MS in Data Analytics
An Overview of the Degree
In today’s world, data rules us all. Our shopping habits, travel plans, entertainment experiences, education, etc. all run through the internet. And all these internet activities generate enormous amounts of data.
Since the late 1990s, companies have been working on utilizing all this data for a different purpose altogether. Big Data is a term used to refer to an amount of data that is so large that conventional database tools prove inept at handling it. Companies like Google and Amazon foresaw the potential of using this data and started investing in this domain. All this requires serious computational skills and training in analytical tools. This led to the creation of a new profession altogether – data scientists. Data Science is an amalgamation of Mathematics, Statistics, Analytics, and Computer programming.
A recent IBM study has even reported that there will be rapid growth in Data Science jobs, with 2.7 million new Data Scientist and Analytics jobs opening every year, by 2020. This only proves that Data Analytics is here to stay.
MS in Data Analytics
MS in Data Analytics is an ideal coursework for graduate aspiring students to progress in this niche domain of data analytics. It is very helpful to increase your potential and moreover combines concepts of mathematics, computing, engineering, and business to develop problem-solving skills.
Along with this, Masters of Science in Data Analytics prepares you to succeed in industries that deal with data such as banking, healthcare, government, insurance companies, and management by allowing you to develop and assess strategies. Also, irrespective of your previous academic background, this program accommodates a range of learners who are interested in the field of data analytics with either a technical or business-related aspect.
Due to the freshness of this profession, many graduate students are not aware of the wide range of opportunities that this course has to offer apart from a glowing career and great salary.
Some Position and their demand in data analytics field
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Is the Masters of Science in Data Analytics Program worth it?
For the students interested in enhancing their technical and problem-solving skills which are the priority requirements of the employers, the Masters of Science in Data Analytics is the right fit for you.
Also, for those who aim to implement independent research and analyze solutions in the field of data analytics, this program is a good choice.
Many universities send their graduates for advanced commercial research where they are given a proper research environment with a variety of resources available at hand.
Furthermore, Universities like Warwick have divided the program into two components namely the taught component and a dissertation. The taught component focuses more on acquiring expertise in technical skills. On the other side, the dissertation primarily focuses on the domain of research and application of complex topics with the assistance of industry workers.
Thus, this course can give you a great chance to build your network in the industry long before you actually join the industry.
All the schools catering to this program offer a wide variety of optional courses for aspiring students. Few of the elective courses which a student studying MS in Data Analytics can choose are:
Duration of MS in Data Analytics Program
The duration of MS in data analytics is normally between 18 to 36 months depending on the School chosen. Most masters in data analytics programs require students to complete about 30 credit hours of coursework and an internship or a capstone project.
The course is usually divided over 3 or 4 semesters with a mandatory project and internship in most of the schools. While a full time MS in data analytics program takes a maximum of 2 years, a part time program under MS in data analytics can go beyond 2 years.
How to decide if a program is good?
The first and also the most important step in choosing a program is deciding what you actually want to study. Most schools put up the entire curriculum on their websites which you can go through. Usually, the coursework is divided into two sections – core courses and electives. Going through these would give you an idea of what the program is all about and you can choose accordingly.
2) Industry Collaborations
Data science is a highly application-oriented field. Hence, you require a good amount of practical training. This training could come in the form of internships, capstone projects, research collaborations etc. Hence, check out the list of companies and the kind of opportunities they are offering before choosing the program.
3) Current Skills and Experience
Apart from your expectations and requirements, your current status is also important. Do not choose a course which you see fit. Consider your current skills and evaluate them to know what actually is a better choice for you. Your current experiences should play a role in deciding your future experience.
4) Return on Investment
Let’s face it, higher education abroad is expensive. If you pay an exorbitant amount of fees and still end up struggling for a job, the situation is worrisome then. Hence, you should compare the total expenses and placements before choosing a school. These stats are readily available on college websites and would help you make a better decision.
Admission requirement for MS in Data Analytics programs
Students who aim to get admitted to a good school for the MS in Data Analytics must be comfortable with complex mathematical problems and have some knowledge about programming and basic computers.
However, those applicants who are not familiar with mathematics and computing can also get admission provided they take preparatory courses in Math and Computers before they join the class.
Some schools like NYU require you to complete courses in Calculus, Linear Algebra, Computer Programming, Advanced Physics, and Engineering. Also, you are required to have obtained the first degree to an international standard of 2:1. However, failing to have this, applicants with work experience in the industry are also considered equally for the course.
In general you need to have proper requirements like an undergraduate degree, certain test scores, and others. After that you need to submit online applications with necessary documents like academic transcripts, CV/Resume, LOR, Essay, passport copy (international students) and some departmental documents. Last round will be an interview round after which your whole application is evaluated.
The most important factor for getting admission in this course is to have 3 glowing letters of recommendation along with your essays and statement of purpose. The final selection is, however, based upon the comprehensive evaluation of your application. This includes the test scores, extracurriculars, and work experience. Any experience in the computer industry is an added advantage.
Which test scores are required?
The GMAT or GRE official test score is an essential element of a student’s application. All top universities demand a high score on these standardized tests to evaluate your mental and intellectual abilities.
Additionally, English Language Proficiency test scores are required along with the application. Some universities accept IELTS while others require TOEFL( minimum score of 100) for international applicants.
Class Size – 65
Median GMAT – 710
Median Quantitative GRE – 165
Age Range – 22 to 42
Average Work Experience – 1 Year
Average International Students– 45%
Background of student – Mathematics & Statistics (34%), Engineering (20%), Economics (16%) and Other Sciences (30%)
Some of the top employers hiring in this field are Facebook, Google, Amazon, eBay, Paddy Power, Capgemini, Mozilla, IBM
Pharmaceutical Industry: Janssen, Merck, GSK, J&J
Financial services Industry: Bank of Ireland, AXA, EY, Accenture, Deloitte, Citi, HSBC
Average MS in Data Analytics Salary
An entry-level data analyst with a Masters Degree might get an average salary around $92,500 per year. As experience increases, the average salary for a data analyst increases to higher numbers.
Also, here are some of the job profiles besides Data Analyst position that are offered post completion of the degree –
1) Business Consultant
2) Data Scientist
3) Marketing Analyst
4) Business Analyst
5) Analytics Consultant
6) Data Solutions Architect
According to the University of San Francisco, the median salary for a MS in Data Analytics Graduate with 1-2 years of experience is $110,000 per year and can reach upto a maximum of $130,000 per year.
More than 95% graduates receive an offer of employment with the first three months of graduation from this program.
Schools that offer MS in Data Analytics
Big data is growing bigger day by day. For example- Walmart collects over 2.5 petabytes (1015bytes) of data every hour from its customers’ transactions. Data is not only helping us make business decisions but rather dictating the company policies itself. In such a scenario, analytics is definitely here to stay.
Analytics as a domain has exploded primarily because it offers businesses to save on the three most important parameters – Time, Money and People. Use of analytics means less time taken to process data which translates into saving money and using fewer people for the same work.
However, now the data is becoming too large for even these frameworks to handle. Today, a humongous amount of data is being created every second and the traditional ways of doing analytics are no longer viable. Keeping data in a centralized location is no longer feasible and hence people are working on Analytics 3.0. It is essentially a combination of traditional business intelligence, big data, and the Internet of Things (IoT) distributed throughout the network. Companies like Google, Amazon, Facebook are heavily investing in analytics thus strengthening the faith in analytics as an essential part of every company.
The world of Analytics started in the 1990s with Analytics 1.0 which was mainly limited to Business Intelligence. Pre-defined queries and already structured data were mainly used for processing. During this era, more time was spent on structuring and preparing data as compared to actually processing the data for analysis. Then in the 2000s, the world evolved to welcome Analytics 2.0 or Big Data analytics. Big Data couldn’t be processed in time on a centralized platform and hence open source frameworks like Hadoop were created. The data that was processed was not only larger but also unstructured.
All in all, we can safely conclude that analytics is here to stay and its importance will only increase in the future.
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