Data Science Curriculum

Top Universities for MS in Data Science in the USA

Top universities for MS in Data Science in USA

Top Universities for MS in Data Science in the USA

Looking into the sea of top Data Science universities the internet has tossed your way, and feeling lost on which one to pick? 

Does that sound like you?

Well, it’s all about asking the right questions and seeking guidance from those who have been in your shoes.

Top Universities for MS in Data Science in the USA

In this article, we’ll take you by the hand and guide you to choose the ideal university and program for you. We’ll cover the topics including

  • Factors to consider when choosing MS in Data Science USA
  • Top universities for MS in Data Science in the USA
  • Course highlights of top Data Science universities

If you are an international student planning to do your studies in the US, then hold on till the last for some bonus tips.

So, grab a cup of your favorite beverage, and let’s dive in together, like detectives on a quest to uncover the secrets of the data science world.

Factors to consider when choosing MS in Data Science USA

  • Ranking and accreditation: Consider universities with accreditation from recognized organizations.
  • Course: Choose a strong curriculum, covering essential data science topics.
  • Faculty: Check faculty members’ qualifications, industry experience, and research accomplishments.
  • Cost: Consider tuition fees and living expenses when selecting a university.
  • Financial aid: Explore financial aid options such as scholarships, fellowships, and assistantships.
  • Research and industry collaborations: Seek programs with research, internship, or co-op experiences.
  • Campus facilities: Look for facilities like cutting-edge labs, libraries, and computing resources. Assess the availability of campus resources, like career services and academic support.
  • Location: Consider the university’s location and proximity to family or friends.

Top universities for MS in Data Science in the USA

Name of the university

National ranking

Name of the Data Science program


Acceptance rate

Standard tuition fee for the Academic Year

Massachusetts Institute of Technology


MicroMasters in Statistics and Data Science(SDS)

Cambridge, Massachusetts



Stanford University


M.S. in Statistics and Data Science

Stanford, California



Harvard University


Master’s in Data Science

Cambridge, Massachusetts



University of California, Berkeley


Online Master of Information and Data Science (MIDS) 

Berkeley, California



Carnegie Mellon University


Master of Computational Data Science (MCDS)

Pittsburgh, Pennsylvania



DePaul University


Master of Science

Data Science




Worcester Polytechnic Institute


Master’s in Data Science

Worcester, Massachusetts



New Jersey Institute of Technology


M.S. in Data Science – Computational Track

Newark, New Jersey



University of San Francisco


Master of Science in Data Science (MSDS)

San Francisco, California



City University of New York


Master of Science in Data Science 

New York



Course highlights of top Data Science universities

1. Massachusetts Institute of Technology

Massachusetts Institute of Technology


  • Comprehensive curriculum: Multidisciplinary approach with four online courses and a virtually proctored exam, covering data science foundations, data analysis techniques, and machine learning algorithms.
  • Flexibility: This program is designed for working professionals. Thus the best Data Science programs in the USA allow students to continue their studies without leaving their day job.

2. Stanford University

Stanford University


  • Flexibility: Students can finish the program in 5 or 6 quarters (excluding summer). This helps to balance their studies with living expenses and other commitments.
  • Research opportunities: Can do independent research(optional) under faculty supervision.
  • Location: This course, one of the best Data Science programs in the USA is located in Silicon Valley. It gives networking opportunities through career fairs hosted by Stanford Career Education.

3. Harvard University

Harvard University


  • Interdisciplinary approach: It allows students to learn from experts in fields like computer science, statistics, and applied mathematics.
  • Customizable curriculum: Apart from the core courses, students can choose from a range of electives. The electives include machine learning, data visualization, natural language processing, and network analysis.
  • Capstone project: The project gives practical experience for students. It helps them showcase their skills to potential employers.

4. University of California(UC)

University of California(UC)


  • Flexible program paths: The MIDS program offered by UC, one of the top Data Science Universities has three completion paths – accelerated, standard, and decelerated. It allows students to complete the program in 12, 20, or 32 months based on their needs.
  • Comprehensive curriculum: Multidisciplinary focus including computer science, social sciences, statistics, management, and law.
  • Capstone project: This project combines technical, interpretive, analytical, and social dimensions, preparing students for long-term professional success in the field.
  • Online learning experience: The online learning platform comes with access to weekly Zoom classes, GitHub integration for coding work, a dashboard for upcoming coursework, and a mobile app for learning on the go. 

5. Carnegie Mellon University

Carnegie Mellon University


  • Core Curriculum: This program has a strong foundation with five core courses. They are Computational Data Science, Cloud Computing, Machine Learning, Interactive Data Science, and a Data Science Seminar. 
  • Concentration areas: Choose at least one concentration from Systems, Analytics, or Human-Centered Data Science. So, depending on your interests, you can specialize in an area that aligns with your career goals.
  • Capstone Project: You’ll work on a research problem with either a CMU or an industry partner.
  • Internship: The industry internship is an amazing opportunity to get experience and make valuable connections.


  • Comprehensive technical knowledge: You will learn to manage and analyze complex data and develop data science models for decision-making.
  • Flexible learning formats: On-campus and online classroom learning options are available.
  • Effective communication skills: You’ll learn to present complex data insights in a clear manner. It will make you a valuable asset to any organization.

7. Worcester Polytechnic Institute


  • Interdisciplinary approach: The program combines Computer Science, Mathematical Sciences, and Business School disciplines.
  • Core coursework: The program will cover essential areas like Integrative Data Science, Data Analytics and Mining, Data Access and Management, Mathematical Analytics, and Business Intelligence and Case Studies.
  • Project or thesis: You can choose to either do this program through a semester-long team project with an industry partner or complete a nine-credit M.S. thesis. 

8. New Jersey Institute of Technology


  • Comprehensive curriculum: The program covers a wide range of topics, including machine learning, data mining, big data analytics, and data visualization.
  • Collaborative projects and research: You’ll have the opportunity to work on collaborative research projects with faculty and other students in one of these top Data Science Universities.


  • One-year, intensive program: The MSDS program at USF, one of the top data science universities is a full-time, one-year program. It combines a rigorous curriculum with practical experience. 
  • Boot camp for foundational skills: Before diving into the main curriculum, you’ll participate in a data science boot camp. This intensive review covers probability & statistics, computation for analytics, and linear algebra. This will make sure you have a solid foundation to succeed in the program.
  • Guaranteed internship placement: USF’s MSDS program guarantees an internship placement for all students. This nine-month internship allows you to work on real-world data science problems with organizations in the San Francisco Bay Area and beyond.

10. City University of New York


  • Solid foundation: The program starts with four core courses. It covers essential topics like machine learning, data visualization, big data analytics, and data mining.
  • Flexible electives: You get to choose two elective courses each from Data Analytics and Data Applications. It allows you to tailor your learning to your interests. 
  • Practical experience: You can either work on a research project or take up a data science internship with the program’s prime location in New York City.
  • Program duration: The program is designed to be completed within just two years.

Out of all the top Data Science universities out there, choosing the right one is a crucial decision that will shape your future career. 

Tips for international students

As a bonus, here are some tips for international students to keep in mind when applying to these universities:

  • Research each university thoroughly, considering program offerings and local culture.
  • Prepare for standardized tests like GRE, GMAT, TOEFL, or IELTS.
  • Craft a strong application highlighting academic achievements, work experience, and extracurricular activities.
  • Apply for financial aid and scholarships.
  • Network with alumni to gain firsthand insights into programs, job prospects, and life as an international student in the US.

Good luck on your journey to pursuing an MS in Data Science in the US!

College Finder

Get personalized assistance to shortlist colleges, programs etc based on your profile.

Day in the life of an MS in Data Science student

Discover a Day in the Life of an MS Data Science Student: What to Expect

Discover a Day in the Life of an MS Data Science Student: What to Expect

A mix of excitement and anticipation, with a touch of uncertainty – that’s how you might be feeling now, right?


Are you wondering how you’ll cope with the new academic system, engage in class discussions, or strike the perfect balance between academics, social life, and self-care?

Day in the life of an MS in Data Science student

In this article, we aim to provide an immersive experience of a typical day in the life of a student pursuing an MS in Data Science in the US. 

We begin with the morning routine and take you through the day until it wraps up in the evening.

As you read ahead you will come across:

  • Morning Routine
  • Attending Classes and Lectures
  • Lunch Break and Socializing
  • Research and Study Sessions
  • Extracurricular Activities and Dinner

Are you ready to start the day in the life of a data science student?

Just so you can really imagine it, we’ll explain how students spend key moments of their days in some of USA’s famous MS in Data Science universities. These are:

  • Worcester Polytechnic Institute
  • College of Staten Island (City University of New York)
  • Ying Wu College of Computing

Day in the Life of an MS Data Science Student

1. Morning Routine

  • Waking up and morning rituals:

Day in the life of a data science student is going to be busy with classes and projects. So starting the day with personal rituals like meditation helps to set a positive tone.

  • Exercise and self-care:

To maintain a healthy mind and body, engage in regular physical activities based on your interest. Many universities offer activities like fitness centers, yoga, running, swimming, or weightlifting.

  • Breakfast and preparing for the day:

Fuel your day with a nutritious breakfast, either from on-campus dining facilities or by preparing it at home. Don’t miss items like your laptop, charger, notebooks, and study materials.

Expert tips for a day in the life of a data science student:

Center for Well-Being at WPI

Source: CWB at WPI

2. Attending Classes and Lectures

  • Core subjects and electives:

The typical day for a data science student starts by attending classes. Focus on mastering core subjects and choosing electives that align with your interests and career goals.

  • Engaging with professors and classmates:

Actively participate in class discussions and connect with professors and peers. It will help you enhance your learning experience and build a strong academic network.

  • Utilizing campus resources:

Make the most of the resources available on campus, such as libraries, study spaces, and tutoring services, to excel in your studies and stay on track.

Expert tips for a day in the life of a data science student:

City University of New York

Source: City University of New York

3. Lunch Break and Socializing

  • Have lunch on campus:

Choose from a variety of healthy meal options available on campus to nourish your body and maintain energy levels throughout the day.

  • Connecting with fellow students:

Take the opportunity to engage with your peers during lunch breaks, fostering connections and building a supportive network of fellow data science students.

  • Sharing experiences and building a network:

Discuss your academic experiences, exchange ideas, and learn from your classmates’ diverse perspectives to enrich your understanding of the data science field.

Expert tips for a day in the life of a data science student:

The Goat’s Head at WPI

Source: The Goat’s Head, a new concept eatery at WPI

  • WPI’s dining services offer a diverse range of food options, including Starbucks, Pho U, and Paper Lantern, at The Goat’s Head in Founders Hall.
  • WPI also has a “ghost kitchen” concept, allowing for ultimate menu flexibility and convenient app-based mobile delivery service. 

4. Research and Study Sessions

  • Working with faculty and research groups:

Collaborate with faculty members and join research groups to get practical experience. It will help you learn more in detail about data science concepts and applications.

  • Attending additional lectures or workshops:

Participate in extra lectures and workshops to supplement your learning. It helps you stay updated on emerging trends, and broaden your knowledge of the data science domain.

  • Study groups and tutoring:

Join study groups or seek tutoring to improve your understanding of course material. It will help you clarify doubts, and improve your problem-solving skills in data science.

Expert tips for a day in the life of a data science student:

Academic Resources Center at WPI

Source: Academic Resources Center at WPI

  • Take advantage of CUNY’s tutoring services offered by the university’s academic support center. Seek tutoring services through the Academic Resource Center at WPI.
  • Attend the guest lecture series and workshops conducted by the universities. They will feature experts from academia and conduct industry discussions in data science.

5. Extracurricular Activities and Dinner

  • Volunteering and community involvement:

Participate in volunteering opportunities and community projects. This will enhance your personal growth, and gain valuable experience outside the classroom.

  • Sports and recreation:

Stay active and maintain a balanced lifestyle by engaging in sports and recreational activities. It is one of the best ways to stay physically fit and relieve stress.

  • Cultural and networking events:

Attend cultural and networking events to expand your horizons, and learn from diverse perspectives. You will also be able to grow connections that can benefit your personal and professional life.

  • Exploring off-campus dining options:

Discover off-campus dining options to enjoy diverse cuisines and experiences, giving you a well-deserved break from academic life.

Expert tips for a day in the life of a data science student:

Students volunteering at NJIT

Source: Students volunteering at NJIT

  • Participate in WPI’s networking events organized by the Center for Well-Being (CWB) like Tea Club and Cooking Club.
  • Get involved in NJIT’s sports and recreation offerings, such as intramural leagues, club sports, fitness classes, and access to the modern Wellness and Events Center
  • Join the volunteer opportunities listed by NJIT’s Office of Civic Engagement. They organize community service opportunities like Civic Chat and Celebrity Reads.
  • Choose CUNY’s off-campus dining options like Manhattan’s Koreatown, Flushing’s Chinatown in Queens, or the authentic Italian restaurants in the Bronx’s Little Italy for dinner.

As you can see, Data science student life highlights offer a well-rounded experience that goes beyond academics.

If you want to explore more, here is some further reading:

College Finder

Get personalized assistance to shortlist colleges, programs etc based on your profile.

Get a Headstart: In-depth Analysis of MS in Data Science Curriculum

From A to Z: Your Ultimate Guide for MS in Data Science Curriculum

From A to Z: Your Ultimate Guide for MS in Data Science Curriculum

Are you a graduate in engineering, mathematics, or another quantitative field who’s rethinking the educational choices you made?

Or perhaps you’re overwhelmed by the countless options, such as boot camps, graduate certificate programs, and online courses.

No matter your situation. We’ve got your back.

Data Science Platform Market

Source: The data science platform market will have a substantial growth of USD 68.02 billion between 2021 and 2026.

With the data science job market growing fast, it’s the perfect time to jump into this exciting field.

We’ll provide an in-depth analysis of the MS in Data Science curriculum through this ultimate guide. 

As you read further down, you will come across the following:

  • Overview of MS in Data Science program
  • MS in Data Science curriculum
  • List of 5 top data science universities
  • Preparing for the admission of MS in Data Science

So, join us. Let’s do an interesting read into the world of data science education!

Overview of MS in Data Science program

Data Science is the field where you use programming, mathematical techniques, and domain knowledge to analyze data. This analysis helps you develop insights that can be used for decision-making and problem-solving in corporate settings.

Choose MS in Data Science over other data science programs because:

  • It offers a comprehensive and in-depth curriculum that covers a wide range of topics in data science.
  • It gives a master’s degree that enhances your credibility and increases your competitiveness in the job market.
  • It provides opportunities to engage in research projects. Then you can collaborate with faculty members on cutting-edge research topics.
  • It takes 1.5 to 2 years to complete, that is 30 and 45 credit hours for completion.

MS in Data Science curriculum:

The core courses that form the backbone of the MS in Data Science curriculum:

  1. Machine Learning: You will learn to create models and algorithms that allow computers to learn from data.
  2. Big Data: You will learn to handle, store, and process large-scale datasets. It will help you extract valuable insights and make data-driven decisions.
  3. Data Visualization: Develop the skill to present complex data in an easy-to-understand format.
  4. Statistics for Data Science: You will gain a strong understanding of statistical concepts and methods. It will help you analyze data and make informed decisions.
  5. Data Wrangling: Learn techniques to clean, preprocess, and transform raw data. You can use it for a more usable format for examination.
  6. Programming for Data Science: Acquire proficiency in popular programming languages like Python or R.
  7. Databases and Data Storage: Understand various database systems and storage solutions that help manage and organize vast amounts of data.

As you progress in your Masters in Data Science, you’ll have to choose elective courses. Selecting the right electives will shape your expertise based on your career goals. 

Here are some popular elective courses and specializations in data science degree programs:

  1. Text Mining: Learn to extract insights from unstructured text data using natural language processing and machine learning techniques.
  2. Recommender Systems: Discover the methods behind personalized recommendations for products, services, or content, like those used by Amazon or Netflix.
  3. Computer Vision: Master the art of teaching computers to “see” and understand images or videos. It enables them to analyze visual content and make decisions.
  4. Data Engineering: Focus on designing, constructing, and managing large-scale data processing systems. It ensures data quality and efficient data pipelines.
  5. Network Analysis: Understand how to model, analyze, and visualize complex networks. It helps to reveal patterns and relationships.

Additionally, some universities offer internships or capstone projects in their MS in Data Science curriculum. It provides valuable experience, allowing you to work on real-world projects with companies to gain expertise.

List of 5 top data science universities:

  1. City University of New York (CUNY)
  2. New Jersey Institute of Technology (NJIT)
  3. Worcester Polytechnic Institute (WPI)
  4. University of Maryland (UMD)
  5. Carnegie Mellon University (CMU)

Preparing for the admission of MS in Data Science:

To secure your spot in an MS in Data Science curriculum abroad, here are some general steps to follow:

  • Qualifications:
    • Usually, a bachelor’s degree in a related field (math, statistics, computer science)
    • Some programs might need work experience in data science or related areas.
  • Prerequisites:
    • Brush up on essential skills like programming, statistics, and linear algebra.
    • Complete any missing courses from your bachelor’s degree by taking certificate courses.
  • Standardized tests:
    • GRE: Aim for a competitive score in both the quantitative and verbal sections
    • TOEFL/IELTS: Take one of these tests to prove your English language skills.
  • Application:
    • Statement of Purpose (SOP): Explain why you’re passionate about data science, your goals, and why you chose the specific program.
    • Letters of Recommendation (LORs): Ask professors or employers who can vouch for your abilities.

Are you ready to take the next step in your data science journey?

Then don’t make these mistakes:

  • Not verifying university-specific requirements: Reading individual university websites and contacting their admission offices for accurate and up-to-date admission details is essential.
  • Neglecting to research program specifics: Ensure you understand the requirements, curriculum, and unique features of each program before applying.
  • Ignoring university rankings: Consider the prestige and resources offered by the university. It can impact your future career opportunities.

Overlooking financial aspects: Research financial aid opportunities early. It will help ease the financial burden of pursuing higher education.

College Finder

Get personalized assistance to shortlist colleges, programs etc based on your profile.