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-

FAQs for MIS/MEM applicants

We are getting lot of interest from MIS and MEM applicants.Plus, those who are interested in Data Science and Analytics specifically. Let’s look at some of the most commonly asked questions. See our previous post for details on these programs and their job prospects.

1. Should I go for MIS only if my profile is not good enough for MS in Computer Science (and other branches respectively for MEM)?
This is a misconception that MIS stands second to core engineering MS programs. I have known students with excellent profiles and a shot at top MS in CS programs to opt for MIS because of their career goals. It is important to understand the difference in career paths that stem from MS CS vs MIS. You should see which program aligns better with your career goals and not choose based upon its perceived reputation in your head.

2. If I want to do MBA later on, then should I still go for MIS/MEM?
Many MIS/MEM programs are offered by business schools (and some by engineering schools). As a result, you might be taking some courses along with the MBA students. I recommend doing MBA much later in career for either switching your career stream or to get a jump in career ladder within your industry. Doing a MIS/MEM (granted that it fits in with your aspirations now) will not rule out an option for MBA later on. You might be studying some of the courses again but you can still do it if you feel the need to do so.

3. Are the job prospects after MIS/MEM worse that MS or MBA?
Please understand that all these are different programs and hence comparing them is not ideal. MS and MBA have been there for a long time and have established reputation whereas MIS/MEM programs are comparatively newer and still building their base. And this is the reason that they are growing steadily in demand as well. Would you rather do MBA when fewer people were doing it and there was a higher demand or when the market is saturated and practically everyone has an MBA? Top MIS/MEM programs such as Duke, CMU, Stanford etc are highly competitive and graduating from them is highly rewarding in terms of career opportunities. So, I feel that if you graduate from a good MIS/MEM program, you will not be compromising on any job prospects compared to other fields. Currently, there is an employment boom for engineers (especially CS related) which may change later on. Therefore, job prospects after these programs depends on the industry demands and not the reputation of these programs alone. As is true for MS or MBA, doing MIS/MEM from higher ranked schools should open lucrative opportunities for anyone.

4. How can I make my MIS/MEM application stronger?
Following things can help especially for MIS/MEM-

  • Having at least a year of fulltime work experience. This is because management programs benefit from exposure to industry and students can better contribute to the classes if they have worked previously. Also, they are able to better understand some concepts that are applicable in real world jobs
  • Get at least one LOR from the Industry. This can come from your Manager if you are employed or a project guide if you are an engineering student and did some industry project/internship.

5. So, should I apply for MIS or not?
If you want to get into IT consulting, Analytics, Project Management, Product Management etc kind of careers, then and ONLY then you should opt for MIS and NOT because it is less competitive or easier entry for US.


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 this Fall. To get our application guidance for Fall2016, please check out our testimonials and packages.

More information on these and some case studies are covered in MS Book: Smart Engineer’s Complete Guide to MS in USA.

Starting salaries for engineers in USA

I am working on a MS related reference book and while exploring the most common reasons why engineers pursue graduate studies, the answer came out to be this.

The most lucrative thing about studying in USA is the ability to work afterwards without much difficulty. If you study from a Top 50 school, you have a pretty good chance of landing a job. But many aspirants wonder how much? And if it justifies the higher cost of study.

So, I would like to share this data on average starting salaries for engineering students taken from Michigan Tech College of Engineering –

startingslaaries

 

As per this: “Year after year, engineering tops the list of majors with the highest average starting salary. The bottom line:  It is well worth the time and effort it takes to become an engineer.

What’s more? I talked to some professionals who recruit engineers for their companies and when I asked hows the recruitment scenario, I got a unanimous reply – a resounding ‘it is a good time for engineers as lot of career opportunities are floating around and we have more positions than qualified candidates’.

So, if you are on fence about whether to join a graduate school in US or not, this is your answer. The market is back in shape (at least for engineers) and it was never more lucrative to earn in dollars.


Looking for expert COUNSELING help in last minute? Check out our cost effective packages and subscribe to our newsletter!

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