarding admission.

• Accepted applicants will be required to meet with one of the MS Advisors as early as possible, but no later than the start of the fall semester to review the tentative course of study plan (included with the application).


A student must complete the following courses by the end of their senior year:

  • STATS 413: Applied Regression Analysis
  • STATS 415: Introduction to Data Mining and Statistical Learning

Note these courses may be counted for the student’s Bachelor’s degree, but they will not be counted for the Master’s degree.

Course Requirements include at least 10 courses for a total of 30 credit hours for the Master’s degree.

Students must take each of the following core courses:

  • STATS 503: Statistical Learning II: Multivariate Analysis
  • STATS 504: Principles and Practices in Effective Statistical Consulting
  • STATS 510: Probability and Distribution Theory
  • STATS 511: Statistical Inference

Note that STATS 500 Statistical Learning I: Regression is no longer listed as a required core course (but rather as an elective).

Further, students must take at least five (3 credit) elective courses:

  • STATS 406: Advanced Statistical Computing
  • STATS 414: Topics in Statistics
  • STATS 430: Applied Probability
  • STATS 451: Bayesian Data Analysis
  • STATS 500: Statistical Learning I: Regression*
  • STATS 501: Applied Statistics II
  • STATS 506: Computational Methods and Tools in Statistics
  • STATS 507 Data Science and Analytics using Python
  • STATS 509: Statistics for Financial Data
  • STATS 526: Discrete State Stochastic Processes
  • STATS 531: Modeling and Analysis of Time Series Data
  • STATS 535: Reliability
  • STATS 547: Probabilistic Modeling in Bioinformatics
  • STATS 551: Topics in Bayesian Modeling and Computation (Pending Approval)
  • STATS 560: Introduction to Nonparametric Statistics
  • STATS 570: Design of Experiments
  • STATS 580: Methods and Theory of Sample Design
  • STATS 607: Statistical Computing
  • BIOSTAT 615: Statistical Computing
  • BIOSTAT 675: Survival Analysis
  • BIOSTAT 682: Applied Bayesian Inference
  • BIOSTAT 695: Analysis of Categorical Data
  • BIOSTAT 696: Spatial Statistics
  • Any approved STATS 600-level or above courses

Cognate Courses

Students may take up to 6 credits (equivalently, two courses) from departments other than Statistics or Biostatistics to fulfill the elective coursework requirement, with prior approval from their advisor

Additional Degree Information

As an AMDP student, some of the requirements for a Master’s degree in Applied Statistics can be satisfied while completing the requirements for their Bachelor’s degree.

• Students in the AMDP will begin graduate-level coursework in their fourth year of study.

• Students do not enroll in the Rackham Graduate School until their fifth year of study, and must have completed or be within 6 credit hours of completing the undergraduate degree by the end of their senior year. Students normally apply for their undergraduate degree at the end of the semester in which their degree requirements are met.

• A maximum of 15 credit hours may be taken outside of the Rackham registration and counted towards the Master’s degree. Of those 15 credits, a maximum of 9 credits used for the undergraduate degree may be double-counted to meet the Master’s degree requirements, provided that these credits were not used to meet the requirements for the undergraduate major and that the student received a “B” or better in each of the courses.

• During their fifth year of study, students must enroll in and complete two full terms (with 9 or more credits each term) as a Rackham Graduate student for the Master’s degree. AMDP students must pay Rackham fees for all classes taken during the two terms when they are classified as graduate students.

• Students must maintain satisfactory progress towards their degrees, and maintain a GPA of 3.0 (“B”) or better.

Sample time-line

• Year 3, term 2: Meet with an advisor and submit AMDP application (deadline: April 1 / notification of acceptance: May 1)

• Year 4, terms 1 and 2: Meet with Statistics MS Advisors to discuss course options. Enroll as advanced undergraduate and begin MS program courses while continuing undergraduate major courses.

• Spring/Summer between years 4 and 5: Industrial or research internship. (Optional but highly recommended.)

• Year 5, terms 1 and 2: Meet with Statistics MS Advisors to discuss course options. Enroll as a Rackham Graduate Student to complete MS program courses.

End of April: Conferral of graduate (M.S.) degree.

Advising, Mentoring and Student Community

All AMDP students will be advised by the Statistics Master’s Advisors, and they will be fully incorporated into the student community and have equal access to the facilities and resources of the department. The Advisors will develop materials and activities specifically for AMDP students and meet with them individually either before or at the beginning of the fall semester of every school year to discuss the study plan.  AMDP students will take at least two core courses (STATS 510 and STATS 511) in their senior year, together with all first-year regular Master’s students. These common courses will facilitate interaction between AMDP and other students in the same cohort and help build a strong and inclusive student community.

Top 5 Universities for MS in Data Science

Top Universities for MS Data Science in USA 

1. Columbia University

The Master of Science in Data Science at Columbia is run by the Data Science Institute at Columbia University. Being an Ivy League institution, there are no questions about its reputation. The MS program provides a good foundation for machine learning and programming along with practical experience. The students will have the opportunity to conduct original research, included in a capstone project, and interact with Columbia’s industry partners and faculty. The program revolves more around the technical aspects and does not cover business modules to a larger extent. However, students may also choose an elective track focused on entrepreneurship or a subject area covered by one of the institute’s seven centers.

2. New York University

NYU was the first university in the world to offer an MS in Data Science program and is one of the best in the world. The MS program is being run by the Center for Data Science at NYU. It is a highly selective program for students with a strong background in mathematics, computer science, and applied statistics. The degree focuses on the development of new methods for data science.

The students have access to courses from a wide range of departments including statistics, machine learning, artificial intelligence (AI), biostatistics, business, economics, psychology etc. Students get the opportunity to work closely with folks in other fields to apply data science to solving real-world problems. You will get to study tracks like Big Data, NLP (natural language), mathematics, data, and physics depending on your interest and career goals. There is ample opportunity for practical training in the form of an internship semester and a capstone project. Besides, the department conducts workshops, tech talks and other events in collaboration with industry professionals. The acceptance rate for NYU data science program is around 15%. Although, the acceptance rate was 5% in 2014 & 2015.

3. Carnegie Mellon University

Carnegie Mellon University (CMU) is one of the most reputed and top universities for research in computer science in the world.  The MS in Computational Data Science at CMU trains students in all aspects of design, engineering, and deployment of very large information systems. Students will cover topics like databases, distributed algorithms and storage, machine learning, language technologies, software engineering, human-computer interaction, and design.

Students will develop a unified vision of very large information systems through cover core classes and electives. Additionally, the internship and capstone project requirements ensure that students are ready for the competitive job market. Tech giants such as Google and Amazon come to the campus to sign up students for placements and internships. So as you would expect, competition for admission is fierce, with an acceptance rate of around 10%. But, CMU is unarguably one of the best universities in the US for MS in Data Science.

4. UC Berkeley

UCB’s MS in Information and Data Science (MIDS) program is a new degree intended for professionals who want to learn how to solve real-world problems in the big data world. Unlike existing programs that focus on advanced mathematics and modeling alone, the curriculum provides students with insights from social science and policy research, as well as statistics, computer science, and engineering. Courses include statistics for data science, applied machine learning and data visualization. Applicants are expected to have a working knowledge of the Python programming language before beginning the course.

Although all coursework is delivered online, students are expected to attend a 4-5 day immersion session on the UC Berkeley campus. Students generally complete the program in 20 months by studying two courses per semester but this can be accelerated to 12 months by taking three courses per semester. MIDS is a uniquely designed program to empower you with the knowledge and relationships you’ll need to accelerate your career wherever you may work and earn a degree simultaneously. It combines the best of on-campus and online education, in an intimate, collaborative environment.

5. Stanford University

The MS in Statistics: Data Science program at Stanford is one of the finest in the lot. Students will develop a broad understanding of data science, statistical modeling, programming, and data mining. There is also scope for specializing in machine learning, big data in medicine, business intelligence, and distributed data management. Upon the successful completion of the MS program, students can also undertake Ph.D. in Statistics, ICME, MS&E, or Computer Science at Stanford (but of course, no guarantee).

Being located in Silicon Valley (Palo Alto, California), students are ideally placed to pursue work experience and internships with the many tech giants. So, if you are looking at to do MS in Data Science in USA, Stanford must be on your list. Based out of Palo Alto,  Stoodnt is working on Machine Learning and Artificial Intelligence as well. So, you could also end up in an internship with Stoodnt.

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