<p>Each year, thousands of students apply to Data Science graduate programmes abroad, as the course is regarded as a stepping stone to a lucrative career. Demand for professionals with Data Science skills across industries grew 29% year-on-year, a 344% increase from 2013 (Indeed Report, 2019). With six-figure starting salaries and mid-range base salaries, the field attracts a vast pool of aspiring applicants. But top universities and colleges have only a 10 - 20% acceptance rate, and competition is fierce. Universities are looking for applicants who demonstrate a strong aptitude for Data Science. How can students ensure their application stands out from the rest? </p>.<p>Choosing the right programme and area of focus is most important. This step can be fairly complex. The field is relatively new and still evolving. There are many types of jobs under the wider ‘Data Science’ umbrella. These include Data Scientist, Machine Learning Engineer, Data Architect, Data Analyst, and more. The type of graduate programme, and an individual’s aptitude and strengths greatly impact the job opportunities available after graduation. And master's programmes for Data-Science-related skills differ from university to university. Some programmes are stand-alone while others are nestled under other departments. </p>.<p>For example, New York University, Columbia, and the University of San Francisco have stand-alone Master's in Data Science programmes. Stanford’s MS in Data Science is under a statistics specialty. They also offer an MS in Computer Science with focused tracks in Artificial Intelligence, Information Management, and Analytics. Carnegie Mellon’s Computer Science department offers an MS in Machine Learning, but two other departments provide other tracks - an MS in Computational Data Science, and an MS in Statistical Practice. Navigating these options can get quite confusing. Apart from carefully comparing course curriculum for each programme, students must gather real-world advice. College seniors, working professionals, or admissions counseling experts can help students shortlist the right programs. </p>.<p>There’s a common misconception that Data Science is mostly for Computer Science graduates. In reality, anyone with an undergraduate degree in engineering, math, or statistics can qualify. We advise interested students in these fields to learn programming languages like R or Python to round off their skill set – if needed, through an external institute. A background in data structures and algorithms is a huge plus. Data Science programmes abroad prefer students strong in quantitative subjects. So, higher grades in probability, statistics, or operations research are an advantage. One or more internships in the field are essential to demonstrate the ability to apply theoretical concepts in the real world. </p>.<p>Many underestimate the power of a persuasive Statement of Purpose (SOP). A well-crafted SOP is a great way to showcase student profiles as the right fit for a specific university and programme. We find that the SOP plays an even more significant role in the application process when, for instance, a student does not have a high undergraduate GPA but is strong in quantitative and programming subjects. Another example is when students from a non-programming specialty wish to change majors to a Data Science track. Using the SOP, a student can describe their rationale for switching, strengths in relevant courses, and highlight outside certifications and internships to make a strong case in their favour. </p>.<p>For jobs in Data Science, Silicon Valley giants dominate the conversation. But predictive analytics based on Data Science now powers even traditional industries such as finance, management consulting, healthcare, pharmaceutical, logistics, and much more. Demand is high while supply is still low. Students applying to graduate programmes abroad must create an attractive profile to improve their likelihood of getting admitted to a good school. This reflects well in the visa approval process and helps propel graduates towards a rewarding career in Data Science. </p>.<p><span class="italic"><em>(The author is the Founder of an EdTech that provides study abroad test prep and admissions counselling)</em></span></p>
<p>Each year, thousands of students apply to Data Science graduate programmes abroad, as the course is regarded as a stepping stone to a lucrative career. Demand for professionals with Data Science skills across industries grew 29% year-on-year, a 344% increase from 2013 (Indeed Report, 2019). With six-figure starting salaries and mid-range base salaries, the field attracts a vast pool of aspiring applicants. But top universities and colleges have only a 10 - 20% acceptance rate, and competition is fierce. Universities are looking for applicants who demonstrate a strong aptitude for Data Science. How can students ensure their application stands out from the rest? </p>.<p>Choosing the right programme and area of focus is most important. This step can be fairly complex. The field is relatively new and still evolving. There are many types of jobs under the wider ‘Data Science’ umbrella. These include Data Scientist, Machine Learning Engineer, Data Architect, Data Analyst, and more. The type of graduate programme, and an individual’s aptitude and strengths greatly impact the job opportunities available after graduation. And master's programmes for Data-Science-related skills differ from university to university. Some programmes are stand-alone while others are nestled under other departments. </p>.<p>For example, New York University, Columbia, and the University of San Francisco have stand-alone Master's in Data Science programmes. Stanford’s MS in Data Science is under a statistics specialty. They also offer an MS in Computer Science with focused tracks in Artificial Intelligence, Information Management, and Analytics. Carnegie Mellon’s Computer Science department offers an MS in Machine Learning, but two other departments provide other tracks - an MS in Computational Data Science, and an MS in Statistical Practice. Navigating these options can get quite confusing. Apart from carefully comparing course curriculum for each programme, students must gather real-world advice. College seniors, working professionals, or admissions counseling experts can help students shortlist the right programs. </p>.<p>There’s a common misconception that Data Science is mostly for Computer Science graduates. In reality, anyone with an undergraduate degree in engineering, math, or statistics can qualify. We advise interested students in these fields to learn programming languages like R or Python to round off their skill set – if needed, through an external institute. A background in data structures and algorithms is a huge plus. Data Science programmes abroad prefer students strong in quantitative subjects. So, higher grades in probability, statistics, or operations research are an advantage. One or more internships in the field are essential to demonstrate the ability to apply theoretical concepts in the real world. </p>.<p>Many underestimate the power of a persuasive Statement of Purpose (SOP). A well-crafted SOP is a great way to showcase student profiles as the right fit for a specific university and programme. We find that the SOP plays an even more significant role in the application process when, for instance, a student does not have a high undergraduate GPA but is strong in quantitative and programming subjects. Another example is when students from a non-programming specialty wish to change majors to a Data Science track. Using the SOP, a student can describe their rationale for switching, strengths in relevant courses, and highlight outside certifications and internships to make a strong case in their favour. </p>.<p>For jobs in Data Science, Silicon Valley giants dominate the conversation. But predictive analytics based on Data Science now powers even traditional industries such as finance, management consulting, healthcare, pharmaceutical, logistics, and much more. Demand is high while supply is still low. Students applying to graduate programmes abroad must create an attractive profile to improve their likelihood of getting admitted to a good school. This reflects well in the visa approval process and helps propel graduates towards a rewarding career in Data Science. </p>.<p><span class="italic"><em>(The author is the Founder of an EdTech that provides study abroad test prep and admissions counselling)</em></span></p>