Are you passionate about data science and considering pursuing a master’s degree in this field? This blog post aims to provide valuable information about scholarships available for international students studying data science at the master’s level. By exploring these scholarship opportunities, you can alleviate the financial burden and focus on your studies. Read on to discover scholarships, eligibility criteria, and the application process for pursuing a master’s in data science

Scholarship Opportunities for Master’s in Data Science:

  1. Data Science Scholarship Foundation: The Data Science Scholarship Foundation offers scholarships to international students pursuing a master’s degree in data science. Visit their official website at for detailed information and application guidelines.
  2. The Facebook Fellowship Program: Facebook offers a fellowship program for students pursuing research in fields related to data science, including machine learning and artificial intelligence. Learn more about the fellowship and application process at
  3. Microsoft Research PhD Fellowship: Although targeted at PhD students, Microsoft Research offers fellowships that support research in areas including data science. The fellowship provides financial support and mentoring opportunities. Visit for more information.
  4. IBM Ph.D. Fellowship Program: IBM provides fellowships to exceptional Ph.D. students pursuing research in areas including data science and artificial intelligence. While it is focused on Ph.D. students, it’s worth exploring for its potential impact on data science research. Details can be found at
  5. Google Ph.D. Fellowship Program: Google offers fellowships to outstanding Ph.D. students conducting research in computer science and related fields, including data science. Although aimed at Ph.D. students, the program provides valuable support and resources. Visit for further information.

FAQs about Master’s in Data Science Scholarships for International Students:

Q1: What are the eligibility criteria for these scholarships?
A: Eligibility criteria vary depending on the scholarship program. Generally, applicants should demonstrate academic excellence, have a strong background in data science or related fields, and meet specific requirements set by each scholarship provider.

Q2: Are these scholarships fully funded?
A: Scholarship benefits and funding vary across programs. Some scholarships may cover tuition fees, living expenses, and provide additional allowances, while others may offer partial funding. It is essential to review the details of each scholarship to understand the extent of the financial support provided.

Q3: Can I apply for multiple scholarships?
A: Yes, you can apply for multiple scholarships as long as you meet the eligibility criteria for each program. However, be sure to carefully review the terms and conditions of each scholarship regarding exclusivity and any specific requirements.

Q4: How do I apply for these scholarships?
A: To apply for these scholarships, visit the official websites of the respective scholarship programs mentioned above. Each program will have its own application process, deadlines, and required documentation. Follow the instructions provided on their websites to submit a complete and competitive application.

Pursuing a master’s degree in data science can be an exciting and rewarding journey. Scholarships specifically designed for international students can provide the necessary financial support to make your dreams a reality. By exploring the scholarship opportunities mentioned above and carefully following the application guidelines, you can increase your chances of securing funding for your master’s in data science. Don’t let financial constraints hold you backā€”take advantage of these scholarships and embark on a successful career in the field of data science.

Note: Scholarship availability, eligibility criteria, and application processes may vary over time. It is recommended to visit the official websites of each scholarship program for the most up-to-date information.