logotype
logotype
Learning
Home Archive by Category "Learning"

Category: Learning

GRELearning

How Do Universities Evaluate Your Profile

Many students ask us questions such as, “How important are my GRE/TOEFL/IELTS scores? Does Work Experience or Research work increase my chances of admission? Will I get into this University with a 6.3 GPA?” Etc…

To get a better idea of your profile, it’s important to know how Universities evaluate a Student’s profile?

These are different aspects of your profile the Universities consider-

  • Undergraduate Academic Scores and Research Experience 
  • Work Experience (If there)
  • GRE and TOEFL/IELTS/PTE Scores 
  • Statement of Purpose, and Letters of Recommendation

The University admissions department, consisting of faculty members of your course, considers all the above factors of your profile, but the weightage given varies.

GPA & Research Experience

This is the most important aspect of your profile! Your faculty gives maximum weightage to your GPA because this directly reflects your expertise in the subject. Universities usually consider grades of your last 3 or 4 semesters, especially grades in the subjects relevant to the course you’re applying to. Any relevant research work, paper publications, Certifications, and final year projects all have a fair impact on the admissions panel.  

Work Experience

Your most recent work experience is another major aspect, given that it’s relevant to the course you’re applying to.  If you’re someone who has been working for  3+ years, your work experience is usually given more importance than your academics, given your work is in the same field as that of your course. Your internships also count as work experience but they’re not as impactful as a full-time job experience. 

Remember that work experience is not mandatory for a student to apply for the majority of Master’s courses. It’s good to have work experience as it will increase your chances of admit but not compulsory. There are a handful of Master’s courses that require work experience, especially a few Management courses, so please check the course website before you apply. 

Keep in mind that Universities give maximum weightage to your most recent “learning experience”  at the time of application, be it work or study. 

GRE Scores

GRE scores in most of the cases, especially for top Universities in the USA and Canada, are mandatory to submit.  The Top Universities of other Countries where it’s not mandatory, take GRE to be an added credential! A good GRE score translates into strong Analytical and logical skills, which are important tools to pursue your Graduate courses. In some cases, we have seen students with low academics, getting admits into decent Universities because of good GRE scores. 

Statement of Purpose and Letters of Recommendation

Statement of Purpose is a letter you write to the admission panel about yourself, your past experiences, and why you want to pursue a Master’s degree? Some Universities will ask you to answer specific questions regarding yourself. SOP is a great opportunity to express your interest, experiences, and ambitions in your field of study. All the members in the admission panel will go through your SOP, as this is the one way of getting to know you personally (Unless you are asked to give a personal interview, which some Universities do. Please check the University website). 

Letters of Recommendation are written by your professors or senior employees under whom you have studied or worked. You’re asked to submit 2 to 3 LORs in your application. Through these letters, the panel will get to know more about your work and performance. 

Both SOPs and LORs combined can be pretty strong influencers on the admission decision. Sometimes, those students who don’t have good academics or GRE scores can convince the panel through strong SOPs and LORs.  

TOEFL/IELTS/PTE Scores

English-based tests scores are mandatory for all international students who are from countries where English is not a Native language. Every University has a minimum score requirement, using which they screen the applicants. They’re not as important as the above factors for securing admission but the applicants have to cross the Minimum requirement. In some cases,  students who do not cross that requirement might be given admission but have to finish Advance English courses at the University. Please check the University’s website for alternatives!  But, in case you’re planning to apply for a Research or Teaching assistantship after you get an admit, having high scores on these tests would increase your chances of getting an assistantship offer. 

All these above aspects are considered by the panel while making an admit decision and no one aspect is “The important”, given you cross the minimum requirements. 

Jayasurya

(M Sc. in Entrepreneurship, Brown University)

READ MORE
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.

READ MORE
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

 
READ MORE
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.

 

 

READ MORE