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Application Preparation for MS in Data Analytics

The MS in Data Analytics (MSDA) program at the University of Texas at San Antonio (UTSA) is all set to welcome the third batch of students this fall. The program attracts many talented applicants globally. However, due to resource constraints, we can admit only a small number of students. The program has two cohorts—daytime and evening. This year we expect to admit anywhere between 80 to 100 students in the two cohorts combined.

The objective of this post is to help prospective MSDA students in preparing their MSDA applications so as to increase their chances of getting admitted to the program. The suggestions below are actually applicable to any good analytics program.

Standardized test (GRE/GMAT) preparation

MSDA program requires either GRE or GMAT scores from most applicants although waivers are available if they meet certain criteria.

  1. Schedule your standardized test such that you have enough time to prepare for it. Many students score high on verbal part but score low on quant section. Although a high score in quant doesn’t imply superior quantitative skills, a lack of good quant score is likely to be a fair indicator of a) poor quant aptitude and/or b) low motivation to prepare for the test. Neither of them is a quality we look for in our students.

  2. If your test score is low, we encourage you to retake the test with the aim of improving the score significantly. If you are aiming for set thresholds, such as 80%, you are likely to under-prepare for the test.

Coursework in statistics

There are three situations in which we recommend you to (re)take a statistics course. If you can’t take the course in your university, we outline a few online options below.

  1. If you have taken a statistics course well in the past but you don’t use statistics in your studies, research, or job.

  2. If you have completed your statistics course recently, but your grade is lower than A-.

  3. You have never taken any statistics course in the college.

A few examples of available online options are as follows:

You can also watch videos uploaded by MIT

Evidence of programming skills

Programming is necessary for data analytics.

  1. In MSDA, we primarily use R, Python, and SAS. R and Python are open-source software. SAS has a free university edition. If you have never used any of these, pick one of the three software and get to know it well. Create small projects and post them publicly on Github or on your own blog.

  2. Take online courses to learn R or Python on Coursera, Data Camp, Udacity, etc. Here is a sample of these courses:
  1. If you are skilled in web development and know javascript, html, etc., show us some evidence such as certification and/or sample of your work.

Evidence of interest in data analytics

Apart from evidence of skills in statistics and programming, there are a few other things you can do to show to us that you really care about data analytics.

  1. In your city, identify groups of professionals in data science, machine learning, artificial intelligence, R programming, etc. and network with them. This will help you understand the job responsibilities of a data analyst and data scientist better. Many of these meetups are easily searchable through Meetup.com. For example, in San Antonio we have a San Antonio Artifical Intelligence Meetup, which has, as of today, more than 280 members.


  1. If you are a working professional, seek out data analytics opportunities within your organization. It could be a simple task that uses only Microsoft Excel.

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