MS in MIS, MEM, MIM – engineering management programs

ms in mis

More applicants are considering MS in MIS and MEM programs for their Master’s ambitions these days. Let us understand the scope of these programs and whether you should consider them too. The process for applying to these programs is same as MS in USA.

What is MS in MIS about?

MS in MIS stands for Master’s in Management Information System. It is one of the most popular techno-management programs that engineers go for.

Specifically, MS in MIS is an engineering management program that teaches how to use IT to manage huge volumes of business-relevant information to facilitate strategic decision-making. One might think of it as a less competitive version of MBA specializing in IS (but then, less reputed than an MBA too).

Why are techno-management programs getting more popular?

As technology spreads to every tiny aspect of our lives, role of engineering management is becoming more relevant in every domain and industry. Be it pharmaceutical or an agricultural company, everyone is dealing with information. While chemical, mechanical, civil, hardware electronics engineering deal with tangible products, software and computer engineering deal with the abstract information.

Understandably, there has been a constant increase in the demand for managers who understand technology and management aspects alike. This gave rise to programs such as MEM (MS in Engineering Management), MIS (Management Information Systems), MIM (MS in Information Management), MSIS (MS in Information Systems) and so forth.

With few differences, these programs aim at combining technical depth with business breadth so that the students can understand both management and engineering language. One theme that underlines all these programs is that they are core business programs with engineering electives or vide versa. The focus is still on management courses such as statistics, analytics, supply chain etc and one can pick electives in database, programming, networks etc.

What is the curriculum of MIS and MEM programs?

It has a combination of engineering and management electives. Some programs are heavier on engineering courses and some are more focused on management courses. Therefore, it is crucial to study the curriculum before choosing to apply to a program – make sure it fulfills your interest.

Some MIS courses also offers specialization tracks. For example, CMU MISM offers following tracks:

  • Managing AI & Robotics
  • Digital Marketing & Commerce
  • Business Intelligence
  • IT Strategy & Management
  • Health Care Informatics

Duke MEM is more business oriented and offers the following:

  • Customer Experience and Product Design
  • Data Analytics and Machine Learning
  • Operations and Supply Chain Management
  • Product Management
  • Technology Development and Commercialization
  • Entrepreneurship and Founders

Engineering electives can include:

  • Data Mining
  • Artificial Intelligence
  • Networking 
  • Object-Oriented Programming in Java

Business electives/core can include:

  • Marketing
  • Finance
  • Management
  • Operations
  • Modeling

What are the TOP MS in MIS programs?

Here are our handpicked 10 best Management Information Systems courses-

UniversitySpecialtyFee
1. CMU MISMThe three semester, 16-month curriculum can be reduced to one year for those with at least three years of professional work experience. One of the most prestigious and job friendly MIS programs. However, it can be expensive but you are getting a CMU degree.$75,100
for 16 months
2. MIT MSMSThe MSMS degree is 90-unit curriculum, with courses at Sloan, in other MIT departments and Harvard University. Those are two schools to kill for – enough said. $84,134 for 12 months
3. University of California, Berkeley MISMThe 48-unit degree program is intentionally interdisciplinary, combining aspects of computer science, cognitive science, psychology and sociology, economics, business, law, library/information studies, and communications.$71,767 for 12 months
4. New York University MSISCombining computer technology from Courant Institute of Mathematical Sciences and business preparation from Stern School of Business, the MSIS at NYU prepares graduates for successful careers in management positions that require deep technical skills.$68,517 for 12 months
5. Texas A&M University MSISTexas A&M is one of the most affordable comprehensive public universities in the nation. The department also offers scholarships and graduate assistantships.$51,000 for a year
6. Indiana University– Bloomington MSISGraduates of this program are in high demand, with 97 percent of them receiving job offers before graduation.$45,000 for 12 months
7. Arizona State University MSIMClasses for the twelve-month on-campus program are designed to accommodate working professionals. Small class sizes and evening classes allow students to get an intimate, collaborative experience while not having to put their careers on hold.$60,000 for 12 months
8. University of Texas at Austin MSITM10 month, STEM designated degree that creates business leaders prepared for disruptive innovations due to emerging technologies, such as deep learning and blockchain.$55,000 for 12 months
9. University of Texas, Dallas MSITMThe Master of Science in Information Technology and Management is a 36-credit curriculum that prepares students to better understand the world of information technology.$37,000 for 12 months
10. Georgia State University MSISThe MSIS offers concentrations in big data management and analytics, enterprise systems, cybersecurity, information technology, health informatics, and management of information technology.$55,000 for 12 months

What is MEM program?

MEM stands for Master’s in Engineering Management. It is the generic techno-management program with courses in Marketing, Finance, Management, Operations, Modeling and available engineering electives (not just software but industrial engineering, nanotechnology etc).

Duke MEM program
Duke MEM students by backgrounds

Duke’s MEM program is leading in this category. Look at their electives to get a sense of the course offering. Above chart shows how the incoming student profile is distributed. As you can see, it is widely spread among various engineering majors.

Some other engineering or techno-management programs go under the names of MIM, MSIS, MISM etc.

What kind of jobs can you land after MIS or MEM programs?

MIS – Expect roles such as IT consultants, data analytics engineer, system analysts, data analysts, systems engineer, database administrators.

MEM prepare candidates for consulting, business analysis in any engineering related work function.

Also read FAQ for MIS/MEM programs

If you found this useful, there is lot more information in MS Book: Smart Engineer’s Complete Guide to MIS in USA

Our students are joining MIS/MEM/MS Business Analytics/MS Data Science programs at Columbia, UT Austin, CMU, TAMU, Duke, Syracuse, Buffalo, NEU, UIC etc every year. Contact us if you need help with your MIS/MEM applications.

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.

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