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If you’re considering going to graduate school, the first thing that comes to mind is probably the cost. But once you’ve determined how much you can afford and that you have the grades and credentials necessary, what’s next?
There are many things to consider when applying to graduate school, including whether or not your field of study has a high graduate school acceptance rate at specific institutions. The graduate school acceptance rate tells you how many applicants have been accepted by an institution in a given academic year. This information can help you determine which programs might be best for you.
When looking at these numbers, keep in mind that even the most qualified and confident applicants are concerned about going to graduate school. But don’t worry! Graduate school acceptance rates can help you determine how likely you are to be admitted to a given program.
stanford university data science
Data Science Degrees offered at Stanford University
Students who want to study Data Science or any relatable field should consider Stanford University, as they have a variety of degree options, both online and traditional. Those who want a major in the social or humanities sciences can also have a Data Science minor that can help them gain more information about analytical methods for statistical data. Students can choose an undergraduate or graduate degree program in the humanities or social sciences (Bachelor’s Master’s, Doctorate) with a minor in Data Science. Students can also choose an undergraduate major (Bachelor’s) in Statistics with a Data Science minor.
The Stanford University website has an extensive list of degree programs available at the university, including several programs relevant to Data Science. For example, there are four different types of undergraduate degrees: Bachelor of Arts/Science (BA/BS), Bachelor of Arts (BA), Bachelor of Science (BS), and Bachelor of Social Sciences (BSS), which all provide students with different options for their education.
For those interested in pursuing an undergraduate degree program with a Data Science minor, there are two options available: Statistics and Computer Sciences & Engineering (CSE). The former requires students complete at least 12 units of coursework
The Master of Science in Statistics with a Data Science focus is an interdisciplinary program that allows students from various undergraduate majors to develop skills in data science. The program is designed for students who are interested in using their knowledge of statistics and applied mathematics to solve real-world problems. Students will learn how to design, implement, and analyze statistical methods for solving problems related to data science.
Stanford University also offers a Master’s of Science in Statistics with a Data Science focus. Big data is becoming increasingly important for applied sciences and engineering fields. Once students have completed their Master’s degree, they can continue onto the doctoral program in ICME, Statistics, Computer Science, MS&E, or start their career as a data science professional. The Political Science (Bachelor’s, Master’s, and Doctorate) department also offers a data science track. Students can learn how to use statistics, algorithms, and formal theories to extract information from data to predict, analyze, or explain political behaviors and phenomena. Stanford University also offers Biomedical Data Science workshops that can double as classes.
Students looking for sociology majors can pursue the Data Science/Markets/Management Track. This Bachelor of Art major allows students to study social situations and focus on computer programming, network analysis, big data, and traditional sociology courses.
The Earth Sciences department also offers a data science track, allowing students to learn about earth imaging, how data can help predict the future or learn more about the past, and more. Undergraduate and graduate degrees are available.
Business Analytics can also help students learn more about data science and include clinical, research, education, departmental, appointment, and workforce analytics.
Those who want to earn a Certificate of Completion may find the Big Data, Strategic Decisions program helpful. It helps students learn about data analytics and how it is powerful, which can help them enhance their performance at their company. Another Certificate of Completion offered at Stanford University is the Foundations for Data Science, which includes three courses. However, there could be prerequisites required before students can complete each course.
Stanford University also offers a variety of online degrees. Most programs offer Bachelor, Master, and Doctoral degree options. Data Science students may want to consider Computer Science Master’s degree, data mining/applications graduate certificate, engineering, business analytics, and other degree options with a data science track.
stanford data science master’s online
Master of Science in Statistics: Data Science at Stanford University
During their time on this full-time only course, students will develop a broad understanding of, and experience working with, cornerstones of data science including statistical modelling, programming and data mining. They then go on to specialize in more in-depth fields such as data in medicine, machine learning, business intelligence and distributed data management.
Being located in Silicon Valley, students are ideally placed to pursue work experience and internships with the many tech giants which share their sunny corner of California.
Coursework
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.
As defined in the general Graduate Student Requirements, students have to maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. Students satisfying the course requirements of the Data Science track do not satisfy the other course requirements for the M.S. in Statistics
The total number of units in the degree is 45, 36 of which must be taken for a letter grade.
Submission of approved Master’s Program Proposal, signed by the master’s adviser, to the student services officer by the end of the first quarter of the master’s degree program. A revised program proposal is required to be filed whenever there are changes to a student’s previously approved program proposal.
There is no thesis requirement.
stanford masters data science admission
MS in Education Data Science is offered by Stanford Graduate School of Education under Stanford University, USA. This a Masters level program of a course duration of 1.5 Years.
The school’s 6% acceptance rate is the lowest in the world.
Students can choose an undergraduate or graduate degree program in the humanities or social sciences (Bachelor’s Master’s, Doctorate) with a minor in Data Science. Stanford University also offers a Master’s of Science in Statistics with a Data Science focus.
stanford data science major
Students must demonstrate breadth of knowledge in the field by completing five core areas.
Requirement 1 : Foundational (12 units)
Students must demonstrate foundational knowledge in the field by completing the following core courses. Courses in this area must be taken for letter grades.
| Course Name & number | Course TItle | Units |
|---|---|---|
| CME 302 | Numerical Linear Algebra | 3 |
| CME 305 | Discrete Mathematics and Algorithms | 3 |
| CME 307 | Optimization | 3 |
| CME 308 | Stochastic Methods in Engineering | 3 |
| or | ||
| CME 309 | Randomized Algorithms and Probabilistic Analysis | 3 |
| STATS 310A | Theory of Probability | 3 |
Requirement 2 : Data Science Electives (12 units)
Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list are subject to approval. Courses in this area must be taken for letter grades.
| Course Name & number | Course TItle | Units |
|---|---|---|
| STATS 200 | Introduction to Statistical Inference | 3 |
| or STATS 300A | Theory of Statistics I | 3 |
| STATS 203 | Introduction to Regression Models and Analysis of Variance (spring quarter) | 3 |
| or STATS 305A | Introduction to Statistical Modeling | |
| STATS 315A | Modern Applied Statistics: Learning | 2-3 |
| STATS 315B | Modern Applied Statistics: Data Mining | 2-3 |
| or equivalent courses as approved by the adviser. |
Requirement 3: Advanced Scientific Programming and High Performance Computing Core (6 units)
To ensure that students have a strong foundation in programming, 3 units of advanced scientific programming for letter grade at the level of CME212 and three units of parallel computing for letter grades are required.
Note: Programming proficiency at the level of CME211 is a hard prerequisite for CME212 (students may ONLY place out of 211 with prior written approval*). CME211 can be applied towards elective requirement.
| Course Name & number | Course TItle | Units |
|---|---|---|
| Advanced Scientific Programming; take 3 units | ||
| CME 211 | Software Development for Scientists and Engineers (can only be used as an elective) | 3 |
| CME 212 | Advanced Software Development for Scientists and Engineers | 3 |
| Parallel Computing/HCP courses: (3 units) | ||
| CME 213 | Introduction to parallel computing using MPI, openMP, and CUDA | 3 |
| CME 323 | Distributed Algorithms and Optimization | 3 |
| CME 342 | Parallel Methods in Numerical Analysis | 3 |
| CS 149 | Parallel Computing | 3-4 |
| CS 316 | Advanced Multi-Core Systems | 3 |
| CS 344C, offered in previous years, may also be counted |
Students who do not have a strong computational and/or programming background will take an extra 3 units to prepare themselves by, for example, taking CME211 Programming in C/C++ for Scientists and Engineer or equivalent course* with adviser’s approval.
If you do not have the background to start this program, we recommend that you take our Introduction to Computational Methods course (CME200) before starting this program. This course is offered every semester. You can find more information about it here: [link].
If you’re not sure whether your background is strong enough, contact your departmental adviser as soon as possible so they can help you determine whether or not it is appropriate to take extra courses prior to starting this program.
Requirement 4 : Specialized Electives (9 units)
Choose three courses in specialized areas from the following list. Courses outside this list are subject to approval.
| Course Name & number | Course TItle | Units |
|---|---|---|
| BIOE 214 | Representations and Algorithms for Computational Molecular Biology | 3-4 |
| BIOMEDIN 215 | Data Driven Medicine | 3 |
| BIOS 221/STATS 366 | Modern Statistics for Modern Biology | 3 |
| CS 224W | Social and Information Network Analysis | 3-4 |
| CS 229 | Machine Learning | 3-4 |
| CS 231N | Convolutional Neural Networks for Visual Recognition | 3-4 |
| CS 246 | Mining Massive Data Sets | 3-4 |
| CS 448 | Topics in Computer Graphics | 3-4 |
| ECON 293 | Machine Learning and Causal Inference | 3 |
| ENERGY 240 | Geostatistics | 3 |
| OIT 367 | Business Intelligence from Big Data | 3 |
| PSYCH 204A | Human Neuroimaging Methods | 3 |
| STATS 290 | Computing for Data Science | 3 |
Requirement 5 : Practical Component
Students are required to take 6 units of practical component that may include any combination of:
- A capstone project, supervised by a faculty member and approved by the student’s adviser. The capstone project should be computational in nature. Students should submit a one-page proposal, supported by the faculty member and sent to the student’s Data Science adviser for approval (at least one quarter prior to start of project).
- Master’s Research: STATS 299 Independent Study.
- Project labs offered by Stanford Data Lab: ENGR 250 Data Challenge Lab, and ENGR 350 Data Impact Lab.
- Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting Workshop up to 1 unit.
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