Introducing our ready-to-edit winning resume template for fresher applicants at the bottom of this article! All your headaches solved in one template.
Purpose
Resumes are often taken for granted. While applicants are busy tinkering their SOPs to perfection, everyone thinks resume is a one night job. Well, may be not. After working with some students, I realized that what I thought was obvious about the resume was not so obvious to the students. And fair enough – since I myself had the benefits of years and diverse experiences before I learned the inside out of a resume writing process. So, I figured that it is time to dedicate a full post to resume building.
First, sit and reflect upon who your reader is and what your resume is trying to convey to her. As an applicant, you want your resume to be coherent with rest of your application. So, while your experiences, education and background are factual information, what to highlight is a matter of judgment. For e.g. if you are showing interest in research/applying for PhD etc, your highlight should be on your research projects and publications. Similarly, if you are applying for a MCS kind of program (geared towards landing corporate jobs upon graduation), your focus should be on professional skills, industry experience etc.
So, before you begin, gather your thoughts and decide the areas/skills in your profile that your resume should be highlighting. Continue reading
We are getting lot of interest from MIS and MEM programs’ applicants. Plus, those who are interested in Data Science and Analytics specifically. Let’s look at some of the most commonly asked questions.
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.
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.
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.
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.
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 every year. Contact us if you need help with your MIS/MEM applications.
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.
The 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.
The 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.
The 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.
Combining 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.
Texas A&M is one of the most affordable comprehensive public universities in the nation. The department also offers scholarships and graduate assistantships.
Classes 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.
10 month, STEM designated degree that creates business leaders prepared for disruptive innovations due to emerging technologies, such as deep learning and blockchain.
The Master of Science in Information Technology and Management is a 36-credit curriculum that prepares students to better understand the world of information technology.
The 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 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.
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.
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.
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
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-
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-
Stay focused, understand your goals and apply selectively
Do proper school research, talk to current students and reflect that in your essays
