When to apply early and why it could be an added advantage for US universities?
Should you apply early in the 1st month of application acceptance?
Know that the admission committee has to fill a particular quota of seats every year. From my observation, in the 1st month of application acceptance, the admission committee tends to be lenient. The number of applicants you are competing with at this time is generally less. A lot of universities will start your evaluation as soon as you complete the application process but they might give the application decisions later. The available grants for scholarships/funding also tend to not have been given out in the 1st month of application acceptance and hence your chances of getting a scholarship also remain higher. Applying early will not only show initiatives from the the student鈥檚 side but also prove that you have taken efforts to apply early.
When not to apply early?
If your recommenders are going to send the LORs after 15-20 days then wait to submit the application because the application won't go into admission review unless all parts of the application are submitted. You can use this time to improve your profile. Don鈥檛 apply early if your profile is going to be improving in the next few weeks or 1 month like a promotion that may be coming up or if your research paper is to be published.
The factors that affect your chances (from highest to lowest weightage) for a tech degree are-
Bachelor's CGPA/percentage, GRE, TOEFL score, Projects accomplished(profile of a student for the degree, competitions won if any, research work if any.
Make your profile fit for a particular field like AI, cloud, security or any other field that you might be interested in. Let's say for instance you are interested in AI.
1. Start with python, learn Github (very important), upload your projects on github. A lot of people just upload the projects to GitHub and leave them. Do not do this. Make a front end for it and publish using GitHub pages or any other publishing tool. Have a github documentation. 3-4 published documented projects are better to show than 6-7 non-documented projects.
2. Compete in Kaggle competitions as much as possible.
3. Andrew Neg's AI Coursera certifications are good to gain knowledge, University of Washington AI specialization, Stanford ML, deep learning specialization by 鈥榙eeplearning.ai鈥�. It is not necessary to complete all of these. But it's good to have them on your resume and would help you gain knowledge in the fields of AI. Udemy has a few good courses also but I don't remember them. Start with Udemy and then move on to Coursera.
4. Write an AI research paper in a reputed institution like IEEE or Springer. Give paper conferences in IIT level institutions or better in the virtual conferences held in the USA via skype. USA universities look for research work in the profiles of applicants.
5. Complete at least 1 internship in the AI field in the summer. You will have to search for an internship at least 6 to 9 months early to avoid the competition.
6. For your final year project, an industry AI/ML project is better than an in-house project. You will get to solve real-world challenges.
7. Try to get as good grades as possible in data science, AI, and ML subjects. This will not only help you to get an admit in the AI field degree but also help you to get a TA or RA in the same field.
You can follow similar steps for the fields you might be interested in. Lastly, applying early for your master's degree would be best. But if your profile is expected to be improved drastically in a month or 2, then waiting is also a good decision. DO NOT WAIT till December. You will face problems like lagging, document uploading problems, and slow email replies from the universities that would take more time. Applying early, in the 1st week of application acceptance, will not only increase your chances of getting funding but also increase the chances of getting an admit. Universities tend to be lenient at the start of application acceptance and not so much as you approach near the deadline.
Hope it helps !!! Good luck !!
#masters #applicationprocess #ProfileBuilding
