MS in Data Analytics
An Overview of the Degree
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 is an ideal coursework for graduates aspiring 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 graduates are not aware of the wide range of opportunities that this course has to offer apart from a glowing career and great salary.
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Is the Masters of Science in Data Analytics Program worth it?
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.
Few of the elective courses which a student studying MS in Data Analytics can choose are:-
Duration of MS in Data Analytics Program
The course is usually divided over 3 or 4 semesters with a mandatory project and internship in most of the schools.
How to decide if a program is good?
2) Industry Collaborations
3) Class Profile
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 the 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.
Which test scores are required?
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
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 take 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.
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.
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.
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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