phd in data science stanford

ICME Doctor of Philosophy

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The Institute for Computational and Mathematical Engineering (ICME), and its predecessor program Scientific Computing and Computational Mathematics, has offered MS and PhD degrees in computational mathematics for over 30 years. Affiliated Faculty conduct groundbreaking research, train and advise graduate students, and provide over 60 courses in computational mathematics and scientific computing at both the undergraduate and graduate level, to the Stanford community.

Doctoral Program

We develop innovative computational and mathematical approaches for complex engineering and scientific problems, attracting talented PhD students from across the globe. Advised in research by more than 50 faculty from 20-plus departments, PhD students are immersed in a wide variety of fields including statistics and data science, machine and deep learning, control, optimization, numerical analysis, applied mathematics, high-performance computing, earth sciences, flow physics, graphics, bioengineering, genomics, economics and financial mathematics, molecular dynamics, and many more. PhD graduates find outstanding positions in industry and national laboratories as well as in academia.

ICME PhD students cultivate a broad and deep understanding of computational mathematics through core courses in matrix computations, optimization, stochastics, discrete mathematics, and PDEs and through their research work with ICME affiliated faculty.

For complete details, coursework, and research requirements please view the Stanford Bulletin:  Doctor of Philosophy in Computational and Mathematical Engineering

ICME PhD Application Submission

Data Science Fellows

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Applications now closed. We will reopen in Fall 2024

Stanford Data Science seeks recent Ph.D.s  of exceptional promise for postdoctoral fellow positions in interdisciplinary research with expertise in both Data Science AND its application in a domain of scholarship, like physical, earth, life, or social sciences, humanities and the arts, business, law, medicine, education, or engineering.

View current Data Science Postdoc Fellows »

The Opportunity

Data Science Fellows work both within and at the boundaries between data science methods and the domains of scholarship that utilize data science to discover and create new knowledge. They will lead independent, original research programs with impact in one or more research domains and in one or more methodological domains (computer science, statistics, applied mathematics, etc). 

Ideal candidates will have earned a PhD in either a methods or applied discipline with demonstrated skills and experience in one of the other complementary areas (as examples: a PhD in statistics with applications to physics, or a PhD in biology with extensive use of machine learning).  Successful candidates will bring a research agenda that can take advantage of the unique intellectual opportunities afforded by Stanford University, and will have experience in working with researchers across different fields. Their research results will be published in technical reports, open-source software, peer-reviewed journals as well as presented at scientific conferences. Ideal candidates will have experience and interests in building community, teaching and training, and leadership with strong communication skills.

Applicants should expect traveling as a requirement to coordinate research with internal and external collaborators and sponsors. 

Appointments are initially for one year, with an expectation of renewal for a second year on satisfactory performance. Fellowships have a competitive salary and benefits, with funds to support research and travel.  There is flexibility about the start date, September 1st is expected.

Qualifications

• Recent PhD (graduation within the last five years) with experience in a complementary field(s).
• Excellent experience in their PhD discipline (or an area applying data science for new discoveries)
• Excellent knowledge of advanced software engineering, computer science and/or statistics
• Demonstrated commitment to reproducibility and open research through existing public release of research data and software code
• Excellent verbal and written communication and presentation skills necessary to author technical and scientific reports, publications, invited papers, and to deliver scientific presentations, seminars, meetings and/or teaching lectures.
• Experience collaborating effectively with a team of scientists of diverse backgrounds.

Desired Qualifications

• Experience in developing curriculum and teaching.
• Experience developing open-source research software used by a community beyond their lab.
• Experience building inclusive communities of practice around data science that are diverse and equitable for all.

Desired Start

September 1st of the following academic year.

Required Application Materials

• Applicants submit their (1) curriculum vitae, (2) a publication/software list, and (3) a two-page letter of intent detailing a proposed research plan. The proposed research plan should include information about both advancing data science and its application in a domain of scholarship. Please also include the names of potential faculty collaborators (ideally bridging a methods domain and an application domain, e.g. Stats+Bio, CS+politics, etc).
• Applicants are encouraged to discuss their proposed research plan with potential faculty collaborator(s)/mentor(s) in preparing their application. Applicants who don’t coordinate with Stanford faculty beforehand should indicate the reason. Stanford collaborators / mentors can be any full-time faculty on campus, prior affiliation with Stanford Data Science is not necessary.
• Applicants need to have two letters of reference submitted through our online form by the deadline: https://forms.gle/1UPvVZgwaBq2ngoo9   (closes 16 Jan 2024 @ 9am pacific)  

Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce. It welcomes applications from women, members of minority groups, veterans, persons with disabilities, and others who would bring additional dimensions to the university's research and teaching mission.

data science

John Chambers

BMDS-PHD - Biomedical Data Science (PhD)

Program overview.

The Biomedical Data Science Program is interdisciplinary and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. The program emphasizes research to develop novel computational methods that can advance biomedicine. Students receive training in investigating new approaches to conceptual modeling and developing new algorithms that address challenging problems in the biological sciences and clinical medicine.

Admissions Information

The program can provide flexibility and complement Stanford’s other applied medical research opportunities. Special arrangements may be made for those with unusual needs or those simultaneously enrolled in other degree programs within the university. Similarly, students with prior relevant training may have the curriculum adjusted to eliminate requirements met as part of prior training.

The GRE is not required for admission.

Individuals wishing to prepare themselves for careers as independent researchers in biomedical data science, with applications experience in bioinformatics, clinical informatics, or imaging informatics, should apply for admission to the doctoral program. Graduate Degrees summarizes the university’s basic requirements (residence, dissertation, examination, and so on) for a doctorate.

Data Science

Apply computational and quantitative thinking to innovative lines of inquiry

Undergraduate B.S. Program

The B.S. in Data Science is the successor to the major in  Mathematical and Computational Science (MCS) . The goals of our program remain ambitious: we aim to provide a broad and deep understanding of the foundations of the discipline, training nimble and versatile data scientists. Increasing data size and availability, enhanced computational power, and progress in algorithms and software make this an ever exciting area. 

Students pursuing the B.S. in Data Science will acquire a core mathematical knowledge, upon which they will build competences in computation, optimal decision making, probabilistic modeling, and statistical inference. By learning the theory behind data science, the students develop the capacity to stay up-to-date in a field that is evolving rapidly. They learn to design new methodologies and quickly get up to speed with new developments.

In addition,  electives and pathways in Data Science provide students the opportunity to develop their interests.  Students can explore how inferential and computational thinking can be effective in areas as diverse as finance, biology, marketing, and engineering; or they can choose to acquire greater depth in one of our core disciplines. 

The Data Science program is interdisciplinary in its focus, and sponsored by Stanford’s departments of Statistics, Mathematics, Computer Science, and Management Science & Engineering.  Students are required to take courses in each of these departments. 

Learn more about the B.S. Degree Requirements

I think [Data Science] is one of the best majors offered at Stanford; it is a "liberal arts" major for the computationally-minded. It has served me very well and it was fun to work towards the major because the classes were easy to balance since they were so different and exciting. I really think it is a gem that showcases Stanford's best departments all under one degree. It prepared me well for my doctoral work and my postdoc.

Faculty & Partnerships

In 1971, four professors -- Rupert Miller from Statistics, Arthur Veinott, Jr. from Operations Research, John Herriot from Computer Science, and Paul Berg from Mathematics -- created an interdisciplinary group to meet the need of having an undergraduate program for students interested in applied math.  The Data Science major is the legacy of these collaborations.

Today, the program utilizes the faculty and courses of the departments of Computer Science, Mathematics, Management Science and Engineering, and Statistics. The variety of topics covered in the courses that make up the degree program require expertise in a wide selection of subject disciplines; by utilizing the resources of several departments in teaching the courses, we hope to give the students the best possible introduction to mathematical and information sciences.

Where can Data Science take you?

Every year, our students continue to pursue their passions in a variety of positions in industry and academia.  The B.S. in Data Science is an ideal major to prepare students for graduate study in quantitative fields such as computer science and statistics, and for careers in a range of industries that require quantitative work, such as information technology and finance.  

Ready to join our community?

Recommended First Courses

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Declare your major in Data Science

• Office of Graduate Education

Applicant FAQ

Frequently Asked Questions – Eligibility, Funding, Knight-Hennessy Scholars, etc.

In addition to the most frequently asked Biosciences questions below, please also visit the Graduate Admissions FAQ web page for a more expansive list.

Are there any prerequisites or background coursework required for the 14 Biosciences PhD Programs?

A majority of the Biosciences PhD Programs do not have specific course requirements for admission.  The faculty like to see that you have taken a rigorous course load, but they will be particularly interested in your research experience.  While many of our graduate students have undergraduate preparation in a life sciences curriculum, it is feasible to enter from other programs, including chemistry, computer science, mathematics, psychology, or physics.  The  Biomedical Data Science ,  Biophysics ,  Molecular and Cellular Physiology , and  Structural Biology  programs have prerequisite or background course requirements. We strongly recommend that you reach out to the  programs  to which you plan to apply, to ask for specific course requirements/suggestions.

Can I apply to other graduate programs (e.g. Applied Physics, Bioengineering, Chemistry, Computer Science, etc.) in addition to the 14 Biosciences PhD Programs?

You may apply to only one graduate program per academic year. The only exception is within the 14 Biosciences PhD Programs, where you may apply for two Biosciences programs within a single application. The 14 Biosciences PhD Programs include:

• Biochemistry
• Biomedical Data Science
• Cancer Biology
• Chemical and Systems Biology
• Developmental Biology
• Microbiology and Immunology
• Molecular and Cellular Physiology
• Neurosciences
• Stem Cell Biology and Regenerative Medicine
• Structural Biology

Can I defer my enrollment?

Admitted students are expected to enroll in their Home Program in September of the year they are admitted. Deferral requests will be reviewed by your admitting program’s admissions committee and are approved on a case-by-case basis. The maximum length of an admissions deferral granted by Stanford is one year. Typically, deferral requests are only approved for military, medical, visa, or education-related purposes.

Can recommenders submit their letter via mail, email, fax, or a letter service?

All recommendations must be submitted using the online application system as recommenders are required to respond to specific evaluation questions on the recommendation form. Letters of recommendation cannot be mailed, emailed, faxed, or submitted through a letter service (with the exception of Interfolio). For letters submitted via Interfolio, please remember that letters written specifically for your Stanford graduate program tend to be stronger than letters written for general use purposes.

Do any of the 14 Biosciences PhD Programs offer an MS degree program?

The Biomedical Data Science program is the only Biosciences Program that currently offers an MS degree program.  Information about the program and its application process can be found on its website .

If you are not interested in one of the 14 Biosciences PhD Programs, you can find a list of all the currently offered degrees at Stanford (along with their contact information) on the Graduate Admissions  Explore Programs web page .

Do I need to hold an MS degree to be eligible to apply?

A Master’s degree is only required if you do not meet the following eligibility requirements.  To be eligible for admission to graduate programs at Stanford, applicants must meet  one  of the following conditions:

• Applicants must hold, or expect to hold before enrollment at Stanford, a bachelor’s degree from a U.S. college or university accredited by a regional accrediting association.
• Applicants from institutions outside the U.S. must hold, or expect to hold before enrollment at Stanford, the equivalent of a U.S. bachelor’s degree from a college or university of recognized standing. See the Office of Graduate Admissions for the  minimum level of study required of international applicants .

Do I need to include a department code number when requesting to have my GRE and/or TOEFL scores sent to Stanford?

Applicants should have the Educational Testing Service (ETS) send scores electronically to Stanford. Our university code is  4704  and no department code is required. You will either self-report your scores or indicate the date you will take the test(s) in the online application. Self-reported test scores will be used by the relevant admissions committee in their initial review process. Your unofficial test scores will be validated when your official scores are received by the University.

Do I need to secure a Lab/Thesis Supervisor prior to applying?

You will not need to secure a research supervisor prior to applying. Incoming students usually do 2-4 lab rotations during their first year.  Information on the rotation process can be found on the following  website .  If you realize a few weeks into a rotation that the lab is not a good fit for you, then there is no reason for you to stay any longer.

Do I need to submit official transcripts/academic records?

Graduate Admissions only requires admitted applicants who accept the offer of admission to submit official transcripts that shows their degree conferral. More details on this can be found on the following Graduate Admissions  webpage .   Please do not send or have sent any transcripts to us or to your program. 

Do you offer fellowships to international applicants?

We have a limited number of fellowships (which include a yearly stipend, tuition, and health and dental insurance) available to the most highly competitive international applicants. The stipend for the 2023-24 Academic Year is $51,600 ($12,900 per quarter). Admittance to the Biosciences Programs for international applicants varies from year to year depending on funding and available space. We strongly encourage applicants to apply for scholarships/fellowships in their home country that can be used overseas. Some useful websites that include information on external fellowships are:

• Fulbright Foreign Student Program
• The Fogarty International Center at the NIH
• International Center at the Institute of International Education (IIE)

Applying for scholarships/fellowships generally takes some time to arrange, so plan ahead. You will be able to list any scholarships/fellowships that you have applied for and been awarded in the “Additional Information” section of the online application under “External Funding for Graduate Study”.  For more information about the costs and estimated expenses of attending Stanford, please visit the following  webpage .

Does the Bioengineering PhD program participate in the Biosciences Interview Session?

The Bioengineering PhD program is not one of the 14 Biosciences PhD Programs and has a separate admissions process and Interview Session.

How do I change one of my recommenders?

On the Recommendations page of the application, click on the recommender’s name you wish to replace, then click Exclude at the bottom of the resulting popup window. You then will see the option to add a new recommender. The recommender you exclude will not receive an email notification.

How does the funding work for those admitted to the Knight-Hennessy Scholars Program and the Biosciences?

The Knight-Hennessy Scholars program funding covers the first three years and your admitting Home Program will cover the remaining years.

I previously applied to the Stanford Biosciences Programs and was not admitted. What application materials will I need to submit?

Applicants who wish to reapply follow the same application process as first-time applicants. Reapplicants have the option of using letters of recommendation from their prior submitted Biosciences application or having new ones submitted.  Prior applications from the Autumn 2022, 2023, and 2024 admission cycles have been retained. It is highly recommended that one new letter of recommendation be submitted on your behalf.  When completing the application, you will be required to enter the information for a minimum of three recommenders (including the information for the letter writers that you plan to reuse).

For the letters you plan to reuse, please notify your recommenders in advance that they will receive a recommendation request but should not take any action.  Once you submit your application, please submit an email to the Biosciences Admissions Office indicating which letters you would like to reuse so we can add them to your application.

I’m an applicant whose first language is not English. Is it possible to have the TOEFL Test requirement waived?

Information about the TOEFL Test requirements, exemptions and waivers can be found on the  Graduate Admissions  website. Please note that if you submit a waiver request, it will be routed to Graduate Admissions  after you submit your application . Allow up to 15 business days after submitting your application for a response.

I’ve applied to multiple Home Programs and was wondering what happens if more than one program is interested in interviewing me?

In that case, the admissions representatives confer and attempt to determine which Home Program best fits your interests and should serve as your host. They will use the information you provided in your Statement of Purpose and on the Biosciences Supplemental Form. In most cases the best match is clear, but in rare cases where this is not the case, an admissions committee member will contact you directly to discuss with you which Home Program would be the best to host your visit. You will also have an opportunity to meet with faculty affiliated with other Home Programs during your visit.

If my school does not use a 4.0 GPA grading scale, how should I report this on my application?

You are asked to enter both GPA and GPA scale for each institution you list on the application. Enter your GPA as it appears on your transcript. Do not convert your GPA to a 4.0 scale if it’s reported on a different scale.

Is there a minimum GPA requirement?

There is no minimum GPA requirement to be considered for admission. The application review process is holistic and all aspects of the application (prior coursework, letters of recommendation, the statement of purpose, prior research experience, and test scores {if applicable}) are considered by the Admissions Committee when making an admissions decision.

What if my recommenders are not receiving their recommender link emails?

Occasionally, some email servers will send recommender link emails directly to Spam or will not allow the email to reach the primary inbox at all (particularly for email addresses located outside of the United States). Please reach out to Technical Support by submitting a request via the “Request Application Support” button on the “Instructions” page of your application.

What is included in the offer of admission?

The offer of admission for the 2023-24 Academic Year included a stipend of $51,600 ($12,900 per quarter), health and dental insurance, and graduate tuition. The stipend and benefits for the 2025-26 Academic Year will be set sometime in March 2025.  For more information about the costs and estimated expenses of attending Stanford, please visit the following webpage .

What is the Knight-Hennessy Scholars program?

The  Knight-Hennessy Scholars  program develops a community of future global leaders to address complex challenges through collaboration and innovation. The program will award up to 100 high-achieving students with three years of funding to pursue a graduate education at Stanford. To be considered, you  must apply to both  the Knight-Hennessy Scholars by Wednesday, October 9, 2024, at 1:00 pm (PST) and to one of the Stanford Biosciences PhD programs by Sunday , December 1, 2024, at 11:59:59 pm (PST) .  Information about the program and the application process can be found on the  Knight-Hennessy Scholars  program website.

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The 7 Best Data Science Courses That are Worth Taking in 2024

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A career in data science involves using statistical, computational and analytical methods to extract insights from data. Data scientists regularly use programming languages like Python and R alongside machine-learning algorithms and data-visualisation software.

The need for data scientists has surged across various sectors, including finance, healthcare and technology, making it a highly sought after and lucrative profession. According to the U.S. Bureau of Labor Statistics , the average annual salary for data scientists in 2023 was $108,020, while demand for them is expected to increase by 35% in the next eight years — much faster than average for all occupations.

SEE: What is Data Science? Benefits, Techniques and Use Cases

Online courses and certifications provide accessible pathways into the field, as many can fit around existing responsibilities like a day job. Such programs provide the expertise required for an individual to land their first data science role or just discover whether the career is for them. TechRepublic takes a look at the top six data science courses available in 2024 for learners with different goals and levels of experience.

• Best for a data science overview: IBM Data Science Professional Certificate - Coursera
• Best for beginner Python skills: Associate Data Scientist in Python - DataCamp
• Best for beginner R skills: R Programming A-Z - R For Data Science With Real Exercises! - Udemy
• Best for beginner applications: Applied Data Science Specialization - Coursera
• Best for mathematics for data science: Mathematics for Machine Learning and Data Science Specialization - Coursera
• Best for intermediate applications: Applied Data Science with Python Specialization - Coursera
• Best for college graduates: MITX - Statistics and Data Science with Python - edX

SEE: How to Become a Data Scientist: A Cheat Sheet

Best data science courses: Comparison table

IBM Data Science Professional Certificate - Coursera: Best for a data science overview

The Data Science Professional Certificate from IBM, hosted on Coursera, offers a great starting point for those interested in learning about data science but don’t fully understand what a career in it would entail. This course provides an overview of the tools, languages and libraries used daily by professional data scientists and puts them into practice through a number of exercises and projects. The final Capstone project also requires the student to create a GitHub account, encouraging them to familiarise themselves with the site and collaborate.

$49/£38 per month after a seven-day free trial.

Six months at ten hours a week.

• Industry recognition, as backed by IBM.
• Self-paced.
• Lacks depth, as aims to provide just foundational knowledge of theoretical data science and practical applications.

Pre-requisites

Associate data scientist in python - datacamp: best for beginner python skills.

DataCamp is another well-regarded provider of data-related courses, and one of its highest rated is titled ‘Associate Data Scientist in Python’. It sets itself apart with its unique hands-on coding exercises, one of which involves manipulating and visualising data on Netflix movies. Language-wise, this course exclusively uses Python, but introduces learners to multiple libraries including pandas, Seaborn, Matplotlib and scikit-learn. Knowledge of Python is not required for this course, as the necessary skills are taught along the way.

$13/£11 a month for full access.

Nine weeks at ten hours a week.

• More emphasis on programming skills and data manipulation techniques.
• Taught in Python, the most popular programming language .
• Less depth in theoretical elements of data science.
• Python-specific knowledge may not translate to different environments.

R Programming A-Z - R For Data Science With Real Exercises! - Udemy: Best for beginner R skills

While many data science courses are taught with Python due to its popularity and simplicity, ‘R Programming A-Z’ on Udemy is aimed at learners looking to get to grips with R and RStudio. R is a powerful language used frequently in data science for handling complex data sets. This course assumes no prior knowledge and starts with the very basics of R programming, including variables and for() loops, before looking at matrices, vectors and more advanced data manipulation. Large projects that help cement learning use real-world financial and sports data.

$109.99/£69.99.

10.5 hours of lectures + exercises.

• Specific to R and RStudio.
• Removes the steep learning curve often associated with R.
• Relatively small focus on data science and machine learning.
• Taught on a Mac and instructions for Windows devices are not always clear.

Applied Data Science Specialization - Coursera: Best for beginner applications

“Applied Data Science Specialization,” another course by IBM, fast tracks data science beginners towards skills with real-life applications. Python skills for data analysis and visualisation are taught assuming no prior knowledge of the language and are then put into practice in the interactive labs and projects. These cover the extraction and graphing of financial data, creation of regression models to predict housing prices and visualisation of data treemaps and line plots on Python dashboards. By the end of the course, participants should have solidified their practical Python skills to the extent that they can confidently explore more advanced topics like big data, AI and deep learning.

$49/£38 per month after a seven day free trial.

Two months at ten hours a week.

• Appropriate for beginners.
• Fast tracks learners to practical applications in data science.
• Lack of foundational knowledge provided.

Mathematics for Machine Learning and Data Science Specialization - Coursera: Best for mathematics for data science

As the title suggests, this course from DeepLearning.ai has a particular focus on mathematics for data scientists. Mathematics underpins the profession and is essential for understanding algorithms, cleaning data, drawing insights, visualisation, evaluating models and more. The course covers the fundamental mathematical toolkit of machine learning, including calculus, linear algebra, statistics and probability. Learners say it provides a good entry point into the theory of data science and the lab exercises are practical.

Six weeks at ten hours a week.

• Mathematics covered relevant to applied data science.
• Does not get into lots of depth on each topic.

A high school level of mathematics and a basic knowledge of Python is recommended.

Applied Data Science with Python Specialization - Coursera: Best for intermediate applications

Similar to the IBM “Applied Data Science Specialization” on Coursera, this course does not teach the fundamentals of programming. Instead, it launches straight into applying techniques related to machine learning, data visualisation and text analysis. What differentiates the course is that it is designed for those that already have a basic understanding of Python but want a more in-depth introduction to real-world applications within data science. Key libraries such as Pandas, Matplotlib and Seaborn are used for applied charting, machine learning and text mining. It is led by professors from the University of Michigan via five modules of video lectures, notes and exercises.

Four months at ten hours a week.

• Concentrates on data science applications of Python.
• Requires knowledge of Python.

Background in basic Python or programming required.

MITX - Statistics and Data Science with Python - edX: Best for college graduates

The “Statistics and Data Science with Python” course presented by the Massachusetts Institute of Technology is by far the most comprehensive course featured on this list. The so-called “MicroMasters” takes learners over a year to complete and prepares them for their first career in data science. It provides a graduate-level introduction to concepts such as statistical inference and linear models, as well as practical experience building machine learning algorithms. It is designed to fit around a day job or university study while not compromising on the level of content.

$1,350/£1,186.

One year and two months at ten hours a week.

• Comprehensive.
• Prepares learners for data science jobs.
• Large time commitment required.
• Requires high-level mathematical knowledge,

University-level calculus and comfort with mathematical reasoning and Python programming are recommended.

What is the difference between data analysis and data science?

The key difference between data analysis and data science is that the former primarily looks to interpret existing data, while the latter involves creating new ways of doing so.

Data analysis focuses on examining datasets to identify trends, draw conclusions and support business decisions. It involves cleaning, transforming and modelling data to extract useful information, often using tools like Excel and SQL. It is performed by data analysts who are typically hired into a wide range of industries, including marketing firms, government agencies, healthcare providers, financial institutions and more.

Data science, on the other hand, integrates data analysis with advanced machine learning algorithms, predictive modelling and big data technologies. Data scientists often develop new tools and methods to handle complex problems and derive insights from large-scale datasets. Skills required for this include proficiency in programming languages such as Python and R, as well as a deeper understanding of statistical methods and machine learning.

SEE: 10 Signs You May Not Be Cut Out for a Data Scientist Job

Is data science still in demand in 2024?

Data science remains in high demand in 2024. The IDC estimates that the amount of data worldwide will reach 291 zettabytes by 2027, and as growth continues, more data professionals will be needed to manipulate and interpret it. Furthermore, many of the key industries within which data scientists work are expanding, such as AI, machine learning and the Internet of Things, while others provide core services such as healthcare, energy, finance and logistics. Salaries also reflect this high demand as, according to Glassdoor , the average base pay of a data scientist in the U.S. is $113,000.

Are data science courses worth it?

Opinions on online data science courses vary within the industry. For some, there are enough free resources available through platforms like YouTube to render paid courses unnecessary. They may also argue that there is no substitute for hands-on experience, and that even beginners should learn the necessary skills by downloading an open-source dataset and attempting to manipulate it themselves.

However, the key to learning anything new is persistence, and it can be difficult to remain motivated without a defined learning programme to follow, coursemates to connect with or a course fee at risk of going to waste. For individuals with a tendency to start projects but not finish them, an initial investment in a structured course may provide the motivation they need. Many paid courses also give direct access to qualified instructors who can provide tailored help that would otherwise not be available.

Ultimately, there are certainly opportunities to break into data science without taking any type of online course. However, if structured learning provides the skills or motivation you desire, then the investment may be worth it.

Methodology

When assessing online courses, TechRepublic examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary considerably, so be sure to choose the option that is right for your goals and learning style.

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MCiM Alumni Spotlight Meet David!

Congratulations to the MCiM Class of 2024!

The Innovative Minds Leading Clinical Informatics Meet our MCiM 2024-25 cohort

A Degree Program Blending Stanford's Expertise in Medicine, Business and Technology Learn More About Why MCiM

The master of science in clinical informatics management.

Developing Leaders to Transform Health Care

The Master of Science in Clinical Informatics Management (MCiM) is a unique degree program combining medicine, business and technology. MCiM prepares the next generation of leaders who can efficiently oversee and implement novel uses of technology within health care; gain core business skills, new health sector insights, and a management-focused and ethical understanding of digital innovations applied to the health care needs of diverse populations. The only cross-disciplinary program of its kind on the West Coast, MCiM cultivates a supportive learning environment to develop leaders who seek to advance diversity, equity and inclusiveness in health care– locally, regionally and globally.

A Program for Leaders

Business and technology skills applied to digital innovations are critical to improving quality, equity and efficiency in health care — an imperative that COVID-19 has only underscored.

What makes MCiM a program that can uniquely serve and address your learning needs to transition into a role where you can harness the power of digital innovations to deliver high-quality, cost-effective health care? 

Admissions Process

Are you interested in joining the 2025-2026 MCiM Cohort? The online application for our program will open in mid-September 2024. 

MCiM follows a robust, holistic admissions process that welcomes applicants with a diverse range of experiences and backgrounds. Applicants will be asked to demonstrate their academic readiness for a rigorous intellectual experience and their topical interest in this exceptional program.

Program Format

MCiM is a 12-month degree program with a set curriculum that blends the quality of a Stanford professional education with a flexible class schedule and learning format. Students move through the set courses as a cohort for the entire year.

Classes are held every other week, Friday and Saturday, and supplemented through online assignments and learnings. The program format is ideal for working professionals, with the majority of students completing the program alongside their full-time work. 

Student Profile

MCiM is designed for individuals from a variety of backgrounds who share the drive to advance their careers and the passion to innovate the $4 trillion US healthcare system. 

At the intersection of medicine, business, ethics, and technology, MCiM offers a unique opportunity to cultivate team and relationship-building in a diverse learning environment. We thereby aim to develop a cadre of professionals in health care with a commitment to inclusivity and to promoting greater diversity in leadership roles.

Commitment To Diversity

Health equity and health justice.

Achieving Stanford Medicine's vision of health equity and health justice requires tackling some of the most difficult challenges in health care in the US and globally. The establishment of MCiM brings a new set of tools and perspectives to this challenge-how can we use technology to achieve health equity. The MCiM curriculum approaches this question from several different perspectives: how do we develop technology to reach the most underserved populations; how do we use technology to make health care more accessible and affordable; and how do we use technology to improve the quality of care.

These are not just technology challenges-embedded in each of these questions is a business model question that has remain unaddressed in most efforts to address health care disparities. MCiM is unique in tackling health equity from both a business model and technology perspective. Further, our integration of biomedical ethics throughout the program brings a new set of perspectives and insights for our students dealing with difficult perspectives on these questions.

Discussions in the classroom bring these challenging topics to life:

• Can a voice interface be used to overcome literacy and language challenges in achieving population health?
• Patient navigators have shown the benefit of reducing complexity of care, but these programs are costly. Can we use service operations to create a better experience to reduce complexity of care as a means of reducing health disparities? Can we create digital patient navigators to ensure care coordination for patients receiving complex sets of services?
• Can personal health records improve the quality of care and adherence to guideline-based care in the US and abroad?

Stanford Medicine REACH Initiative

The Social Justice and Health Equity (SJ&HE) Curriculum Thread was initiated in the Summer of 2020. Following the recruitment of its faculty lead, Dr. Italo Brown, its committee has been tasked with a comprehensive review of the Stanford Medical School Curriculum to identify strengths and gaps in addressing anti-racist education, health equity and other social justice issues for both our clerkship and pre-clerkship curricula. Following the review, Dr. Brown spearheaded the implementation of important reforms to the existing medical school curriculum by working with course directors, faculty, students and staff to revise the existing educational materials. Our expectation is that anti-racist education will be a permanent thread included within the Stanford Medical School Curriculum.

The Racial Equity to Advance a Community of Health (REACH) post-baccalaureate research program provides highly motivated scholars the opportunity to engage in a 1-2 year research opportunity with structured professional development and academic training components. Individuals may consider completing MCiM during their REACH research activities to provide additional curriculum pathways to foster individualized career development.

Please visit https://med.stanford.edu/reach/programs/md-ms.html to find out more about the REACH Initiative at Stanford Medicine