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.

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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.

Machine Learning

Machine Learning is aimed at the automation of decision making. It rests on the theoretical premise that computer and AI systems can identify patterns of data and make useful decisions on their own. It is based on powerful algorithms that are aimed at anticipating repeated patterns and taking actions accordingly. A Machine Learning program is based heavily on training in complex algorithms. Data mining and analysis are aspects that would be touched upon in the coursework. Deep learning, pattern recognition and behavioural imaging are other important components. The coursework can be considered complete if it covers probabilistic graph models and stochastic modelling. Machine Learning experts are absorbed by leading tech companies as Machine Learning Developers and Machine Learning Engineers. They often work in close coordination with Data Scientists and Artificial Intelligence Engineers. 

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Artificial Intelligence

A master’s in Data Science with a specialization in Artificial Intelligence (AI) trains the students in developing systems that can respond to newer and more complex situations. AI is aimed at replacing human thinking in analysis and decision making so that human intellectual energies can be devoted to higher-order-thinking tasks. Some of the main components of the coursework include probabilistic modelling, knowledge representation, natural language processing, deep networks, logic programming, cognitive computing and agile software systems. AI experts find jobs as AI consultants and experts. They may also be hired as robotics programmers and video game programmers. 

Business Analytics

Business Analytics involves the use of Data Science for solving critical problems faced by businesses. The predictive use of data analysis lies at the core of this specialization. It trains the students to use advanced methods, techniques and software to maximize financial performance, aid strategic management of business processes and induce operational efficiency in day-to-day activities. The most important course components include enterprise analytics, customer analytics, optimization methods and analytics software (R, Python, SQL etc.). Master’s graduates in Data Science with Business Analytics specialization are hired as Business Analysis Manager, Quantitative Analyst, Business Data Analyst, Operations Research Analyst, Market Research Analyst and Financial Analyst etc.

Big Data

A master’s program in Data Science with a specialization in Big Data is aimed at creating experts that can identify, manage and analyze huge volumes of data by creating relevant tools that work with it so as to produce knowledge. This meaningful information is then used to improve outputs and make the functioning of processes efficient. Big Data experts have to deal with huge databases. The essential course components of a Big Data specialization include analysis of scalability of algorithms, data warehousing, online analytical processing, big data programming models, visualization of data, social media analysis, distributed algorithms, data mining, scalable machine learning and cloud computing. Big Data experts are employed as Management Analysts, Operations Research Analysts, Business Analysts and Data Modelers.   

Global Rank

College Name Popularity Rank Global Rank Total Tuition Fees Deadline Annual RA+TA Unsecured Loan Offer Secured Loan Offer
Columbia University 1 18 96864 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
George Mason University 2 801-1000 40380 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
University of Southern California 3 132 61805 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Northwestern University 4 28 108240 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Georgia Institute of Technology 5 70 57136 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
University of California, San Diego 6 38 53088 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
University of Minnesota -Twin Cities 7 163 42000 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Michigan Technological University 8 551-600 34290 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Northeastern University 9 346 47070 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
University of Pennsylvania 10 19 86264 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
New Jersey Institute of Technology 11 801-1000 59920 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Harvard University 12 3 92680 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
The Johns Hopkins University 13 17 107480 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
University of Delaware 14 421-430 54810 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Illinois Institute of Technology 15 395 56700 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Wayne State University 16 461-470 42720 Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
New York Institute of Technology 17 NA NA Log in Log in Log in 1.5 Cr, Interest Rate starts @8.85%
Source: GyanDhan's analysis, partner data & university's website. While deadlines are latest, other figures are as of 2018. Double-check deadlines from the university's website

A greater number of organizations require expert Data Scientists with each passing day. The main roles of Data Science graduates are:

Data Scientist

Data Scientists have a number of responsibilities towards her or his organization. They usually begin by identifying the problems associated with the analysis of data. Thereafter, they go on to demarcate the data-sets and associated variables that can be most useful for their organization. Developing a method for the continuous collection of structured and unstructured data is another part of their job. They also need to ensure that the data is accurate, comprehensive and uniform for the tasks at hand. Designing models and algorithms for the storage, maintenance and analysis of data is also one of their chief responsibilities. Finally, they have to figure out the patterns and trends in the data that can be used for predictions before they can be used for building solutions.

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Data Analyst

The role of Data Scientist is more technical and mathematics oriented while that of a Data Analyst is more business oriented. Unlike Data Scientists, who work to build computer based processes that work with data, data analysts develop performance indicators, create visualizations of data for easier understanding, and use their business intelligence to make the most out of data by working with data analysis tools such as SQL, SAS, Google Analytics, and Tableau etc. Data Analysts can be said to be the bridge between the technical aspect of data science and its business aspect. Their job involves in-depth analysis of data so that it fits the goals and requirements of the organization. Data Analysts identify the use of data, whether it is required for market research, operations research, logistics and minimizing the costs, etc. They conduct various types of diagnosis that can be descriptive, diagnostic, predictive or prescriptive. 

Data Engineer

Data Engineers are concerned with the infrastructure that enables the tasks of the entire team working with data. Their role involves the building and maintenance of data architecture. Their central responsibility is extraction, transformation and loading (ETL) of data. They have to work in tandem with data analysts and data scientists and ensure that their work is made easier. Data Engineer’s job is foremost and fundamental to the predictive use of data so that the workflows of the entire project can be  managed. Command over big data tools and programming in Java, SQL and Scala is a great advantage for this role besides an in-depth knowledge of data structures and algorithms. 

Machine Learning Engineer

Machine Learning Engineers are typically programmers that have command over AI. Their job is to create algorithms and programs so that computers can respond to and perform specific tasks on their own. Their main focus is on automating as many processes as possible so that human effort can be used for tasks that require deeper analysis and predictive thinking. They can be understood as software engineers working with big data. They have to design systems that can scale increasing volumes of data. Data modeling and evaluations are their other core responsibilities. 

Global Rank Employability Range Salary Range
1-10 Log in Log in
11-25 Log in Log in
26-50 Log in Log in
51-100 Log in Log in
101-200 Log in Log in
201-500 Log in Log in
500+ Log in Log in
Source: GyanDhan's proprietary analysis
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