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It goes without saying that the best thing you can do here, is to work as hard as you can and earn a good score in order to help your chances of getting into a top school abroad. If you haven’t got great scores, you could always try to compensate these with great SAT and GRE scores.

As schools look at more than academics when considering your application, you can always ensure that you pay attention to the following areas:

Co-curricular activities. Develop co-curricular activities to add color to your personality.

Take initiatives. Get out of your comfort zone and do something meaningful. It could be anything, from playing a sport to organizing a blood donation drive in your neighborhood. Be visible in what you do, rather than a participant that isn’t very interested in what they’re doing.

Personal attributes schools would love to know about include your leadership attributes and your ability to work in a team.

You won’t be able to fake these attributes and skills, so start early and cultivate them. You should pay special importance to improving your communication skills.

What you should know before the application process?


The first step every student takes before applying to colleges is shortlisting the colleges that are best suited for them. Doing this early on will ensure that you apply to colleges well before the deadline, which is always recommended.

While selecting the colleges you wish to apply to, we suggest that you divide your universities into three categories – Dream, Reach and Safe.

1. **“Dream”** should include universities that you dream of getting but may not get into as a result of not meeting the program requirements or due to intense competition.
2. **“Reach”** should include universities that you have better chances of getting into as you meet all the requirements set by the them.
3. **“Safe”** should include universities that you are sure you will get into.
4. The first impression always matters, so ensure that you complete and submit your application as soon as you can.

Your essays and recommendations should demonstrate that you are the right “fit” for the school and would be able to positively contribute to the intellectual and cultural community.

While writing your essay, “demonstrate” – rather than say “you are a good student”.

You will need to provide proof that you have sufficient financial backing to support you during the course of your higher studies.

What you should know during the application process?

Does this question bother you? Here are some tips to help you out:

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Will a School Accept Me?

Data science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in either structured or unstructured forms.

From scientific discovery to business intelligence, data science is changing our world. The dissemination of nearly all information in digital form, the proliferation of sensors, breakthroughs in machine learning and visualization, and dramatic improvements in cost, bandwidth, and scalability are combining to create enormous opportunity.

The field also presents enormous challenges, thanks to the relentless increase in the volume, velocity, and variety of information ripe for mining and analysis.

It employs concepts and techniques from mathematics, statistics, information science, and computer science, in particular from machine learning, classification, cluster analysis, data mining, databases, and visualization.

“Data scientist” has become a popular occupation with the Harvard Business Review dubbing it “The Sexiest Job of the 21st Century” and McKinsey & Company projecting a global excess demand of 1.5 million new data scientists.

What is Data Science?

Data scientists use their data and analytical ability to:

- find and interpret rich data sources
- manage large amounts of data despite hardware, software, and bandwidth constraints
- merge data sources
- ensure consistency of datasets
- create visualizations to aid in understanding data
- build mathematical models using the data
- present and communicate the data insights/findings.
- They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards.

What do Data Scientists do?

Thinking for pursuing an MS in Data Science (or, Machine Learning)?

Head to the Home of Data Science and Machine Learning – Kaggle Competition!

Kaggle is a platform for predictive modelling and analytics competitions in which companies and researchers post data and statisticians and data miners compete to produce the best models for predicting and describing the data. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know at the outset which technique or analyst will be most effective.

Kagglers come from a wide variety of backgrounds, including fields such as computer science, computer vision, biology, medicine, and even glaciology. It also includes many of the world’s best-known researchers, including members of IBM Watson’s Jeopardy-winning team and the team working on Google’s DeepMind. Many of these researchers publish papers in peer-reviewed journals based on their performance in Kaggle competitions.

How to Build Your Profile for MS in Data Science?

- Companies and organizations prepares the data and a description of the problem. Kaggle frame the competition, anonymize the data, and integrate the winning model into their operations.
- Participants, like you, experiment with different techniques and compete against each other to produce the best models. Work is shared publicly through Kaggle Scripts to achieve a better benchmark and to inspire new ideas. Submissions are made through Scripts or through private manual upload. For most competitions, submissions are scored immediately (based on their predictive accuracy relative to a hidden solution file) and summarized on a live leaderboard.
- After the deadline passes, the host company pays the prize money for the winning solution. many companies recruit participants based on their place on the leaderboard, final score, and submitted scripts.
- Alongside its public competitions, Kaggle also offers private competitions limited to Kaggle’s top participants.

How does Kaggle Competitions Works?

A career in Data Science involves statistics, mathematics, business, economics and Computer Science.

After a Master’s in Data Science, you can work in various sectors such as finance, healthcare, consulting, retail or consumer products – basically any field where there is lots of data and there is a requirement to analyze large data sets to develop custom models and algorithms to drive business solutions.

With regard to Data Science, the primary focus is on applications rather than research. You use some knowledge from Computer Science (data structure, deep learning, computer vision, natural language processing, machine learning) in your data science role.

Typical employers include Walmart, Tesla, Intuit, Collective Health and numerous financial/trading companies on Wall Street.

The average salary for a job in Data Science in the US is about $113,000 as per Glassdoor. Another source – Payscale – puts the median salary at about $93,000.

Let’s have a look at the application of data science in different fields.

**Data Science in Retail** 
With online commerce, retail data is increasing exponentially in terms of volume, the velocity at which data is being generated and their value for the kind of insights and profit they could offer. As per McKinsey’s report on Big Data, retailers using big data analytics could raise their operating margins by as much as 60 percent.

The following points are a few of the applications of big data in retail:

1. Customer Experience: Personalized recommendation based on purchase history, sentiment analysis, predictive analytics for improving customer experience across all channels and devices
2. Merchandising: Improving layout, product placement and promotional display, identify cross-selling opportunities
3. Marketing: Location-based personalized offers on mobile phones, real-time pricing, better targeted campaigns
4. Supply chain logistics: real-time inventory tracking and management, demand-driven forecasting, route optimization and efficient GPS-enabled transportation.

**Data Science in Health Care** 
In the US, health care expenses represented 17.6% of the GDP in 2013 with annual spend of $2.6 trillion. Out of this, $600 billion was consumed by waste and fraud. By 2020, this figure is estimated to rise to nearly 20%.

Big Data has the potential to help physicians make better decisions across the board – from personalized treatments to preventive care, while, at the same time, slashing the cost of providing health care services.

The following list details some of the applications of big data in retail:

1. Personalized medicine: Create a personalized treatment plan based on individual biology using data from various sources including clinical trials, electronic medical records, online patient network, genomics research etc
2. Genomics: Inexpensive DNA sequencing and next-generation genomic technologies are changing the way health care providers do business. They are getting better understanding of the genetic bases of drug response and disease by combining genomic data with other data in disease research.
3. Predictive analytics and preventive measures: Some examples are: Mount Sinai Medical Center reduced its readmission rate, Texas Health identified high-risk patients to offer them customized interventions and Methodist Health System predicted patients who will need high cost care in future.
4. Patient monitoring and home devices: Wearable body sensors – sensors tracking everything from heart rate to testosterone to body water – can take vital stats of the patients every minute of the day. Personal ECG heart monitor, medical monitoring devices and mobile applications are cropping up daily.

**Data Science in Finance** 
There has been a flood of financial data in recent times from various sources such as social media activity, mobile interactions, server logs, real-time market feeds, customer service records, transaction details and, of course, information from existing databases.

The following list details some of the applications of big data in finance:

1. Sentiment analysis: Use natural-language processing, text analysis and computational linguistics to discover what people really think.
2. Automated risk credit management: Alibaba has successfully used big data to offer loans to entrepreneurial online vendors without any collateral by using their transaction records, customer ratings, shipping records and a host of other info.
3. Real-time analytics: helps in fighting financial fraud, improve credit ratings and providing more accurate pricing
4. Predictive analytics: For example, whether certain customers are likely to pay off their credit cards using the demographic characteristics of customers’ neighborhoods and making calculated predictions.

**Big Data in Telecom** 
Mind Commerce, a market research firm, predicts that the big-data-driven telecom analytics market will grow by nearly 50 percent from 2014 to 2019 and forecasts that by the end of 2019, the market will be up to $5.4 billion in annual revenue.

Here are some applications of big data in finance:

1. Personalized services: applications include determining a subscriber’s lifetime value, reveal cross-channel insights and avoid customer churn
2. Network optimization: using real-time and predictive analytics, analyze subscriber behavior and create individual network usage policies
3. Location-based initiatives: use geo-fencing and sensor technology data scientists can predict a subscriber’s location and specific data needs with stunning accuracy to, for example, create targeted offers, when a subscriber is in a super market
4. Churn prevention: combine variables such as calls made, minutes used, number of texts sent, average bill amount and behavior such as visiting competitor’s website to predict the likelihood of subscriber changing to a competitor for bargains
5. There are similar applications of big data in other domains such as Utilities, Travel and Transportation, Insurance, Pharmaceutical, Manufacturing, Gaming, Hospitality, Biotech and Energy.
 

Let’s quickly compare a career in Data Science with a career in Machine Learning.

**Career in Machine Learning** 
Machine learning is the study of how computers can learn complex concepts from data and experience, and seeks to answer the fundamental research questions underpinning the challenges outlined above.

The field of machine learning crosses a wide variety of disciplines that use data to find patterns in the ways both living systems, such as the human body and artificial systems, such as robots, are constructed and perform.

Whether it’s being applied to analyze and learn from medical data, or to model financial markets, or to create autonomous vehicles, machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.

In a Machine Learning job, you are expected to solve new and emerging technical challenges related to human-machine interactions.

In your role, you will utilize core computer science and engineering skills like high-performance computing, distributed systems and applied math.

You are expected to have 5+ years of experience in programming parallel and distributed systems, debugging low-level problems, performance analysis and optimizations, and numerical methods.

Also include – experience in using machine learning techniques for classification, regression, or ranking problems, experience in building predictive models for recommendations or personalization, design and implementation of shipping, innovative consumer products etc.

Typical employers include Facebook, Amazon, Apple, Google and Microsoft.

Career in Data Science

Factors important to identify best universities in machine learning / data science

**1) University reputation (rankings)** 
This factor is important in general but more so for the data science programs. This is because most of them are relatively new, i.e. around 2-4 years old and it’s difficult to establish credibility in the industry in such a short duration. – Thus, the university brand name plays a key role on how your candidature will be perceived in the industry after completing the degree. No doubt, your knowledge would always matter more, but university reputation plays a crucial role for new courses.

**2) Location** 
Location plays a pivotal role in practical learning opportunities outside the campus. Practical training typically comes in the form of internships, capstone projects, weekend hackathons, etc. Given that data science is a highly application-oriented domain, practical training would play a crucial role in your overall development. – While you are in the program, its location can have quite an impact on your profile in terms of getting good internship opportunities. Also, a strong data science community gives access to specialized skill meetups and hackathons. For instance, the data science communities in cities like New York or Silicon Valley will be much stronger than other suburban locations. – After the program, a good location definitely helps with the job search as there will be ample employment opportunities.

**3) Curriculum** 
I believe this is the most important aspect and the first thing which you should check out. The curriculum actually tells you what subjects you’ll be studying and gives an impression about the relevance of the program for you. Typically, coursework is divided into core courses (compulsory courses) and electives. You should also check out the list of courses from which you can choose the electives. – Curriculum flexibility i.e. the ratio of elective courses, is another important factor. It can vary from as high as 60-70% in some courses to almost none in others.

**4) Industry Collaborations** Since most of the programs in data science related courses are professional, industry collaborations will play a key role in your experience through the program. You should check out the particular companies, which domain they belong to, what sort of activities are conducted like technical talks, research collaboration, capstone projects, etc.

**5) First Hand Experience** 
The first step is to log into the university’s website and have a look at the details of the program. You can do a first level filtering based on the evident information on website. But, an equally important aspect is to talk to people who are already studying there as well as the university’s alumni. You can definitely apply to all the colleges you like, but for making the final choice, I can’t over-emphasize the importance of this step, which will give you a true picture about the college administration and recognition in the industry. These factors are really hard to judge from any university’s website. Also, given that these programs are mostly new, the amount of discussions on third-party websites like Quora are also limited. – If you’re wondering how to find these people, again LinkedIn and Facebook are your best friends!

**6) Program name is not so important!** 
The traditional philosophy – ‘Don’t judge a book by its cover’ works in this case as well. Since Data Science (and Machine Learning) is a non-traditional program, you’ll find all sorts of names like Masters in Analytics, Masters in Business Analytics,Masters in Data Science, Masters in Predictive Analytics, Masters in Marketing Analytics, Masters in Information Systems, etc. Trust me, names can be very misleading. Although, they do give you an idea of what the program is all about, the name of the program should definitely be your last concern, if at all!

How to shortlist universities for MS in Data Science?

The following schools are some of the best schools that offer programs in Data Science, and you can consider these for your reach, match and safe shortlist.

**University of Southern California** 
Program: MS Data Science

The MS in Computer Science – Data Science provides students with a core background in Computer Science and specialized algorithmic, statistical, and systems expertise in acquiring, storing, accessing, analyzing, and visualizing large, heterogeneous and real-time data associated with diverse real-world domains including energy, the environment, health, media, medicine, and transportation.

Courses: Foundation of artificial intelligence, Analysis of algorithms, Databases
Electives: Information retrieval and web search engines, high performance computing and simulations, advance topics in database systems, foundation of data management, machine learning, probabilistic reasoning, advanced big data analytics, foundation and application of data mining, optimization: theory and algorithms, information visualization, building knowledge graph, numerical analysis, applied probability

**Columbia University** 
Program: MS Data Science

The Master of Science in Data Science allows students to apply data science techniques to their field of interest. Our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. Students may also choose an elective track focused on entrepreneurship or a subject area covered by one of our seven centers.

Who should apply – Individuals looking to strengthen their career prospects or make a career change by developing in-depth expertise in data science. Candidates for the Master of Science in Data Science are required to complete a minimum of 30 credits, including 21 credits of required/core courses and 9 credits of electives.

Required Courses: Probability theory, algorithms for data science, statistical inference and modeling, computer system for data science, exploratory on data analysis and visualization

**University of Rochester** 
Program: MS Data Science

The Goergen Institute for Data Science offers a STEM-accredited MS program in data science. This program allows students to study the broad area of data science or to concentrate their studies in one of the following areas:

Computational and statistical methods
Health and biomedical sciences
Business and social science
The program can be completed in either one year or one and a half years of full-time study. Each graduate will receive a degree conferred by the University’s School of Arts and Sciences.

**University of Massachusetts Amherst** 
Program: MS CS (With concentration in Data Science)

The Computer Science Masters with a Concentration in Data Science was created to help meet the need for expanded and enhanced training in the area of data science. It requires coursework in Theory for Data Science, Systems for Data Science, Data Analysis and Statistics.

Aerial photo of computer science buildingThe Masters Concentration in Data Science teaches you to develop and apply methods to collect, curate, and analyze large-scale data, and to make discoveries and decisions using those analyses.

**University of Washington Seattle** 
MS Data Science

The Master of Science in Data Science at the University of Washington gives you the technical skills to extract knowledge from large, noisy, and heterogeneous datasets — big data — to provide insights that people and organizations can use.

Our interdisciplinary curriculum was developed by leading faculty from six top-ranked departments and schools at the UW, with input from top companies looking to hire data science professionals. In this program, you’ll build deep expertise in managing, modeling and visualizing big data to meet the growing needs of industry, government, nonprofit and research organizations today.

**University of California Irvine** 
Program: Master of Science in Business Analytics

MSBA program at The Paul Merage School of Business at UC Irvine is a STEM-designated, intensive one-year, full-time degree program for students with or without work experience. Taught by world-class faculty and leading researchers in the field, graduates of this program will be eminently qualified for big data and analytics careers.

Merage School Virtual TourThe MSBA offers flexible curricular tracks to align with your desired career path:

- Data Analytics – actuaries, database administrators, financial analysts, information security analysts, management analysts, survey researchers
- Marketing Analytics – market research analysts, marketing specialists, marketing managers, sales managers, survey researchers
- Operations Analytics – analytics specialist, financial analysts, management analysts, operations research analysts, statisticians, transportation, storage and distribution managers
- Learn more

**New York University** 
Program: MS in Data Science

The Master of Science in Data Science is a highly-selective program for students with a strong background in mathematics, computer science, and applied statistics. The degree focuses on the development of new methods for data science.

We live in the “Age of the Petabyte,” soon to become “The Age of the Exabyte.” Our networked world is generating a deluge of data that no human, or group of humans, can process fast enough. A new discipline has emerged to address the need for professionals and researchers to deal with the “data tidal wave.” Its object is to provide the underlying theory and methods of the data revolution. This emergent discipline is known by several names. We call it “data science,” and we have created the world’s first MS degree program devoted to it.

The curriculum is 36 credits, and offers two ways to structure the graduate program that gives students the opportunity to pursue a specialization through tracks. NYU will offer a limited number of tuition scholarships to selected students admitted to the program. All applicants for admission will be considered for these awards on a competitive basis.

**University of Texas Dallas** 
Program: MS CS (Data Science Track)

STRATEGICALLY LOCATED in the middle of the Telecom Corridor, which is home to hundreds of hightech companies, the Computer Science Department is in the midst of a growth phase that includes addition of new programs in cybersecurity, information assurance, data sciences and interactive computing, hiring of a large number of new faculty, and a steep increase in external research funding.

Courses in Data Science Track: Statistical Methods for Data Sciences, Big Data Management and Analytics, Design and Analysis of Computer Algorithms, Machine Learning

**Michigan Technological University** 
Program: MS Data Science

Our degree will provide you with a broad-based education in data mining, predictive analytics, cloud computing, data-science fundamentals, communication, and business acumen. Additionally, you will gain a competitive edge through domain-specific specialization in disciplines of science and engineering. You will have the freedom to explore and develop your own interests in one or more domains.

Learn more

** Illinois Institute of Technology** 
Program: MS Data Science

In Illinois Tech’s Master of Data Science program, you learn to explore data using high-level mathematics, statistics, and computer science. In particular, you learn how to analyze data, visualize your results, and articulate your discoveries. You will leave the program with the ability to think about the real problems that need to be solved, not to simply find technical solutions.

You will learn to question underlying premises and reformulate issues, explore and improve the structure of available data, create and evaluate models, construct and test hypotheses, draw conclusions, and determine if the results make sense in the real world. You will then learn to communicate these results to specialists and non-specialists alike.

The program is offered to full-time students on the Mies Campus, just minutes south of the Chicago Loop, a global finance center, whose businesses rely on amassing data in finance, healthcare, retail, manufacturing, consumer services, tourism, professional sports, and cultural activities. In this international city on the shores of Lake Michigan, data science students have the opportunity to engage in Chicago’s thriving tech community. Moreover, the City government allows and encourages access to its publically available wealth of municipal data.

**Cleveland State University** 
Program: MS Computer & Information Science

The Information Systems (IS) track in the Master of Computer and Information Science (MCIS) program at Cleveland State University is a specialized degree program designed to prepare students for careers as information professionals. The IS track is housed within the Monte Ahuja College of Business. The coursework combines a blend of technology and management-oriented courses designed to prepare the next generation of technology managers to lead enterprises in innovative ways. Specializations within the program allow students to build specific skill sets in areas such as: information security, information technology management and business analytics.

**Rochester Institute of Technology** 
Data science, a term first coined in 2008 by data analytics leaders at Facebook and Google is a new and inherently multidisciplinary field – combining computing, mathematics, statistics, and the sciences – devoted to the management and analysis of massive, mostly unstructured data. RIT’s approach to data science is distinctly different from the existing programs. Firstly, this degree is career focused, aiming to equip students with practical skills to handle large-scale data management and analysis challenges that arise in their daily work. The career-focused degree will also significantly benefit from one of the world’s largest co-op programs at RIT, which brings in practical problems, real world data, and software tools commonly adopted in industry to enrich our curriculum.

Secondly, the program is highly interdisciplinary and domain driven, focusing on domain specific problems and solutions. It also provides students the opportunities of interdisciplinary study. Important domains (e. g, biology, physics, and statistics) are judiciously selected and integrated as part of the curriculum to provide customized, domain-specific training to next-generation data scientists.

**Wayne State University** 
MS Data Science and Business Analytics

The Mike Ilitch School of Business and College of Engineering have developed a novel Interdisciplinary Master of Science in Data Science and Business Analytics program, which is designed to help students excel in both industry and academia.

This novel Master’s program is designed to provide students with a broad range of data science and business analytics knowledge and skills. Each student will need to select one of the three major concentrations to provide a specialized track to give them more in depth knowledge and skills in that area of specialization.

When applying to the program, applicants will have to select which Program and Major Concentration they want to apply for:

- Data Science and Business Analytics – MS Business Program – Data-Driven “Business” Concentration
- Data Science and Business Analytics – MS in Engineering Program – Advanced “Analytics” Concentration
- Data Science and Business Analytics – MS in Engineering Program – Computational “Engineering” Concentration
- Learn more
- 
**Additional Schools** 
At following schools, you would find Data Science, Business Analytics and related programs:

- Carnegie Mellon University: MS in Computational Data Science
- Stanford University: MS in Statistics: Data Science
- Georgia Institute of Technology: MS in Analytics
- MIT Sloan: Master of Business Analytics
- Columbia University: Master in Data Science
- Michigan State University: MS in Business Analytics
- University of Washington: MS in Data Science
- University of Southern California: MS in Data Informatics
- University of Chicago: MS in Analytics
- New York University: MS in Data Science
- Northwestern University: MS in Analytics
- North Carolina State University: MS in Analytics
- Texas A&M University: Masters in Analytics
- University of Cincinnati: MS in Business Analytics
- Arizona State University: Master in Business Analytics
- Illinois Institute of Technology: Master of Data Science

13 Schools for MS in Data Science that you can consider

Learn about exciting career options in data science at 16 top schools in the US and how to shortlist the best schools for your profile to reach those career goals

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Learn about exciting career options in data science and how to shortlist the best schools for your profile to reach those career goals  in this blog

How to Build a Career in Data Science

Data science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in either structured or unstructured forms. From scientific discovery to business intelligence, data science is changing our world. The dissemination of nearly all information in digital form, the proliferation of sensors, breakthroughs in machine learning and visualization, and dramatic improvements in cost, bandwidth, and scalability are combining to create enormous opportunities.

Data scientists use their data and analytical ability to find and interpret rich data sources and manage large amounts of data despite hardware, software, and bandwidth constraints along with merging data sources. They ensure consistency of datasets and create visualizations to aid in understanding data. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards.You will get the knowledge you need to transform your organization’s data into a usable asset.

Head to the Home of Data Science and Machine Learning – Kaggle Competition! Kaggle is a platform for predictive modelling and analytics competitions in which companies and researchers post data and statisticians and data miners compete to produce the best models for predicting and describing the data. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know at the outset which technique or analyst will be most effective.

A career in Data Science involves statistics, mathematics, business, economics and Computer Science. After a Master’s in Data Science, you can work in various sectors such as finance, healthcare, consulting, retail or consumer products – basically any field where there is lots of data and there is a requirement to analyze large data sets to develop custom models and algorithms to drive business solutions. With regard to Data Science, the primary focus is on applications rather than research. You use some knowledge from Computer Science (data structure, deep learning, computer vision, natural language processing, machine learning) in your data science role.

What are the Career options in Data Science?

University of Southern California, Columbia University, University of Rochester, University of Massachusetts Amherst, University of Washington Seattle, New York University and Wayne State University are some of the best universities for Data Science.

What are the Schools for MS in Data Science?

After you have short-listed the universities, you begin filling complicated and lengthy application forms, you start writing your Statements of Purpose, preparing your resume, and obtaining Letters of Recommendation from your professors in college.

Having the assistance of experts with a keen eye for all these issues that affect your chances admission; and resolving such issues quickly and decisively will maximize your chances of getting in.

Pre- final year students are highly recommended to start building up their profile as US MS applications warrant a portfolio of research work/ papers/ projects on the field they want to specialize in. Unfortunately in India as against the US education systems, research papers/ projects are not an integral part of Under Grad courses. Indian students have to work on their profile at least from the year before they have to send their applications. They should be advised from experts on what how an which projects/profile development work will be best for them.

**How to go about your MS Applications?** 
Graduate school can be fun, challenging, and rewarding; it can also be difficult, demanding, and frustrating. Either way, it will be one of the most memorable experiences of your life.

Once the decision of embarking on the graduate (Master’s) program is made, you must sit down and make a list of all the alternatives available.

There are many things do and you may feel that there is a shortage of time. Discussions and chats with seniors about the value of an MS in your career will definitely be a clear navigation towards your goals. The more time spent, the more informed your preferences will be.

**Taking your GRE** 

- The earlier the GRE is taken, the better it is. Start preparing the moment you plan to join a graduate school.
- Spread out the load of the application process by booking dates for ETS exams (GRE, TOFEL) in January.
- During summer, take the GRE and TOFEL, and in September, take TSE, if required.
- You could take the GRE subject Test in October/November of your 2nd or 3rd year of college.
Save time, money, effort and stress: Apply Online

The application, involves among other things, an online application form, and the application packet consisting of the following:

- Copy of the online application form.
- Photocopy of the test Scores (GRE, TOEFL, TSE)..
- Resume.
- Financial documents (if required).
- Letters of Recommendation.
- Statement of purpose (SOP).
- Certificates, Papers etc. (if required)
- It is always a good idea to apply online for the simple reason that it saves you a lot of trouble and time.
- It is a more proactive process and you can send the forms electronically with the click of a mouse..
- Applying online also gives you the advantage of being able to track the receipt of your documents and status of your application.

**Making an application schedule** 
- It is always advisable for you to plan and set a time line.
- You need to create a timeframe in which you allocate the universities, prepare and take the GRE/TOEFL/TSE, contact the professors you would like to work under, and go about preparing the application SOP, resume, and other documents.

**Which graduate school is the one for you?** 
- Before you can decide on a graduate school it is essential to research the expansive list of US universities to find a good match.
- To remove the biggest obstacle of choosing from the expansive list of US Universities, you need to shortlist 10-12 universities from which you can shortlist your final 5.
- Check the faculty and the number of professors in your research area. Spend time exploring especially from university websites which give you a fairly good idea of the same.

**Putting it all Together** 
- Begin thinking about, and subsequently, writing the statement of purpose by July or August. Try to complete two or three drafts by mid-September.
- An editor can help you create a timeline for working on your documents.
- Creating a cycle to complete your tasks will help you draft and complete all documents for a required university with two to three weeks.
- Try to send all the documents by the middle of October. The deadlines may be much late. You would definitely have an edge over the admission and aid.

Applying to a graduate school is a long and daunting series of tasks.

Wanting to pursue Masters but don't know how to go about it? Here is a detailed blog on how to start your MS applications with ease and calm.

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MS applications made easy: planning, saving money and other tips

Graduate school can be fun, challenging, and rewarding; it can also be difficult, demanding, and frustrating. Either way, it will be one of the most memorable experiences of your life. Once the decision of embarking on the graduate (Master’s) program is made, you must sit down and make a list of all the alternatives available. There are many things to do and you may feel that there is a shortage of time. Discussions and chats with seniors about the value of an MS in your career will definitely be a clear navigation towards your goals. The more time spent, the more informed your preferences will be.

How to go about your MS Applications?

Before you can decide on a graduate school it is essential to research the expansive list of US universities to find a good match. To remove the biggest obstacle of choosing from the expansive list of US Universities, you need to shortlist 10-12 universities from which you can shortlist your final 5. Check the faculty and the number of professors in your research area. Spend time exploring, especially from university websites which give you a fairly good idea of the same.

Which graduate school is the one for you?

The title of ‘European PhD with distinction’ can be used if the following criteria are fulfilled:

- During the period of study, the PhD student must spend a minimum of three months in a higher education institute or research centre in a European Union Member State either following a course of study or working on research projects that have been recognised by the university.
- Part of the doctoral thesis, at least the abstract and the conclusions, must be written and presented in one of the official EU languages other than the official languages of Spain.
- The doctoral thesis must be supervised by at least two experts from a higher education institution or research institute of a European Union Member State.
- The doctoral thesis to be defended at the university at which the doctorand is enrolled must be supervised by at least one expert from a higher education institution or research institute of a European Union Member State other than Spain, who holds the title of doctor and is not the supervisor mentioned in sections a) and c).

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Read on to find out all the conditions you need to meet in order to earn the title of 'European PhD with distinction.

When can you use the title ‘European PhD with distinction’?

The title of ‘European PhD with distinction’ can be used if the following criteria are fulfilled: During the period of study, the PhD student must spend a minimum of three months in a higher education institute or research center in a European Union Member State either following a course of study or working on research projects that have been recognized by the university.  The doctoral thesis must be supervised by at least two experts from a higher education institution or research institute of a European Union Member State. The doctoral thesis to be defended at the university at which the doctor and is enrolled must be supervised by at least one expert from a higher education institution.

These programs are mainly offered for the students for the reasons mentioned below:

1. A student who has not met the minimum requirement or who has a low GPA in Bachelors for a particular program, and who have scored less in IELTS/TOEFL/PTE for those students who have missed the application deadlines.
2. If the student is a working professional, having 1, 2 or up to 5 years of experience and has no time in taking up GRE.
3. The student has appeared for GRE but has scored very less and does not want to reappear again.
4. If the student is very keen about the USA and has only 3 months of time for a particular intake to start, the student can directly start the application process, and can expect to receive the admissions decision within 4 weeks and can proceed with the visa filing by scheduling or applying for emergency visas without any additional cost.
5. If the student is not willing to pay the application fees or courier charges.
6. These pathway programs are also applicable for bachelors students, who have not taken up ACT/SAT or have scored very low marks.

Why Opt for Pathway Programs?

There will be 2 conditional admits for the pathway programs mentioned below:

**Condition 1:** The student has to maintain a cumulative GPA of 3.0-3.5 out of 4 in the first semester of their program

**Condition 2:** Certain programs such as MIS or Computer Science or Computer Engineering programs do require GRE. For these programs, the student gets 4 to 6 months time to take up the GRE, while studying their master’s program and the scores should be submitted once the student has completed the first semester.

What are the conditions of the pathway programs?

Most of the universities in the US, which offer these pathway programs are of Tier 1 and Tier 2 universities. Getting visas should not be a problem if the student is confident in answering the visa officer about the pathway program.

And almost all the programs come under accreditation i.e for Engineering and Technology it comes under ABET (Accreditation Board of Engineering and Technology) and for Business Schools it comes under AMBA or AACSB (Association Of MBA, and Association to Advance Collegiate Schools of business).

What is the Visa Success Ratio for the Pathway Programs?

There is no major difference between the tuition cost. Overall, a master’s program for direct admissions will cost around 35 to 40 lacs and for the pathway programs, for a few universities, it will be about the same or up to 45 lacs.

What is the difference in tuition cost between pathway programs and direct programs?

Once the student gets into the pathway program, an academic advisor will be assigned to the student, who can help the student in their academic profile building and also helps in building resumes and keep a track of the student’s performance.

What are the advantages of getting into pathway programs?

## INTO
- INTO Colorado State University
- INTO Drew University
- INTO George Mason University
- INTO Hofstra University
- INTO Illinois State University
- INTO Marshall University
- INTO Oregon State University
- INTO Saint Louis University
- INTO Sufflock University
- INTO Washington State University
- INTO Western Washington University
- INTO Glasglow Caledonian University
- INTO London World Education Centre
- INTO Manchester
- INTO Newcastle University
- INTO Queen’s University, Belfast
- INTO University of East Anglia
- INTO University of Gloucestershire
- INTO University of Stirling
## Study Group
- ISC – Baylor University
- ISC – James Madison University
- ISC – Lipscomb University
- ISC – Long Island University, Brooklyn
- ISC – Long Island University, Post
- ISC – Merrimack
- ISC – Oglethorpe University
- ISC – Roosevelt University
- ISC – Texas A & M University-Corpus Christi
- ISC – The City College of New York
- ISC – University of Vermont
- ISC – West Virginia University
- ISC – Widener University
- ISC – Bellerbys College
- ISC – Coventry University London
- ISC – Durham University
- ISC – Keele University
- ISC – Kingston University, London
- ISC – Lancaster University
- ISC – Leeds Beckett University
- ISC – Royal Holloway, University of London
- ISC – The University of Sheffield
- ISC – University of Huddersfield
- ISC – University of Leicester
- ISC – University of Lincoln (Study Group)
- ISC – University of Strathclyde
- ISC – University of Surrey
- ISC – University of Sussex
- ISC – University College Dublin
- ISC – Holland International Study Centre
## Navitas
- Australian College of Applied Psychology (ACAP)
- Curtin College
- Deakin College
- Edith Cowan College
- Eynesbury College
- Griffith College
- Hawthorn-Melbourne
- Health Skills Australia (HSA)
- La Trobe College Australia
- La Trobe University Sydney Campus
- New College of Public Safety (NCPS)
- Newcastle International College (NIC)
- SAE Institute
- South Australian Institute of Business and Technology (SAIBT)
- Sydney Institute of Business and Technology (SIBT)
- University of Canberra College (UC College)
- Western Sydney University International College (WSUIC)
- Western Sydney University Sydney City Campus
- UC International College (UCIC)
- Fraser International College (FIC)
- International College of Manitoba (ICM)
- Richard Bland College of William & Mary Global Student Success Program (RBC GSSP)
- UMass Boston Navitas Global Student Success Program
- UMass Dartmouth Navitas Global Student Success Program
- UMass Lowell Navitas Global Student Success Program
- University of Idaho Global Student Success Program (Idaho GSSP)
- University of New Hampshire Global Student Success Program
- Virginia Commonwealth University Global Student Success Program (VCUGSSP)
- Birmingham City University International College (BCUIC)
- Cambridge Ruskin International College (CRIC)
- Hertfordshire International College (HIC)
- International College at Robert Gordon University (ICRGU)
- International College Portsmouth (ICP)
- The College, Swansea University
- Curtin Singapore
- Murdoch University Dubai
- The Hague Pathway College (THPC)
- Twente Pathway College (TPC)
## CEG
- ONCAMPUS Birkbeck – University of London
- ONCAMPUS Coventry University
- ONCAMPUS Goldsmith- University of London
- ONCAMPUS London South Bank University
- ONCAMPUS Queen Mary University of London
- ONCAMPUS Royal Holloway University of London
- ONCAMPUS Royal Veterinary College- University of London
- ONCAMPUS The Courtauld Institute of Arts
- ONCAMPUS University of Central Lancashire
- ONCAMPUS University of Hull
- ONCAMPUS University of Reading
- ONCAMPUS University of Sutherland
- ONCAMPUS – Amsterdam University of Applied Science
- ONCAMPUS Lund University
- ONCAMPUS- University of Amsterdam

Universities with Good Pathway Programs

Learn all about how pathway programs like INTO, Navitas, CEG, and Study Group, can help your career. Register with us for more details including eligibility.

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Opt for Pathway Programs Overseas

These programs are mainly offered for the students for the reasons mentioned below: 1. A student who has not met the minimum requirement or who has a low GPA in Bachelors for a particular program, and who have scored less in IELTS/TOEFL/PTE for those students who have missed the application deadlines. 2. If the student is a working professional, having 1, 2 or up to 5 years of experience and has no time in taking up GRE. 3. The student has appeared for GRE but has scored very less and does not want to reappear again. 4. If the student is very keen about the USA and has only 3 months of time for a particular intake to start, the student can directly start the application process, and can expect to receive the admissions decision within 4 weeks and can proceed with the visa filing by scheduling or applying for emergency visas without any additional cost. 5. If the student is not willing to pay the application fees or courier charges. 6. These pathway programs are also applicable for bachelors students, who have not taken up ACT/SAT or have scored very low marks..

There will be 2 conditional admits for the pathway programs: Condition 1: The student has to maintain a cumulative GPA of 3.0-3.5 out of 4 in the first semester of their program, Condition 2: Certain programs such as MIS or Computer Science or Computer Engineering programs do require GRE. For these programs, the student gets 4 to 6 months time to take up the GRE, while studying their master’s program and the scores should be submitted once the student has completed the first semester.

Most of the universities in the US, which offer these pathway programs are of Tier 1 and Tier 2 universities. Getting visas should not be a problem if the student is confident in answering the visa officer about the pathway program. And almost all the programs come under accreditation i.e for Engineering and Technology it comes under ABET (Accreditation Board of Engineering and Technology) and for Business Schools it comes under AMBA or AACSB (Association Of MBA, and Association to Advance Collegiate Schools of business).

Once the student gets into the pathway program, an academic advisor will be assigned to the student, who can help the student in their academic profile building and also helps in building resumes and keeping a track of the student’s performance.