How to plan the timeline of your Fall applications?

Many people are shocked to learn that applications to universities abroad can take anywhere between 6-9 months.

It’s not a sprint, it’s a marathon. And, to do well, you need to have a proper plan in place.

The good news is your future does not depend on the outcome of a single exam. One bad day doesn’t ruin you. You can do many things to put up a winning application in the end.

First things first – you need to follow the timeline so that you do not lose track of multiple things that need to be completed. Applying late or very close to deadlines can be detrimental to success. You do not want to do that.

What is the best time to apply to maximize your chances of admit? When should you take GRE? How long should you work on your SOP and essays? If you have all these questions, you are in the right place.

MS in USA application process can be overwhelming and cumbersome. So, here we have tried to put up an easy to understand timeline so that you can keep a tab on what needs to be completed when.

Discipline and time management is key to cracking your dream schools. And it starts from TODAY.

GRE/TOEFL

Take by end of Aug so that you have enough time to retake if necessary
Retake GRE if required by end of Sep (need 21 days between reattempts)

Shortlist Schools

by end of Sep – mid Oct. Here’s full post on how to shortlist schools.

Email Professors

Drop emails to relevant Professors you are interested to work with (especially for people interested in research) by mid of Oct.

Apply for WES

If any of your shortlisted schools require a WES evaluation, apply for it by the end of Oct since it can take a month or more to get their results.

Check for scholarships

There are some third party scholarships available for MS applicants. Search these by end of Oct (so that you can apply by their stated deadlines)

Order transcripts

from your undergraduate college by end of Oct

Finalize SOP, essays and LORs

by mid Nov

Fill the application online and send application packets where required

by end of Nov – mid Dec

Follow up on the status of your applications

and if the required material has been received by the schools in Jan. Resend if anything is amiss

Receive results

by end of Mar-Apr

Submit financial docs for I-20

by end of Apr

Finalize your school and apply for education loan

by May

Appear for visa interview

by end of June

Arrive on the campus

by Aug (2 weeks earlier if you need to look for funding on campus)

That’s it – it is not as cumbersome if you know what to expect. Save this infographic for a quick reminder.

Timeline for MS applications (fall)

For personalized assistance for your applications, check our counseling packages.

Cost of MS in USA in INR – how you can afford it

study-abroad-finances

In this post, we discuss how much does it cost to do an MS in USA and why it is affordable for you. Some of the content is taken from the MS Book: Smart Engineer’s Complete Guide to MS in USA.

Understanding Grad School finances

If you are concerned about finances of managing a Master’s degree education, a good way to start is to understand:

  • how much does it cost to do MS in USA
  • how long will it take you to recover your expenses

For this, we need to understand what total expenses you are likely to incur, what kind of income are you expecting after graduation and how much can you save. This is known as break even analysis and I have covered it in detail in the MS book so that you know what to expect. Doing this analysis using the tools in the book will let you feel more comfortable about the decision. 

What is the cost of doing MS in USA?

The cost of MS in USA is a sum total of what you spend in getting there + what you spend after getting there. Following is the breakdown:

  • Taking the tests – $500
  • Applying to Grad Schools – $1000-1500
  • Visa – $600 (F1 and SEVIS fee)
  • Traveling to USA – $1500
  • Tuition fee – $50,000
  • Living Expenses – $18,000

In short, the cost of doing your Master’s in US can go up to $75,000 which comes to ₹50 lac at the exchange rate of 1 USD = INR 75.

How long does it take to recover your money?

For e.g. if you spend $77000 on your MS (tuition + living + interest on loan), getting $14000 in summer internship, paying a 12% interest rate on your loan of Rs 20L, graduating in 4 semesters and saving 40% of your post-tax salary ($84000 p.a), it will take you roughly 3.15 years to break even in worst case. If you find any assistance or on-campus job (which is very feasible and again, discussed in the book) or graduating in 3 or fewer semesters, you can break even sooner.

breakeven

The details of the calculations are covered in the book. You get to download a sheet (snapshot above) that shows you exactly how long will it take to recover your investment in the Grad School. You can add variables as per your specific scenario and play with it.

Let’s say that the sheet tells you that it will take roughly 2 years to recoup your money. Usually, the starting salary in US far exceeds the package in India at that level upon graduation. You can compare the financial benefits and kind of work exposure you are getting in US to decide whether it makes sense for you.

What did I get from doing an MS in USA?

Professional value

Coming from a middle class family and a mediocre college in Central India, I received admission from MS in CS program at UIUC. That was the 4th best program in the world. From there, I managed to secure an internship at Wall Street for which only 7 people were selected all across the world (I was actually the only girl in there). After getting one of the highest packages ($100,000+) during those days, I decided to get into startups and secured admission to the MBA program at New York University which is 10th best MBA program in the world.

Seeing the world

I have lived in Manhattan, worked at Wall Street and startups. That is where I got to network with some of the finest entrepreneurs and investors in USA. Meanwhile, I traveled the world and finally came to India to work on my own ventures and write. I find my life personally and professionally rewarding and I want you to enjoy yours without compromising on your dreams.  

Finding the joy of helping people

Plus, we have helped some of the students at Scholar Strategy inch towards their dream life. For example, one of our first students, Supreeth, had a failed application season before he joined us. He had a good profile but had some weaknesses. We helped him highlight his strengths, focus on the programs that were better for him and re-apply. He wanted to get into Robotics and he was able to get into one of the Top 10 programs in that field. After a year, he emailed us to inform that he will be interning at Microsoft Research Labs. Now that is what I call a perfect hustle and not giving up till you reach where you want to be.

You would say I got lucky or Supreeth got lucky or I had some advantage that you don’t. But my background will show you that I began just like you are now. And that anyone can reach the places I did. Back in college, I also thought that US education is unaffordable but now I know how wrong I was! It is within your reach. Not only that, I strongly believe it is worth all the expenses.

I devoted an entire section in the MS Book to tell you how to think about money and finances and why MS is totally affordable. But let’s quickly understand how to look at it here.

Education is not an expense but an investment in yourself

I considered my MS fee as an investment and not an expense. If that is how you look at it, then you realize that its not the money that is sinking down the drain but you are gaining:

  • Skills that make you employable
  • A credible degree in the resume
  • A great brand name to help you stand out in the competition

Imagine applying for a job with a credible international grad school degree in your resume. Who is the employer going to recruit? You with a reputed degree from US or someone with a relatively unknown degree in India? 

And with a little hustle, you can manage funding for your education abroad.

msbook-guide-to-ms-in-usa
Smart Engineer’s Complete Guide to MS in USA

MS in Canada compared to MS in USA

MS in Canada is becoming an increasingly lucrative to international students looking to study abroad. Let’s see its pros and cons over MS in USA.

Why apply to MS in Canada?

1. H1B and green card uncertainty in USA due to Trump policies

Trump has hinted that he does not like immigrants (which is the intention of 90%+ internationals who go for MS in USA), H1B caps and OPT rules are constantly under scrutiny and green card processing takes forever.

In comparison, you easily get work permit upon graduation in Canada for 2-4 years in any field to work anywhere you want. The study permit itself allows for taking jobs off campus right from the beginning. Within this time frame, you can easily get your permanent residency. Therefore, studying from Canada means not only getting higher education but a hassle-free option of settling in Canada.

2. The high quality of living and curriculum

Canada’s political stability, tolerant government, super healthcare, natural beauty lends itself to the wonderful quality of living in a peaceful environment. Yes, winters might be an issue for some people.
I saw the ultimate reward of being in Canada in our alumnus’ Rafi’s response –

“I used to weigh 120+ kg in India and had multiple health issues. After coming to Canada and seeing the fitness of people around me, it made me work hard to become healthy myself. Today I weigh 70 kg and have learned swimming and skiing. I love it here.”

While Canada may have a fewer number of schools than the USA, most of them are comparable in quality of coursework and research to the top tier schools of USA.

3. Not so bad job opportunities

Plus, for all the health and wellness benefits it offers, the job scenario is also not bad (we are talking about engineering fields for this post). There are increasing opportunities in growing fields such as Data Science. Plus, most of the bigger tech companies are opening offices in Canada if not already.

In terms of the cost of attendance and living, it may be akin to the USA in total. However, financial aid opportunities seem to be abundant in good Canadian universities.

Also read: Courses with high ROI in Canada

So, where is the catch?

As is the case we discussed in MS in Germany blog post, downsides of studying in Canada are:

1. Lower job packages

While it is not unheard of to get $100K USD+ packages in the USA in software and technology along with handsome relocation bonuses, Canadian offers are lower with little bonuses.

2. Lower possibilities of working in the USA

It is hard to get placement in US offices from Canada. So, if you graduate from Canada, you are best positioned to work in Canada only. I still believe that the USA offers the best job market and growth opportunities in most of the engineering fields.

To summarize, those looking for long-term settlement, relocation to a foreign country and peaceful living, Canada offers you a wonderful opportunity. But if you are studying abroad to earn as much as possible and might want to come back to India, USA is still a better bet.

The comparison is more clear from this interview with Rafi Alam. He shares insights about studying and working in Canada.

Also read: MBA in Canada for Indian applicants

Hear it from someone who pursued CS from Waterloo

Hear it from Rafi Alam

Here is someone who studied at University of Toronto

Read Shreya Rajput’s account of studying in Canada – how do the work visa and PR work?

That’s it, hope it helps you make a more informed choice for your study abroad plans!

We have now covered MS in Germany, MS in Canada and MS in USA. Which other country are you considering and would like to know more about?

Interview – Should you consider doing an MS in Germany?

MS in Germany can be lucrative

Did you know that Hyperloop competition by Elon Musk was won by TU Munich students for a second time! That is not all, pursuing MS in Germany can nearly be free in technical streams. German schools further help all students in getting co-ops kind of internships for practical training. However, there is still a catch when you compare it to USA. Read on to find out…

In an interview with Sangram Gupta, we chat about his experience of pursuing an MS in CS from TU Munich, Germany. He is also interning with a Business Intelligence startup called Incuda.gmbh where he is earning 15 euros per hour. Best thing – he did not spend a single penny in his MS.

Interview covers

  • How helpful is the curriculum like?
  • How long does it take to do an MS?
  • How much does one spend on graduate school in Germany? (Its almost free!)
  • Who should apply to German schools (which kind of programs are best)?
  • What is the process to apply to programs in Germany?
  • What is the internship process and ease of doing internships in Germany?
  • What is the employment scenario in Europe?
  • Why you should or should not consider MS in Germany? (Conclusive comparison with USA)

Video

Let us find out more directly from him in this video-

Resources

Here is the list of top German schools that Sangram considered while applying.

We have now covered MS in Germany and MS in Canada. Which other country are you considering and would like to know more about?

MS in Financial Engineering courses

“Everyone from derivatives traders to hedge fund managers want to predict financial market outcomes. Who wouldn’t bet on a sure thing? The reality is that no one can really guarantee where the stock market is headed. But don’t think people aren’t trying. Financial engineers are the folks that just might figure it out.” – reads the description on Baruch Master’s in Financial Engineering program website. Although that sounds a tall claim to me, the fact is that these programs are on a rise.

For long, finance as a field attracts engineers because of the stupendous compensation packages as well as the glamor. While the traditional route has been to study MBA later in your career to move into Banking or Trading, why compete in those super-duper competitive Business Schools? Why not go directly for a Finance course? Enter Master of Financial Engineering (MFE) degree programs such as CMU, Columbia, Berkeley, UCLA, NYU, Cornell, Washington etc. Baruch is probably the cheapest of the lot but ranked quite high.

The finance industry needs people who possess deep mathematical modeling skills and computational expertise. While no specific undergraduate major is required, most students will have a degree in quantitative subject matters such as mathematics, engineering, computer science, physics, etc. Engineer’s quantitative and programming skills lend themselves well to solving the complex and creative challenges of today’s financial markets. Although it is not related to traditional branches of engineering, the field picks a lot of people with engineering backgrounds (as is true with MBA). Financial engineering draws on tools from applied mathematics, computer science, statistics and economic theory to address current financial issues as well as to devise new and innovative financial products.

What specializations are possible within MFE?

One can venture into Computational Finance, Risk Management, Corporate Finance, Algorithmic and Technical Finance etc.

What career options open up after MFE?

Following image is taken from UCLA MFE course website. As it shows, one can go into risk management, trading desks, financial services, quant pricing, regulatory roles etc. Often incoming students are also coming from similar roles and are looking for further specialization. In non-finance category, probably engineers make the biggest chunk of the incoming class.

When should you go for MFE?

If you have made your mind to get into finance industry and some relevant experience or background, MFE might offer you good opportunities with the advantage of lower competition than traditional MBA (Stanford MBA has acceptance rate of <6%). Plus, make sure you love Mathematics!

How to prepare your application for MFE?

Join us and we will guide you with our very strategic guidance! 🙂 Strong quantitative background, preparation for CFA, programming skills, mathematical modeling tools knowledge etc helps. Rest, we help you step by step in crafting a strong story and guide you through our selective sample SOPs.

Why do applicants prefer Fall semester over Spring?

ms in usa - fall vs spring

If you did not receive your dream admit in Fall or if you had missed your Fall deadlines, you are probably considering a critical question that many applicants are confused about – Fall vs Spring?

Although it remains inconclusive whether starting your graduate school in Spring adversely affects your admission chances, there are few things you should know about applying for Spring semester:

School shortlisting in Fall vs Spring

Primarily, not all schools have a Spring intake and hence, your choices of programs that you can apply to are fewer than what you have in Fall. Moreover, some schools have Spring applications only to fill out their left over seats from Fall admissions and admit students on a rolling basis. Some of the schools that have a Spring intake are:

  • Cornell
  • UIUC
  • Purdue
  • CMU
  • University of Wisconsin Madison
  • USC
  • NYU
  • Syracuse

Getting a summer Internship Fall vs Spring

You can get a CPT only after 9 months of your arrival into USA. For this reason, Spring students cannot get a CPT to intern off-campus during their first summer. However, they can still do a full-time summer internship their second summer but they need to attend one more semester at their school after finishing the internship. This means that they cannot do a summer internship in the second year if they are planning to graduate in 3 semesters only. If a summer internship is critical for you, then consider applying for a course beginning in Fall so that you do not face such restrictions.

Do enquire with your department if summer internship is possible after going in Spring. Our student Praneeth confirms that SUNY Stonybrook does NOT allow it.

Getting Financial Aid

While some people argue that chances of scholarships are low in Spring than Fall, there is no evidence to support this. It all depends on the availability of funding with the university and the Professors.

Availability of Courses in Fall vs Spring

There is another notion that not all the mainstream courses are offered in Spring. Again, I have seen this both ways i.e. some courses are offered in Fall typically and some in Spring, so there is no general rule across all the schools. More popular courses might be offered preferably in Fall since that is the most common intake period and number of students enrolling for the courses are higher in Fall. However, it is better to take a look at the curriculum before assuming that you will not find enough good courses in Spring. You might be interested in some courses which are offered in Fall only but the chances are that you can take it in your second semester anyway. Many courses are designed such that you can take first part in fall second part in spring, you wouldn’t be able to take these courses in spring.

Accommodation, Weather and Networking

It might also be very difficult to get accommodation when you join during spring as most leases start fall and end in summer. You also miss the big university wide orientations that happen during fall. January weather in east coast is pretty bad too.

This is part of the MS Book. To get more such relevant information, please check out MS Book: Smart Engineer’s Complete Guide to MS in USA.

Results 2015-2017

Here are the fabulous results for Spring/Fall 2017, we have some great new schools to our portfolio now – UIUC MS CS, UT Austin, MS CS, UCLA MS CS, UC Davis PhD, Wisconsin Madison MS EE, CMU ECE, Berkeley MIS, Foster MIS, Virginia Tech CS, Purdue Mechanical etc etc! If you are applying next year and want to work with us, check out our Counseling Options.

  • Kanagaraj – MS/PhD CS (315, 9, 5 yrs work-ex) – UMN, Virginia Tech
  • Aketh – MS CS (325, 8.6, 3 yr work-ex) – UTD Spring, NEU, SUNY SB, NYU Courant
  • Praneeth – MS CS (311, 0.81, 3 yr work-ex) – UTD, SUNY SB Spring
  • Prateek – MS CS (317, 0.71, 3 yr work-ex) – ASU IT, NEU CS, UC Irvine SE, UTD
  • Arjun – MIS Analytics (316, 6.7, 2 yr work-ex) – Syracuse
  • Sangram – MS CS (319, 7.3, 2 yr work-ex) – TU Kaiserslautern, TU Munich for MS in CS
  • Nitesh – MS CS (315, 0.8, 3 yr work-ex) – Rutgers MBS Analytics, Syracuse CE, NEU MSIS
  • Sneha – MIS Analytics (321, 0.83, 3 yr work-ex) – U Connecticut MSBA, Buffalo MIS, Cincinnati MSBA, Simon Fraser MS BigData
  • Somendra – MS Data Science (317, 8.6, 4 yr work-ex) – NCSU, ASU CS
  • Aashray – MIS Analytics (327, 8.25, Fresher) – Berkeley OR, Dartmouth MEM 40% aid, UMCP MIS, John Hopkins MSEM
  • Harish – MS EE Robotics (316, 0.64, 2 yr work-ex) – Colorado State Uni, Boston U, George Mason U
  • Khushboo – MS CS (319, 0.76, Fresher) – Syracuse 30% schol, Delaware, UTD, IIT Chicago, UNCC
  • Ankan – MS Aerospace (318, 8.23, 2 yr work-ex) – ASU, NCSU, UIUC,Virginia Tech, Penn State MEng
  • Bibin – MS EE Power (323, 8, 10 yr work-ex) – CU Boulder Power Elec Spring
  • Naveen – MS Data Science (315, 7.64, 6 yr work-ex) – NCSU MSA interview, ASU MSBA, George Washington DS
  • Akhil – MIS MEM (304, 0.7, 3 yr work-ex) – UIUC MS IM Spring
  • Sharang – MIS (325, 65% 3.04, 3 yr work-ex) – CMU 12 mon, IUB interview, UMCP, TAMU, Cincinnati with schol
  • Ujjwal – MS Data Science (314, 0.68, 4 yr work-ex) – UTD CS, UMN DS, ASU SE
  • Ela – MIS Analytics (306, 0.78, 4 yr work-ex) – UTD, Syracuse, GSU, NEU
  • Khushboo – MEM (312, 9.21, 2 yr work-ex) – ASU IE, UIC IE, NEU MEM, Rutgers IE, UTD SCM, TAMU MS ESM
  • Vipul – MS Embedded (313, 0.807, Fresher) – ASU CE, CU Boulder Professional MS Embedded
  • Saurabh – MIS Analytics (317, 6.9, 3 yr work-ex) – IUB interview, UMCP MIS, Cincinnati, NYU MSIS
  • Bhargav – MS CS (329, 9.4, Fresher) – UC Irvine, NCSU, Virginia Tech, USC, Stonybrook PhD (funded), UCLA
  • Puneet – MSOR, MSBA (322, 0.73, 3 yr work-ex) – UMN 10K schol BA
  • Monica – MSOR, MEM (323, 8, Fresher) – NCSU MSOR, Purdue IE, Duke MEM
  • George – MS EE Wireless (325, 9.36, Fresher) – USC, Georgia Tech, UCSD, UWashington, CMU MS ECE (Spring 2018)
  • Niket – MS CS (339, 0.525, 7 yr work-ex) – SJSU
  • Raghav – MS IE, OR, MEM (312, 8.34, 2 yr work-ex) – Duke, Notre Dame ESTEEM (25% schol), NYU MoT (4k)
  • Yashovardhan – MS CS ML (318, 8.2, 3 yr work-ex) – ASU MCS Big Data, CMU BIC, KTH Sweden ML, UFL
  • Ashish – MIS, MEM (321, 8.54, Fresher) – UMCP MIS, Duke interview, TAMU, Columbia Applied Analytics, CMU 16 mon
  • Balarama – MS Mechanical (328, 8.12, 3 yr work-ex) – CMU
  • Neel – MS EE Semiconductors (319, 8.76, Fresher) – UPenn MSE in EE, UMN EE, ASU
  • Valliappan – MIS, MEM (315, 8.27, MBA, 2 yr work-ex) – John Hopkins – Carey(Dean’s schol $16K ~25%), UMCP, TAMU, GSU, Utah, UFL, USF
  • Vinayak – MEM, MS Finance (320, 7.88, MS Econ 3 yr work-ex) – Georgia Tech MFE, Columbia MFE
  • Karttik – MIS, MEM (320, 8.8, 3 yr work-ex) – Syracuse, Cincinnati, IUB interview, TAMU, CMU 16 mon
  • Jayeeta – MIS, MEM (313, 7.75, 4 yr work-ex) – GSU, Syracuse, UIC, UTD, UT Austin MS IS
  • Manoj – MS CS (313, 8.1, 2 yr work-ex) – ASU MCS, UTD, NEU
  • Tushar – MIS, MEM (315, 8.9, 1 yr work-ex) – Notre Dame ESTEEM 20K schol, USC MEM, CMU MISM 16 mon, John Hopkins MEM, Dartmouth MEM
  • Sreedev – MS Mechanical (319, 3.77, 2 yr work-ex) – UC Davis PhD (full schol), UMCP PhD
  • Kartika – MS CS (311, 7.13, Fresher) – Syracuse, RIT HCI, UWashington HCDE
  • JVN – MS IE, Data Sc (321, 8.9, 1 yr work-ex) – UT Dallas(MS in CS), UIUC(MS in IE with concentration in analytics)
  • Nimish – MS CS Data Sc (320, 7, 4 yr work-ex) – CMU 12 mon MISM, NCSU
  • Kovuru – MS Embedded (311, 7.9, 4 yr work-ex) – ASU CE, CU Boulder Professional MS Embedded
  • Amogh – MS CS (313, 7.34, 3 yr work-ex) – Stonybrook, Colorado, ASU MCS
  • Mayank – MSBA (323, 0.73, 3 yr work-ex) – UMN MSBA (after interview), CMU BIDA
  • Ayush – MS CS, MIS (320, 7.87, Fresher) – UMCP MIS, Syracuse MIS, UTD MIS, NEU CS, NYU Poly CS
  • Navya – MS EE Comm (327, 9.2, 2 yr work-ex) – UCSD, UMich Ann Arbor, ASU, UCI
  • Adithya – MS EE (324, 8.3, 1 yr work-ex) – Virginia Tech, MSU with RA, ASU, UTD Power Systems, TAMU Power Systems, Utah
  • Mariyah – MIS, MEM (306, 8, 3 yr work-ex) –CMU 12 mon, UIC, UMCP, UIUC MSTM, NEU MEM, Duke MEM
  • Raghav – MIS Analytics (312, 0.583, 2 yr work-ex) – Syracuse, UTD 50% schol
  • Abhijith – MS/PhD ECE (323, 9, 2 yr work-ex) – UPenn MSE EE
  • Arun – MS EE Power (320, 7.3, 3 yr work-ex) – NCSU, ASU, TAMU, Wisconsin Madison
  • Shripal – MS CS (320, 7, 3 yr work-ex) – ASU SE
  • Ayesha – MS Data Sc (304, 8, Fresher) – IUB DS, CMU MISM global, USC Data Informatics
  • Priyanshi – MIS/MEM (303, 0.67, 2 yr work-ex) – IITC, Stevens
  • Siddharth – MS CS (329, 0.715, Fresher) – Stonybrook
  • Shravya – MS CS Networks (325, 9.3, Fresher) – CMU MSIN (with fellowhip), Georgia Tech, UCLA, USC (CS and Networks)
  • Anmol – MS Mechanical (324, 7.9, Fresher) – Purdue
  • Sushruth – MS CS (306, 0.7, 2 yr work-ex) – RIT, UCF, Santa Clara, Syracuse
  • Payal – MS Analytics (720, 0.72, 2 yr work-ex) – CMU BIDA, UT Austin interview, USanFran interview, UCSD MSBA & NYU MoT (14k)
  • Abhinav – MS CS Data Sc (327, 9.94, 4 yr work-ex) – UIUC, USC Deans scholarship, UT Austin, UCSD, Columbia, UC Berkeley (MEng)
  • Adarsh – MSIE, MEM (320, 7.9, 4 yr work-ex) – NYU MoT, NCSU IE, Duke
  • Palash – MS Software Engg (301, 0.649, 2 yr work-ex) – IITC CS, NEU, UT Arlington, SE, UNCC MSIT, Southern Methodist SE
  • Arihant – MS CS (317, 7.4, Fresher) – UIC
  • Gursimran – MS CS (325, 7.3, 4 yr work-ex) – UC Davis PhD, U of Alberta MS (full funding), Purdue MS CS, ASU MS
  • Yash – MSOR (317, 7.9 MBA, 6.9 BTech, 8 yr work-ex) – Oklahoma State PhD, NYSU, Buffalo
  • Krushab – MS CS, MIS (, 0.617, 2 yr work-ex) – BU, Washington State MS CS, Syracuse, UIC, Stevens, NJIT MIS
  • Siddhant – MSBA (322, 5.2, Fresher) – Penn State Great Valley, UTD
  • Nymisha – MSBA (650, 8.4, 2 yr work-ex) – UIC, UConn, IE Spain
  • Abhishek – MSBA (310, 7.6, 4 yr work-ex) – NEU MEM
  • Nitasha – MS Analytics (310, 7.4, Fresher) – ASU MSBA, Stevens BI, RIT Stats, Rutgers MBS
  • Priyasha – MIS, MEM (710, 0.82, 12 yr work-ex) – MIT SDM, UWashington Foster MSIS, Berkeley MISM, UMCP, Cincinnati, TAMU, UIC, Syracuse
  • Ronak – Analytics (333, 7.6, 6.5 yr work-ex) – Columbia Applied Analytics

Here are Spring/Fall 2016 results (awesome, isnt it? 🙂 )-

  • Akash B – MS ECE (332, 9, 3 yrs work-ex) – Georgia Tech, UC San Diego ($5K scholarship), UMN Twin Cities, NCSU
  • N Saxena – MS/PhD CS (332, 75%, Fresher) – Harvard, Columbia, UC Santa Cruz, Buffalo, Syracuse
  • Souptik S – MS CS (338, 76%, 3 yrs work-ex) – UPenn, U of Wisconsin Professionals MS, UMass Amherst (with RA), CMU INI (with graduate fellowship), NCSU, SUNY SB
  • Shashank R – MS CS Data Science (324, 9.6, 2 yrs work-ex) – Georgia Tech, UCSD, CMU BIDA, USC, U of Washington MSIM, NYU MS Data Science, SUNY SB, NCSU
  • Anirudh R – MIS/Operations Research (334, 7.5, 2 yrs work-ex) – U Berkeley MSOR, Dartmouth MEM, Columbia MS&E, Duke MEM
  • Nikita K – MIS/MEM (324, 9, 1 yr work-ex) – Dartmouth MEM, Columbia MS&E, Duke MEM, TAMU MIS, UMCP MIS, USC MEM, UIUC MSTM
  • Kanagaraj – MS/PhD EE (315, 9, 5 yrs work-ex) – Rutgers PhD, Columbia MSEE
  • Keshav S – MS CS (324, 9, 2 yrs work-ex) – UCSD, UMN Twin Cities, NCSU
  • Ishan M – MS CS (319, 9.4, 4 yrs work-ex) – UCSD, Ohio State, NCSU, ASU Polytechnic
  • Gayathri J – MS ECE (325, 8.7, 3 yrs work-ex) – UMN Twin Cities, UFL, Gatech Shenzhen, Portland State
  • Sharan S – MS Business Analytics (324, 9.2, 5 yrs misc exp) – UIUC – Spring 2016
  • Rafi A – MS CS (78%, 3 yrs work-ex) – Waterloo – Spring 2016
  • Akshata M – MS Business Analytics (323, 8.9, Fresher) – UT Austin, CMU BIC, CMU MISM, IUB Data Science
  • Sheelabhadra D – MS CS (326, 8.4, 1 yr work-ex) – TAMU MSCS, CU Boulder, NCSU ECE, UFL CS, WPI
  • Chakshu M – MIS (700 GMAT, 78%, 6 yrs work-ex) – CMU, UMCP, Cincinnati, TAMU
  • Neha A – MIS (710 GMAT, 3.6, 2 yrs work-ex) – CMU BIDA, UIC MSBA, Connecticut
  • Suvrodeep G – MIS (650 GMAT, 7.6, 5 yrs work-ex) – TAMU, UMCP, SUNY Buffalo
  • Deepika A – MS CS (317, 80%, 3 yrs work-ex) – NCSU, SUNY SB, USC, NEU
  • Anshul G – MEM (325, 8.2, 2 yrs work-ex) – Cornell MEM, Duke MEM, Tufts with $15K schol
  • Sanchit M – MS CS, MEM (318, 7, 2 yrs work-ex) – Duke MEM, NYU
  • Pritesh R – MS CS (320, 64%, 2 yrs work-ex) – U of Utah, NCSU
  • Vinod S – MS Mechanical (332, 7.9, 2 yrs work-ex) – UIC, NCSU
  • Tariq I – MS CS (322, 7.2, 4 yrs work-ex) – ASU, UC Irvine
  • Sathwik N – MS CS (313, 7.7, 2 yrs work-ex) – ASU, NCSU
  • Ravi T – MS Industrial/Financial Eng (311, 7.7, 3 yrs work-ex) – ASU
  • Samantha S – MIS (301, 8.7, 4 yrs work-ex) – CMU MISM
  • Shubham S – MS CS (311, 7.2, 4 yrs work-ex) – NEU
  • Shilpi K – MS CS (312, 8.5, 6 yrs work-ex) – UTD, USF, NEU, SUNY SB PhD
  • Karthik T – MS ECE (318, 9.2, 3 yrs work-ex) – TAMU, USC, UTD, CU Boulder ITP
  • Yasho V – MIS (318, 6.5, 1.5 yrs work-ex) – UMCP, Cincinnati, CMU MISM, USF, UTD, Syracuse, UIC
  • Selina B – MS CS (320, 9.2, 1 yr work-ex) – USC, Waterloo
  • Sanjana W – MS CS (313, 8.3, Fresher) – UC Santa Cruz, ASU, UTD
  • Srikanth M – MS ECE (323, 8 in MS, I yr work-ex) – UC Santa Cruz
  • Piyush P – MS CS (318, 73%, 3 yrs work-ex) – ASU
  • Apurva P – MS CS (325, 69%, 2 yrs work-ex) – NYU
  • Ankita S – MS CS (318, 8.4, 2 yrs work-ex) – ASU, CMU MSISPM
  • Melvin T – MS EE (314, 8.2, 2 yrs work-ex) – Clemson, UTD, Portland State
  • Aashish D – MIS (314, 61%, 3 yrs work-ex) – UMCP MIS, SUNY Buffalo, Syracuse, UIC, USF, UTD, UMCP MIM
  • Shivam S – MS CS (317, 8.2, 2 yrs work-ex) – NCSU, NEU, UTD
  • Nitesh G – MIS/MEM (320, 8.2, Fresher) – Notre Dame, CWRU with 13k scholarship, UTD MIS (instate tuition fee scholarship), NEU MEM
  • Nitesh G – MS Telecommunications (320, 8.2, Fresher) – CU Boulder ITP
  • Sumanth N – MS EE Robotics (303, 79%, 4 yrs work-ex) – UC Santa Cruz, WPI, University of Bonn
  • Aditya V – MS EE Robotics (322, 7.7, 3 yrs work-ex) – WPI
  • Rajdeep K – MIS (310, 60%, 5 yrs work-ex) – UFL (ISOM), UTD (ITM)
  • Siddharth N – MS CS (319, 8.2, 1 yr work-ex) – CMU MSIT – Privacy Engineering, ASU MCS, NEU, TU Munich
  • Manika B – MS CS (312, 7.75, Did ME) – NEU, SJSU, NJIT
  • Aditya K – MIS (321, 6.3, 3 yrs work-ex) – NYU, USF, UTD, Syracuse
  • Mohit V – MS EE Embedded (317, 9.1, Fresher) – SUNY SB, NCSU, UTD, UNCC
  • Suchitra D – MIS (316, 77%, 2 yrs work-ex) – Syracuse, Cincinnati, Buffalo, UNCC Data Science, UIC MIS, UTD MS CS
  • Hari N – MS ECE (314, 7.9, 5 yrs work-ex) – UMCP ENTS
  • Saloni S – MS Data Science (311, 7.1, 1 yr work-ex) – IUB
  • Ranadeep G – MS CS (314, 7.7, 3 yrs work-ex) – Rutgers, NEU MS IT
  • Sanjana E – MIS (308, 8.45, 2 yrs work-ex) – Rutgers, USF, UIC
  • Priyank J – MS CS Cybersecurity (303, 61%, 1 yr work-ex) -RIT, IIT, Drexel, De Paul, George Washington University
  • Aroushi S – MS CS (313, 73%, Fresher) – UTD, Houston Main Campus, U of Glasgow, U of Manchester
  • Hemant P – MS CS (310, 80%, Fresher) – UTD, SUNY SB, NEU
  • Supraba M – MS CS (316, 8.4, 1 yr work-ex) – UTD, IUB Data Science
  • Preethi T – MIS (313, 7.5, 1 yr work-ex) – USF, UTD
  • Ram K – MIS (314, 7.4, 3 yrs work-ex) – Utah, USF
  • Mohit G – MS CS (312, 77%, 1 yr work-ex) – UTD
  • Sajin S – MS ECE (325, 7.3, 1.5 yrs work-ex) – UNCC
  • Rucha K – MS CS (310, 70%, Fresher) – UNCC
  • Tushar A – MS CS (319, 66%, Fresher) – NYU Poly
  • Pallavi K – MS EE (310, 9 in MS, Fresher) – NYU Poly
  • Asha A – MS EE (312, 7.8, Fresher) – U of Rochester ECE, CU Boulder Power Electronics, SUNY Bufallo ECE, UTD Energy Mgmt, Rochester Entrepreneurship Mgmt, IIT
  • Varun C – MS CS (329, 76%, 1 yr work-ex) – ASU IT, RIT
  • Adarsh S – MEM (317, 8.2, 1 yr work-ex) – NYU MoT, NEU MEM
  • Sujay P – MS ECE (305, 75%, 6 yrs work-ex) -Southern Methodist, NEU, Stevens, Pittsburgh
  • Chandrakala J – MIS (305, 64%, 5 yrs work-ex) – USF

And here are the results from Fall 2015-

  • Sadavath S – MS in Business Analytics (325, 80%, 2 yrs work-ex) – UT Austin
  • Akash S – MS in Mechanical (319, 9.55, 1 yr work-ex) – UC San Diego, Columbia, CMU, USC, U of Washington Seattle, ASU
  • Mohnish P – MS in ECE/CyberSecurity (325, 8, 2 yrs work-ex) – NYU Poly, CMU MS IT, John Hopkins, Columbia MS&E, Columbia MS in Computer Engg
  • Arushi A – MS in Data Science (320, 84%, Fresher) – Columbia
  • Arushi A – MS in CS (320, 84%, Fresher) – USC, UC Irvine
  • Hari P – MS in CS (314, 8.5, 2 yrs work-ex) – SUNY Stonybrook, NCSU (MS CN), NYU Poly
  • Karandeep – MS in Civil Engg (337, 8.7, 1 yr work-ex) – UIUC, TAMU, U of Colorado Boulder, Colorado State Univ
  • Navaneeth R – MS in ECE (307, 8.1, 1 yr work-ex) – UMCP ENTS, ASU, UIC
  • Vipul M – MS in CS (315, 65%, 4 yrs work-ex) – CMU BIC, ASU, NEU MS in Information Assurance
  • Vini G – MS in CS (321, 8.98, 3 yrs work-ex) – Cornell MEng, USC (Data Science)
  • Vini G – MISM (321, 8.98, 3 yrs work-ex) – CMU
  • Pragya T – MIS (650 GMAT, 68%, 3 yrs work-ex) – CMU, TAMU, U of Cincinnati, SUNY Buffalo, UIC
  • Pratik C – MS in EE (Robotics/Embedded) – CMU Robotics, Tufts, KTH Sweden
  • Anirudh S – MS in EE (324, 8.61, Fresher) – Ohio State University, NCSU, Virginia Tech non thesis, TU Delft
  • Sushma G – MS in CS (318, 9.5, 1 yr work-ex) – USC, Cornell MEng
  • Nishad S – MS in Embedded/EE (329, 7.98, 3 yrs work-ex) – UNCC, NCSU
  • Vikas S – MS in Mechanical (315, 9.08, 1 yr work-ex) – CMU, USC, U of Washington Seattle, ASU
  • Kevin G – MS in Mechanical (322, 9.1, Fresher) – U of Washington Seattle
  • Kevin G – MEM (322, 9.1, Fresher) – Cornell MEM
  • Indona V – MS in CS (325, 82%, 4 yrs work-ex) – NCSU, TAMU, UC Irvine
  • Anas S – MS in CS (324, 8.0, Fresher) – NCSU
  • Hardik J – MS in Mechanical (313, 68%, 5 yrs work-ex) – TAMU, U of Washington Seattle, UNCC
  • Shantanu K – MIS (317, 65%, 2 yrs work-ex) – U of Cincinnati, UMCP MIM, UIC, USF, UTD (50% tuition waiver)
  • Sahil N – MIS (312, 9.0, 2 yrs work-ex) – CMU, UMCP, NEU, USF, UTD, UIC
  • Neyaz S – MS in CS (331, 7.27, 4 yrs work-ex) – UFL, NYU Poly, Vanderbilt, SUNY Buffalo, IUB
  • Kalyan C – MS in CS (319, 8.3, 4 yrs work-ex) – UFL
  • Pranjal – MS in CS (303, 71%, Fresher) – RIT, U of Delaware
  • Ankita D – MIS (300, 8.92, 2 yrs work-ex) – Syracuse, NEU, IIT Chicago, NYU Poly, Stevens
  • Srishti S – MS in EE/CE (Robotics) (321, 66%, Fresher) – WPI, UNCC, Colorado State, NYU Poly
  • Yash G – MIS (321, 7.37, 2 yrs work-ex) – U of Arizona Eller, UIC
  • Vikrant M – MS in EE () – NYU Poly, U of California SantaCruz, Vanderbilt, SDSU, Utah State Uni
  • Vivek J – MS in Business Analytics (322, 7.24, 2 yrs work-ex) – Drexel with 12K scholarship, Louisiana State U, Waitlisted at University of San Francisco, University of Virginia
  • Deepthi V – MIS (314, 8.5, 2 yrs work-ex) – UMCP, Georgia State University
  • Alok S – MIS (322, 8, 3 yes work-ex) – TAMU, SUNY Buffalo
  • Saumya G – MS Chemical Engineering (314, 75%, Fresher) – Ohio State University, Columbia, ASU
  • Vinayak R – MIS (319, 72%, 2 yrs work-ex) – UIC, Syracuse, UMCP, U of Cincinnati, CMU, U of Arizona Eller
  • Jaskaran K – MEM (320, 8.36, 2 yrs work-ex in Mech) – Case Western Reserve University with 40% scholarship, UIUC MSTM, Duke
  • Srishty P – MIS (316, 78%, 2 yrs work-ex) – UT Dallas
  • Mohnish P – MIS (325, 8, 2 yrs work-ex) – CMU MIS
  • Samiksha R – MIS (294, 58.8%, 1 yr work-ex) – RIT, IIT Chicago, Texas Tech
  • Lokesh A – MS in CS (314, 60%, 2 yrs work-ex) – RIT
  • Ramya – MS in EE (304, 7.36, 1 yr work-ex) – NYU Poly
  • Sanket K – MIS (304, 63%, 2 yrs work-ex) – Stevens, WPI, USF, U of Florida, NYU MoT
  • Prakhar M – MS in Construction Mgmt/Environmental Engg (296,  , Fresher) – Steven, Bradley, IIT Chicago
  • Rohit A – MIS (299, 73%, 3 yrs work-ex) – NJIT, NEU, UNCC

And for all the graduate and to-be-graduate students, your academic life is incomplete until you read all the PhdComics. Here’s one for you-

Case Study – MS in MIS (AG)

Presenting the case study of AG’s successful MIS application journey.

AG graduated from NIT Allahabad with a great GPA of 8.55 and incredible GRE score of 332 (170, 162, 5). He had a Toefl score of 112 and will be finishing 3 years of work experience at Oracle before moving for his MIS program. His professional and academic projects gave him enough credibility for the IS stream. His goal was clear from the beginning to go for MIS and not normal MS in CS. This helped focus on exactly the programs he wanted. Sometimes, students are trying to apply for both and their efforts eventually get diluted. Since his profile was above average, he selectively applied to only the top programs – CMU, TAMU, University of Arizona and IU Kelley. His first interview happened for Kelley but the group case study didn’t go as well as he wanted, turning into a reject. However, he aced all apps after that getting into CMU, TAMU and Arizona.

Some lessons-

  1. Stay focused, understand your goals and apply selectively
  2. Do proper school research, talk to current students and reflect that in your essays