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Tag: strategy

Education

Masters in USA Beyond branded Universities

There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don’t look even slightly believable. As students across the globe continue to see their learning plans significantly.

The Craze for Branded Universities-

Most of the students aiming for a great master’s education want to get into one of those “Branded” universities everyone is talking about. I’m not just talking about the Harvards and Stanfords of US but even the next tier of Universities such as University of California, LA or SD or North Carolina, Chapel Hill or TAMU, college station etc.. Remember, even these universities have a huge number of applicants from all around the world, especially from the Subcontinent, China, Korea etc.. . Getting into such Universities of course takes a great profile with good GPAs, GREs etc.. An added hurdle is the sheer amount of competition the applicants face.

For example, even after guiding multiple students over the years, for us it’s still quite hard to tell if a student with a great profile can get into CS program of North Carolina, Chapel Hill or not. It really depends on how competitive the Pool of applicants is.

Moving Beyond…

What I can safely claim is that there are Universities which are almost on an equal footing as the “Popular” ones in terms of quality of education, but have somehow missed the limelight. To be honest, a few reasons for them not being that popular could be High tuition fees, Location, limited intake etc.. However, one secret advantage these Universities enjoy is the “Relatively low volume of applicants”. This very situation can increase one’s chances of admit into a great graduate course. Note that some courses in such universities might be ranked higher than the popular ones.

Also, one’s chance of getting on campus jobs or Assistantship increases owing to less number of students in the program, which in turn decrease your expenses.

Branded V/S Not-Branded-

For many students, the “Craze for Brand” is not just the driving factor to aim for such universities, but the opportunities that are created in terms of Job. I cannot deny that. Graduating from a “Maryland, college park” or “Columbia” definitely turns heads. The competition is crushing though.

On the other hand, graduating from a good Master’s course offered by these Not-branded Universities can also positively impact one’s resume, because employers usually look not only at the “brands” but also the content in an applicant.

In my opinion, if you are looking for an enriching education experience, do not limit yourself by just applying for brands. You should also give a shot to the other set of Universities.

QS rankings for some Popular Universities-

University of California, Los Angeles – 35

University of Illinois, Urbana Champagne – 75

University of North Carolina, Chapel Hill- 90

University of Southern California- 129

Texas A&M, College Station – 189

University of Illinois, Chicago- 231

UMass, Amherst- 305

Northeastern University- 344

Stony Brook University, SUNY – 359

University of Texas, Dallas – 501-510

University of Cincinnati – 561-570

QS Rankings of some “Not so popular” Universities

Northwestern University 31

Rice University 85

Boston University 98

Case Western Reserve 167

Vanderbilt 200

Dartmouth College- 207

Georgetown University- 226

University of Notre Dame- 241

Tufts University 253

LeHigh University 551-560

Drexel University 561-571

College of William and Mary – 600+

 

Of course some might not agree with the Universities in the “Not so Popular” list, but we have observed fewer students applying to them (from India).

I also tried to include popular universities from different “Tiers” keeping all sorts of profiles in mind.

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Education

IELTS-TOEFL-or-PTE-what-to-prefer

Everyone who is planning to study or immigrate abroad faces this dilemma. I have put together some information that could help you make a better choice about which test to take. 

 Before looking into what’s the best option for you, please remember that all these tests measure your English Reading, Writing, Listening, and Speaking abilities but on different scales. Obviously, the exam patterns are different from each other. However, Universities have a certain minimum requirement for each of these tests. For example, one university in the US has minimum requirements as – TOEFL iBT 79+/120 , IELTS Academic 6.5+/9, Duo Lingo 105+/160, and  PTE Academic 58+/90. 

 Also Please note that IELTS and PTE tests have two different categories called Academic and General. You choose the Academic version if you’re applying to universities/colleges and the General version for Permanent Residency or Work permit in some countries. 

Universities in different countries accept a slightly different set of the above tests so let me breakdown country-wise.  

1.USA

     For Universities –

TOEFL iBT- Accepted by all Universities. 

IELTS Academic – Accepted by all Universities.

PTE Academic –  Not all Universities accept PTE. 

Duo Lingo- As of 2020-2021, which is the current year, this test has just started being accepted by a few popular Universities but not all of them accept these scores. However, more number of universities might pick it up in the near future. Please check with the universities you’re applying to before taking the test. 

For Immigration/PR- 

No Proficiency test is required for getting H1B or Green Card. 

2.Canada

For Universities/Colleges-

All universities accept TOEFL iBT, IELTS Academic, and PTE Academic.

For Immigration- 

If you’re applying for PR permit, Canadian immigration department asks you to submit English proficiency proof and IELTS General is the most popular pick. TOEFL , PTE and Duo Lingo are not accepted as proof here. 

If you’re planning to apply for universities or PR , it’s best to choose IELTS over others. Of course, you need to write IELTS Academic in case of study permit and General in case of PR permit. 

3.UK

For Universities/Colleges-

Similar to US, all the universities are accepting TOEFL iBT, IELTS Academic, and PTE Academic. A few Universities started to accept Duo Lingo scores as well. 

For Immigration- 

No requirement of English proficiency for work visa or Permanent Residency permit. 

4.Australia and New Zealand

 For Universities-

All Australian and New Zealand universities accept IELTS Academic, TOEFL iBT, and PTE Academic. You can choose either of them. 

For immigration-

Unlike Canada, here all the three tests are accepted – TOEFL iBT, IELTS General, and

PTE General. 

     5.European and other Countries- Most universities accept both TOEFL iBT and IELTS.

Final verdict

 In our training institute and the University guidance department, we see a lot of students choosing IELTS over other tests. It’s the most popular choice because of its wide acceptance by both Universities and countries across the world. 

 Also, please note that there are other not so popular tests such as the TOEIC, Cambridge etc.. which are accepted by a few universities and countries (UK, Canada etc..). Since the tests mentioned in the article are the most popular picks among students, I have addressed them. 

 In case you are still confused or you’re looking for something apart from the IELTS, I suggest you go through test patterns of the other tests and attempt a few questions in sample tests available online. See for yourself which pattern seems most comfortable for you. Of course, don’t forget to check with the Universities (you’ll be applying to) as well to see which of the tests they’re accepting. The details along with minimum requirements are available on University websites. 

 Any questions? Please write to me at jayasury@drrajus.com

 

Jayasurya Pathapati

MSc. Entrepreneurship, Brown University ’17, USA. 

Linkedin- https://www.linkedin.com/in/jayasurya-pathapati-1348647

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EducationLearning

Why Pursue a Master’s Degree?

Why should you get a Master’s degree?

Is it for better Money? 

Is it because you want to upgrade your skills?

Is it because you think a Master’s degree is a one stop solution for all your professional and personal desires?

If your answer is amongst the reasons mentioned above, I am sorry, in the long run you will be disappointed. If you want to earn a decent salary you ought to have skills. For developing skills you don’t have to go to university, you’re better off doing bootcamps or online courses. Your Return on Investment will be great and you can save a lot of time. Average shelf life of IT-skills is three years. So, upgrading is no longer an option. You are up or out.

So What is Master’s degree for?

1.To develop niche-skills: Let me give a specific example. If you are an electronics engineer, I hope you will relate to this. In order to design a normal Power amplifier all you have to use is the knowledge gained in the second year of your degree. Quite frankly, someone with a decent understanding of circuits can design it without a degree. But, if you want to design the same amplifier with restrictions on parameters such as noise ratio, high unity gain bandwidth, low input resistance, then maybe you will require some more knowledge. This too can be done without much assistance. Let me up the game a little. Now, if you want to design an Amplifier at nanoscale (used in computers) with hundreds of technical parameters, do you think you can do it without simulators and guidance from your professors? I guess the answer will be a no for many. Now, that’s where a Master’s degree comes in. For niche fields such as Quantum computing, Nanotechnology,ASIC design, Computer Vision etc., masters is not an option, it’s mandatory.

2. For guidance: Those of you who are thinking that Einstein has come up with the Theory of Special relativity on his own, wake up. He had phenomenal insights but insights won’t transform themselves into equations, you need guidance. Albert Einstein got the best education of his day and built upon work done by Graduate school professors. In fact he was a faculty member at Princeton University.

3.To observe and exploit patterns: Do you know that many of the best engineers and physicists who get the top dollar, work in hedge funds? What do you think is common amongst them? Math. Now just doing a master’s degree doesn’t give you a good understanding of Finance. What it does is give you is better tools(than that you used in your bachelors) to gauge odds.

4. To network with peers : When it comes to Management education, it is no secret that professionals do an MBA for networking, not curriculum. The advantages of networking are too obvious to discuss here. Also, the more diverse, distinct and talented the peer group is the better. Hence, the competition for top MBAs is insanely high.

5. To change domains: After four years of engineering if you realised that it is not for you, no worries. Master’s degree is the best way to break into the field you want. We have seen core-engineering students opt for fields such as – Operations Research, Quantitative Finance, Engineering Management, Data Analytics etc.. which all have great scope for employability.

6. Career growth – With a master’s degree you’ll have a kickstart in your career. For example, with a Master’s in Finance degree, you can start as an associate instead of starting as an analyst(In Investment Banking roles) or post your Master’s in Computer Science, you will be qualified to work for FAANG and many other such reputed IT firms .

7. Research – If you want to break into academia, you’ll have to publish high index research papers. Your index depends on the quality of your research. Mostly, bachelor’s degree is about testing different waters, but in master’s you actually get to immerse yourself completely in a stream. Generally, universities in the USA, Europe, UK, Canada etc.. receive a high amount of funding from Government and MNCs for research. This will enable you to be part of cutting edge research in advanced labs, where you can collaborate with your Graduate school faculty and colleagues from different streams. Remember, almost all Noble laureates have a Master’s degree and have done their research in Universities.

8. International Immersion– At graduate school, you will be meeting people from different cultural backgrounds and diverse academic fields. You also get to explore a very different environment and study curriculum, which will help you broaden your perspectives. 

Conclusion

In conclusion, a Master’s degree is for you if you want to acquire advanced skills and/or want to dig deeper into the field you’re passionate about. It’s not just another degree to have on your checklist but it will be a very influential step in your career, if you make the right use of it.

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Education

Csc Specializations for Masters

In order to write the perfect blog post, you need to break your content up into paragraphs. While most blog posts use paragraphs, few use them well. Take the time to put links in your blog post…

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EducationLearning

ECE and EEE Specializations

Specializations for Electronics and Communications Engineering and Electronics and Electrical Engineering students.

 

In many countries the Master’s degree in ECE/EEE is offered under the names of Ms in Computer Engineering (Ms in CE) and Ms in Electrical Engineering (Ms in EE). These streams are two pretty common choices among Engineering students, second to only Computer Science. Although CSc grads get the highest pay among engineering grads, Electrical Engineers are not very far behind, as the industries in this segment are booming internationally and domestically. Since more and more Electrical students are directly or indirectly opting for jobs in CSc, there will be shortage and consequently more demand for Highly skilled electrical Engineers across the world. 

 

Here are some specializations you can explore in this field–



1. VLSI and circuit design:

Description: Thanks to VLSI engineers, your laptop is functioning as efficiently as a room sized computer used to. VLSI is basically packing a lot(millions) of switches in a chip. Common Roles: ASIC frontend designer,FPGA frontend designer,Library developer

 

2. Nanotechnology:

Description: Now, if you want to place more chips(billions) in a single chip, do you think merely decreasing the channel length of the transistor will do the job? Nope. Scaling doesn’t work like that in electronics. Many nonlinear effects arise. In order to understand the effects and come up with new designs, one needs to have a good understanding of underlying physics. That’s where Nanotechnology comes in.

Common Roles: Materials Engineer, applied research positions in tech giants

 

3.Optics and Photonics:

Description: What if computers can perform calculations at a fraction of light speed? Am I sounding futuristic? Please check out Optical computing. Take a look at the router that you are using. Did you observe a thin cable connecting it and a port nearby? Well that’s an optical fibre. Applications of Laser and Photonics are vast. This field is probably the most exciting interplay between Physics and Electronics.

Common Roles: Photonics engineer, Silicon photonics, Bio-Photonics

 

4. Bio Electronics:

Description: Few weeks back, Neuralink released a video showing a chimpanzee playing video games. Mind boggling, isn’t it?! We were taught that behind every action there is an impulse sent by the brain. Artificial limbs are applications of bioelectronics.

Common Roles:  R&D labs of electronic equipment designers

 

5. Networks:

Description: This specialization focuses on making systems interact with each other. One will learn how to manage bandwidth, traffic, and the security of networks , as well as any devices connected to the network.

Common Roles: Network Administrator, Network Analyst, Network Architect.

 

6. Signals and Image Processing:

Description: Ever wondered why there are many image formats such as jpeg,jpg and tiff. Ever thought how we are able to compress huge files(from GB to MB) ? Well, it all comes down to compression techniques. How is it related to Signal processing, you ask? Image and text are nothing but signals. Deep Learning is extensively used in this domain.

Common Roles: Cryptography, Image processing engineer, Deep Learning Engineer

 

7. Radio Frequency engineering:

Description: Why doesn’t your normal speaker work for satellite broadcasting. Well, frequency is the answer. The bandwidth of the human voice and that of radiofrequency waves are different. At higher frequency even a small wounded wire in your circuit may act as an inductor. In RF engineering you will learn how to design equipment for capturing, amplifying and processing electromagnetic waves(30HZ to 300 GHZ)

Common Roles: RF engineer, Microwave engineer

 

8. Telecommunications:

Description: Just like a music director innovates with different instruments and modulations, a Telecom Engineer comes up with different ways of transmitting a signal from A to B with minimal information loss. For doing so ,he /she comes up with protocols such as GSM,3G,4G,5G  and techniques for packing and transmitting signals such as CDMA,FDMA,etc. 

Common roles: Telecom Engineer, Protocol testing engineer

 

There are many other specializations for ECE and EEE students such as Power & Energy systems,  Robotics, Computer Vision, Bio Engineering etc.. which all have great real world applications and hence many job opportunities around the world. 

 

Expert Tip– If you want to get jobs right after graduation, pick universities in areas/countries where there are good electronics companies. In the USA, apart from popular states such as California, Texas etc there are other states such as North Carolina, New Mexico and a few more, where job opportunities for Electronics students are plenty. The State Universities here also offer good funding opportunities. After Master’s, many students interested in core Electronics pursue PhD (which is just another two years), in order to find high paying jobs in the R & D division of top electronics companies. 

Interested to shift to another field? 

Many ECE students  transfer to the IT sector by applying for MS in CE or MS in CS. EEE students usually take up MS in CE (not direct CSc) and then take elective courses related to CSc and apply for jobs in the field. Students also pick courses such as  Ms in Data Science, Business Analytics, Engineering Management, Operations research, Ms in Quantitative Finance etc.. to find good job opportunities outside the field. 

 

To Apply for MS in CE/EE or any other specialized master’s courses, get in touch with our experts to discuss the best streams for you. You can write to our student advisor, Jayasurya at jayasurya@drajus.com or schedule a call with us by submitting your contact details here

 
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Education

MS in Chemical Engineering

Here are some Core areas in which Chemical Engineering Students can choose to build their expertise.

 

Materials Processing: It is the study of behavior of various materials and how they can be made more efficient and safe. Materials Processing Engineers come up with new ways of separating and transporting chemicals including hazardous materials,oils or gas. Knowing the functionality of different materials and their behavior under a wide range of conditions is vital to succeed in this domain.

 

Materials Synthesis: PVC pipes which you see everyday are created by Materials synthesis engineers.this specialisation is very vast, ranging from nanoparticles to biomaterials. Material Synthesis engineers have to come up with new ways to optimise existing processes to reduce costs.

 

Environmental Engineering: It involves using chemicals and their components to improve or sustain the natural environment. Environmental engineers come up with new ways to reduce pollution by reusing the same chemicals that may otherwise cause pollution. “Sustainability” is the name of the game.

 

Alternative Energy: Sustainability and Alternative Energy go hand in hand.This domain looks at biofuels,batteries and Solar and Wind energy. Those who specialise in this field will always try and look for replacing an existing Energy generation source(mostly environmentally harmful ones) with sources that are relatively more Environment friendly.

 

Biomedical Chemical Engineering: There is a considerable overlap between Biomedical Science and Chemical Engineering fields. The study of how a biological organism responds to a stimulus by a chemical is a part of this domain. Synthesising artificial tissues is also something they do.

 

Food Engineering: Improving food preservation and making it more healthier using chemicals is the main focus of Food Engineers. By estimates, the entire human population will reach eight Billions. Now imagine how important it is to increase yield.

 

Engineering Design: Every lab uses simulations before going for a hardware setup or before conducting any experiment. Now, computers do not understand the subtleties involved in simulations of different domains. The parameters that a Chemical Engineer optimises for, will obviously be different from the parameters an Electronics engineer optimises. This is why a domain expert collaborates with software engineers to create tools. From creating tools such as CAD to designing test processes and decreasing complexity of work, their scope is wide.

 

Oil and Gas: From digging for oil using manual labor to detecting oil using seismographs, we have come a long way. Now, tech giants are using AI to detect oil spills.Algorithms don’t work by themselves. It takes subject matter experts to tweak the algorithm according to a scenario. 

 

NanoTechnology and Nuclear Engineering are also parts of Chemical Engineering.

 

It’s common to see a lot of Master’s in Chemical Engineering students pursue PhD, in order to gain advanced experience in lab-work and knowledge in Research. This makes them much more prepared for “scientist roles” in Industries, Government, or Academia. Students who are solely interested in working in industries can apply to Professional Master’s Programs in Chemical Engineering, which prepares students for the job market. The PMP program is offered by a limited set of universities.

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EducationLearning

MS Data Science and Best Universities in USA

“Be job-ready in 6 Months by learning Data Science from us”, claim few institutes.

“Data Scientist: The Sexiest Job of the 21st Century”, Harvard Business Review.

The term “Data Science” is probably the highest used by education institutes in the past decade. It is easy to get lost in the buzz surrounding it. So, without further delay, let’s cut the clutter.

What exactly is data science?

It’s all in the name. Data simply means a collection of facts and statistics together for analysis. It is raw and unorganized. Using Statistics, Programming, and Industry experience on collected data to gain insights into a business problem or an academic problem is called Data Science.

Data + Programming + Statistics + Industrial/Academic Acumen  = Insights

For example, if a paint manufacturing company decides to open a new plant, it will start gauging demand for its product. To do that, they will collect Petabytes of data on the target population’s demographics. Features such as income level, age, profession, etc. will be collected. It is followed by processing the data and using algorithms to extract information out of it. All this is done by a Data Analyst’s knowledge of Feature collection and algorithm application combined with Industry experience. In the corporate, there are many professionals like, Product Analysts, Marketing Analysts, Supply Chain Analysts, Quantitative Analysts, etc. who are all Data Analysts with experience and knowledge in their respective sectors.

In an Academic setting, Data Science is being heavily used for Research in Science and Medicine to analyse huge sets of data and get deeper insights- for example, currently volumes of data on the COVID19 victims is being analysed to find out potential ways to curb the disease.

The above examples clearly show how vital data science expertise is, in today’s world and the demand for Data Science experts across all sectors.

How is the demand for data science professionals in the US in the near future?

Many Industry experts say that the supply of Data Science professionals is low and demand is massive and is only going to increase in the next decade or so. A simple search will show you how rapidly this demand is growing. Please refer to the article: Is Data Science Still a Rising Career in 2021.

How to get into the field?

Pursuing a Master’s in Data Science is the most direct way to gain the required expertise and qualifications to be a Data Science professional. Many MS Data Science graduates are getting hired by MNCs and High Growth Start-ups within a few months of graduating from the Master’s program.

What’s unique about this degree is that anyone with a STEM background can apply for it, even without programming skills or IT experience. However, a good grip on certain concepts like Probability, Statistics, Data Interpretation, Calculus, Linear Algebra, etc.. is required to do well in this field. This means all Engineering, Sciences, Mathematics, etc. undergraduates who have covered Maths Courses in their bachelors are all eligible to apply for MS in Data Science.

So What does MS in Data Science cover?

  • Probability and Statistics
  • Machine Learning
  • Deep Learning
  • Big Data
  • Linear Algebra
  • Econometrics
  • Programming

What job positions do they get after studying MS in Data Science?

Data Analyst (Varies depending on the industry), Data Scientist, Business Analyst, Marketing Analyst, Financial Analyst, Market Research Analyst, Product Manager etc.

Some top MS in Data Science Programs

There are 110+ Universities offering Masters in Data Science or related degrees, which is massive compared to any other country in the world. Owing to the USA’s major corporations across sectors such as IT, Finance, Manufacturing, Electronics, Consumer Goods, etc., Data Science graduates get hired instantly in any of these sectors. These above reasons make USA the best country to pursue an MS in Data Science.

Here are some Top Programs-

UNIVERSITY LIST FOR DATA SCIENCE AND DATA ANALYTICS:

Stanford University: MS in Statistics (Data Science)

 Minimum Requirements: Admission Criteria is Holistic

  •  GPA: Average 3.5 (around 80-90%) but no minimum requirement. Undergraduate Institute’s repetition will play an important role
  • GRE: Average: (v: 97%-165; Q: 97%-170; AWA: 5.0)
  • TOEFL: 100
  • Work Experience: Some relevant work experience (or Internship) in a relevant field is an added advantage
  • Prerequisites: A strong mathematics background and advanced undergraduate level courses in linear algebra and probability, and introductory courses in stochastic processes, numerical methods and proficiency in programming (Basic usage of the Python and C/C++ programming languages)
  • Research Experience: Not mandatory but Stanford looks for this area as a competitive component
  • SOP
  • Recommendation Letters
  • Resume
    • Tuition Fees: 1, 10,000 USD for the program (Living expenses extra)
    • Course Duration: 15-18 months (45 credits-5 quarters)
    • Application Deadline: Autumn: Dec 1st
    • Concentration Areas: MS in Statistics (Data Science area)
    • Course Work:  Statistical Interference, Regression Models and Analysis of variance, Applied statistics, Numerical linear algebra, Stochastic methods in engineering,  Artificial Intelligence, Deep Learning, Data Mining, Convolution neural networks for visual recognition, Natural language processing, Scientific Computing, large scale computing, Applied machine learning, Capstone project and Practicum in Data Science stream

Harvard University (MA):  Masters in Data Science

 

Minimum Requirements:

A.     GPA: 3.5 (around 80-90%) . no minimum GPA…admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.      GRE is not at all required and not be submitted at any cost

C.      IELTS: 6.5 TOEFL: 80

D.     Work Experience: Not mandatory but distinctive professional accomplishment in the relevant area. 

E.      Research Experience: Not mandatory but Harvard looks for undergraduate research as a competitive component

F.      Prerequisites: Successful applicants do need to have sufficient background in Computer Science, Math, and Statistics – including fluency in at least one programming language like R or Python and knowledge of calculus, linear algebra, and statistical inference. Research Experience: not mandatory

G.     Personal Statement -SOP

H.     3-Recommendation Letters

I.       Professional Resume

 

1.   Tuition Fees: 90,000 USD for program (Living expenses additional)

2.   Course Duration: 18-24 Months (Full Time)

3.   Application Deadline: Fall Deadline: December 15th  

4.   Concentration Areas: Nothing in Particular  

5.   Course Work:  Introduction to data science, Advanced topics in data science, Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference, and Optimization, Systems Development for Computational Science, Critical Thinking in Data Science, Machine learning, Artificial intelligence, Data Systems, Visualization among many others. Research project in data science and a capstone project in data science are mandatory.

II Program : Harvard University, Chan School of Public Health, M.S. in Health Data Science

https://www.hsph.harvard.edu/health-data-science/

 

Yale University: MA in Statistics & Data Science

Minimum Requirements: Admission Criteria is Holistic

1. GPA: Average 3.5 (around 80-90%) but no minimum requirement. Undergraduate Institute’s repetition will play an important role

2. GRE: Not mandatory but may be submitted ; No minimum requirement but above 90% ..and GRE Subject in Maths is also an optional one.

3. TOEFL: 100: IELTS: 7.5

4. Work Experience: Not mandatory but extraordinary professional achievement (or Internship) in relevant field is an added advantage

5. Prerequisites: A strong mathematics background and advanced undergraduate level courses in linear algebra and probability, and introductory courses in stochastic processes, numerical methods and proficiency in programming (Basic usage of the Python and C/C++ programming languages)

6. Research Experience: Not mandatory but Yale university looks for this area as a competitive component

7. SOP

8. Recommendation Letters

9. Resume

1.      Tuition Fees: 87,000 USD for program (Living expenses extra)

2.      Course Duration: 24 months

3.      Application Deadline: Fall : Dec 15th t 

4.      Concentration Areas: MS in Statistics (Data Science area)

5.      Course Work:  Probability and Statistics, Multivariate Statistics, Applied Data Mining and Machine Learning, Deep Learning Theory and Applications, data Analysis, Optimization Techniques, Machine Learning, Deep Learning Theory and Applications, computational Tools for Data Science, Parallel Programming Techniques, Building Distributed Systems, Object-Oriented Programming, Statistical Computing, Computational Statistics, Internship among many other courses.

University of Pennsylvania (Philadelphia, PA):  MSE in Data Science

Minimum Requirements:

A. GPA: 3.5 (around 80-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.   GRE (no min. requirement  but very high scores are preferred to the tune of 325-330: 158V, 167:Q, AWA:4)

C.   IELTS: 7.5. TOEFL: 100

D. Work Experience: It is not mandatory for the applicants to have relevant work experience. However, they look for practical experience with Data Science, either through project work in a course or job/internship. Special emphasis is placed on there being a fit between candidate’s interests and the Data Science Program.

E.   Prerequisites: The MSE in Data Science targets students who have strong mathematical and statistical proficiency, and some programming experience.

F.   Research Experience: not mandatory

G. SOP

H. Recommendation Letters

I.    Professional Resume

 

1.      Tuition Fees: 80,000 USD for program (Living expenses extra)

2.      Course Duration: 18-24 months (10 courses)

3.      Application Deadline: Fall Deadline: Nov 15th ( Priority), March 15th final deadline

4.      Concentration Areas: 1. Network Science. 2. Digital Humanities.3. Public Policy.4. Computer & Information Science.5. Electrical & Systems Engineering.6. Scientific Computing

5.      Course Work:  Machine learning, Big Data Analytics, Statistics   and several elective and in-depth courses can be taken from above concentration areas of specializations.

University of California, Berkeley: Master of Engineering in Data Science and Systems

Minimum Requirements:

A.   GPA: 3.5 (average: around 80-95%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.   GRE (Average 90% quant, 70% Verbal and AWA >3.5)

C.   TOEFL: 100. IELTS: 7.0

D. Work Experience: Not mandatory but will be an added advantage to secure admission. Internship experience and other relevant certifications are also considered.

E.   Prerequisites: Experience in programming, algorithms, data structures, and theory at or above the undergraduate level.

F.   Research Experience: not mandatory but will be an added advantage

G. SOP

H. Recommendation Letters

I.        Professional Resume

 

1.   Tuition Fees: 61,000 USD for program (Living expenses extra)

2.   Course Duration: One year program

3.   Application Deadline: Fall Deadline: Jan 6th

4.   Concentration Areas: Nothing in Particular

5.   Course Work:  Machine learning, Optimization models in engineering, User interface models design and development, Convex optimization and Approximation, Parallel Computing among several courses. Many Capstone projects in Data Science are offered to complete the degree.

Carnegie Mellon University (Pittsburgh, PA):  MS in Computational Data Science (MCDS)

Minimum Requirements:

A.      GPA: 3.0 (around 75-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.      GRE : No minimum criteria but high GRE scores are recommended to the tune of around 325-330 (Average 154-160 V, 168-170 Q, AWA: 3 to 4)

C.      TOEFL: 100.  IELTS: 7.0

D.     Work Experience: Not mandatory but relevant experience in data science will be an added advantage to secure admission.

E.      Prerequisites: Experience in programming, algorithms, data structures, and theory at or above the undergraduate level.

F.      Research Experience: not mandatory

G.     SOP

H.     3-Recommendation Letters

I.        Professional Resume

 

1.      Tuition Fees: 87,000 USD for the program (Living expenses extra)

2.      Course Duration: 18 Months (3 semesters and summer internship)

3.      Application Deadline: Fall Deadline: Nov 19th(I round), Dec 10th ( II round)

4.      Concentration Areas: 1. Systems.2. Analytics.3. human-centered Data Science

5.      Course Work:  Machine learning, Cloud Computing, Interactive Data Science, DataBase systems, Advanced Cloud computing, Computer networks, Distributed Systems, Statistical Machine Learning, Convex Optimization, Machine Learning for Big Data, Machine Learning for text mining, Machine learning for signal processing, deep learning, conversational machine learning, deep learning, design and engineering of intelligent information systems, Neural Networks for NLP among many other courses in the above concentration or speciality areas. Internship is mandatory.

University of Michigan, Ann Harbour  (Michigan):   Data Science MS  

Minimum Requirements: Admission Criteria is holistic

A.      GPA: 3.0 (around 75-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.      GRE: No minimum criteria but high GRE scores are recommended to the tune of around 325-330 . For Fall 2022 GRE scores are waived.

C.      TOEFL: 84.  IELTS: 6.5

D.     Work Experience: Not mandatory but relevant experience in data science will be an added advantage to secure admission.

E.      Prerequisites: While a Data Science undergraduate major is not required, it is expected that applicants will have at least the following background before they join: 2 semesters of college calculus, 1 semester of linear or matrix algebra, and 1 introduction to computing course.

F.      Research Experience: not mandatory

G.     SOP and Personal Statement

H.     3-Recommendation Letters

I.        Professional Resume

 

1.      Tuition Fees: 85,000 USD for program (Living expenses extra)

2.      Course Duration: 24 Months

3.      Application Deadline: Fall Deadline: Jan 4th  

4.      Concentration Areas: Nothing in particular but there are several electives from different fields.

5.      Course Work:  Introduction to Discrete Mathematics, Programming for Scientists and Engineers, Data Structures for Scientists and Engineers, Statistical Inference, Database Management Systems, Advanced Database Systems , Data Mining and Statistical Learning,, Statistical Learning II: Multivariate , Machine Learning, Advanced Data Mining, Applied Machine Learning, Machine Learning for Health Sciences, Big data analytics, modern statistics among several courses

Columbia University, Manhattan (NY):   MS Data Science

Minimum Requirements: Admission Criteria is holistic

1.      GPA: 3.0 (around 75-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

2.      GRE: No minimum criteria but high GRE scores are recommended to the tune of around 320-330 .

3.      TOEFL: 100.  IELTS: 7.0

4.      Work Experience: Not mandatory but relevant experience in data science will be an added advantage to secure admission.

5.      Prerequisites: Prior quantitative coursework (calculus, linear algebra, etc.). Prior introductory computer programming coursework

6.      Research Experience: not mandatory but if you have publications one can upload.

7.      SOP and Video Interview

8.      3-Recommendation Letters

9.      Professional Resume

 

1.      Tuition Fees: 80,000 USD for program (Living expenses extra)

2.      Course Duration: 18 Months

3.      Application Deadline: Fall Deadline: Jan 15th   

4.      Concentration Areas: Nothing in particular but there are several electives from different fields such as statistics, computer science and operation research

5.      Course Work:  Computer Systems for Data Science, Machine Learning for Data Science, Algorithms for Data Science, Probability and Statistics for Data Science, Exploratory Data Analysis and Visualization, Statistical Inference and Modelling, Applied Machine Learning, Applied Deep Learning, Data Analytics amongst others.

New York University, NY City (NY):   MS Data Science

Minimum Requirements: Admission Criteria is holistic

A.      GPA: 3.0 (around 75-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

B.      GRE: No minimum criteria but high GRE scores are recommended to the tune of around 320-330.

C.      TOEFL: 100.  IELTS: 7.0

D.     Work Experience: Not mandatory but relevant experience in data science will be an added advantage to secure admission.

E.      Prerequisites: Prior quantitative coursework (calculus, linear algebra, etc.). Prior introductory computer programming coursework

F.      Research Experience: not mandatory

G.     SOP and Personal History Essay

H.     3-Recommendation Letters

I.        Professional Resume

 

1.      Tuition Fees: 80,000 USD for program (Living expenses extra)

2.      Course Duration: 24 Months (36 Credit Hours)

3.      Application Deadline: Fall Deadline: Jan 22nd    

4.      Concentration Areas: 1. Data Science Track, 2. Natural language Processing.3. Mathematics and Data.4. Biology Track.5. Biomedical Informatics. 6. Big Data 7. Physics .8. Data Science Industry Concentration

5.      Course Work:  Introduction to Data Science, Probability and Statistics for Data Science

Machine Learning, Big Data ,Capstone Project and Presentation, Inference and Representation, Deep Learning, Natural Language Processing with Representation Learning, Natural Language Understanding and Computational Semantics, Mathematical Tools for Data Science, Optimization and Computational Linear Algebra, Fundamental Algorithms

Database Systems, Programming Languages, Bayesian Machine Learning, Risk Management & Machine Learning among many others can be chosen from electives from various tracks.

 II Program : New York University, Stern School of Business (NYC and Shanghai campuses): M.S. in Data Analytics and Business Computing

 https://stern.shanghai.nyu.edu/en/program/ms-data-analytics-business-computing/class-profile

 

Johns Hopkins University, Baltimore (Maryland):   MSE in Data Science

 

Minimum Requirements: Admission Criteria is holistic

1.      GPA: 3.0 (around 75-90%) ..admission criteria is holistic..low GPA can be compensated with other criteria. Undergraduate Institute’s repetition will play an important role

2.      GRE: No minimum criteria but high GRE scores are recommended to the tune of around 320-330.

3.      TOEFL: 100.  IELTS: 7.0

4.      Work Experience: Not mandatory but relevant experience in data science will be an added advantage to secure admission.

5.      Prerequisites: candidates should have completed undergraduate-level courses in Calculus (through multivariable calculus), Linear algebra, Differential equations, Probability, Computer programming (e.g., in C++ or Python) at least,  preferably complemented with a course in Statistics and at least one proof-writing course.

6.      Research Experience: not mandatory

7.      SOP and Personal History Essay

8.      3-Recommendation Letters

9.      Professional Resume

 

1.      Tuition Fees: 1,16,000 USD for program (Living expenses extra)

2.      Course Duration: 18-24 Months

3.      Application Deadline: Fall Deadline: Dec 15th. Spring Deadline: Sept 15th     

4.      Concentration Areas: Statistics, Machine Learning, Optimization, and Computing

5.      Course Work:  Machine learning, Bayesian Statistics, Casual Inference, Statistical Pattern Recognition, Big Data Algorithms, Monto Carlo Methods, Computer Vision, Wavelets and Filter Banks , Deep Learning, Natural language processing among many other electives can be chosen from above concentration areas.

 

 

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