“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.