Masters in Data Science

Data Science: The Most In-demand Master’s Degree in the Times

Data Science is undoubtedly the most promising career field for engineers these days. We live in an age of information explosion. The management and analysis of data amidst the data deluge for bettering the outputs of enterprises and organizations is becoming increasingly challenging with each passing day. It was precisely this sort of a challenge that led to the coining of the term and the emergence of this interdisciplinary area of study in the last few decades. Typically, data science is the confluence of computer science, statistics, mathematics and management, oriented towards observing, analyzing and organizing data such that it helps to minimize the gap between the actual phenomenon and human understanding.
Data Science is one of the youngest disciplines, and there has been considerable controversy over its status as a separate discipline. It was said to be rooted in the business-oriented use of statistics which led many experts to claim that Data Science is merely a voguish term for business analytics. However, of late, the technological and business fraternities have come to a consensus that Data Science is a separate discipline that is oriented towards finding patterns that can be used for predictions. There is no wonder that Data Science has flourished as one of the most popular and useful fields in the last few years, mushrooming into a number of more specialized sub-disciplines. Today, every leading university has a separate department that offers Masters in Data Science courses and strives to be a leader in Data Science research. Unsurprisingly, no company or public institution can ignore the benefits of employing specialist data scientists today.

Some of the main concerns of Data Science and its subfields that separate it from similar disciplines like Business Analytics, Statistics or Computer Science (programming and algorithms) are the following:

• Data Science concerns itself with the capture, maintenance, processing, analysis and communication of data to maximize the efficiency of processes.
• Pattern detection, grouping and prediction are at the heart of Data Science.
• Automation of decision-making for the generation of a prediction-engine is one of the chief functions towards which the Data Science is geared to.
• Recommendations and forecasts based on data analysis are central to a Data Scientist’s responsibilities.
• The robustness and potential of this fast-growing field can be gauged further by understanding what a Master degree in Data Science can offer.

4 Things That a Data Science Course Must Have!

Data Science is aimed at creating knowledge from big data. Even though it is a fast-expanding field, there are a few essentials that every Data Science course structure must possess.

Statistical and Computational Methods: These are the foundation of Data Science. They involve the practical application of statistics and computational sciences by the use of calculus, matrices, probability, algorithms and analysis of variance.

Data Mining: This is the first stage in the process of creating knowledge out of vast databases by making data more usable for further study. It aims at gathering categorized data and finding data patterns from huge databases. One may say that data mining is to data analysis of what chewing is to the process of digestion. It involves the use of artificial intelligence, management of virtual data rooms, machine learning and visualization of data.

Big Data: This portion of any MS in Data Science program involves the handling of databases. It is aimed at data exploration, finding parallel data, visualization of data, analysis of graphs, and finding relations between data. It involves the use of SQL (Structured Query Language) programming language and algorithms.

Options to acquire in-demand Data Science skills that are gradually becoming the norm in the list of master degree courses in Data Science: One can make the most out of a Master degree in Data Science only when some of the in-vogue electives are chosen. Deep Learning, Natural Language Processing, Human-Computer Interaction and Network Analysis are some of them. Besides business, policy-making and scientific research, Data Science is making its impact felt in Biomedical Science. Application of Data Science in Biomedical Science is fast becoming the best course for MS in Data Science.

What are the Requirements for a Masters in Data Science?

Most universities offer master’s in Data Science as either a Master of Science in Data Science (MS) or a Master of Engineering in Data Science (MEng). One must check the exact Masters in Data Science admission requirements before applying. However, the following list could be of great help to check the common requirements for eligibility for Masters in Data Science Engineering or Masters in Data Science.
Bachelor’s Degree Requirements: Data Science involves extensive use of statistics, mathematics, programming and algorithms. Therefore, students who have a Bachelor’s degree in mathematics, statistics or computer science would be best suited for a master’s in data science, besides, of course, Data Science bachelors . However, with the engineering education becoming increasingly interdisciplinary by the day, students who have done prerequisite mathematics and computer courses at the bachelor’s level would also be eligible to apply. Usually, these include:
Quantitative Coursework: Linear Algebra, Probability, Statistics etc.
Computer Programming Coursework: Python, Java, C+ etc.
Some universities entertain applications from all graduates from science background and offer bridge courses before the start of master’s program. Besides, many universities ask for 16 years of formal education for masters. Hence, this condition should be kept in mind.
Other Requirements:
TOEFL and IELTS: Unlike many other engineering disciplines that had their roots in Europe, Data Science emerged from the US. It is a program that is mostly offered in English as a medium of instruction which make competitive IELTS and TOEFL scores an important requirement for admission. One should check the cut-off scores before applying.
GRE: Even though the GRE is not a prerequisite for applying for a master’s in Data Science, because it requires a strong base in quantitative methods, most universities prefer applications that come with high GRE quantitative score component. This does not mean that average Verbal or Writing section scores would pass unnoticed. Mathematics is the GRE test subject for prospective master’s students of Data Science.
Undergraduate GPA: It is usually higher in the case of Data Science than in most engineering and science master’s courses. Leading Data Science departments might ask for a minimum of 3.5 GPA or even higher.
Letters of Recommendation and Statement of Purpose: Usually, a university might ask for at least three letters of recommendation and a statement of purpose. However, some universities accept applications without them.