Master's in Data Science and Data Management

A master's degree in data science most often attracts individuals with a strong background in both mathematics and computer science. Most universities will expect students to have already taken courses in mathematics, statistics, and computing, specifically in subjects like linear algebra, probability, and programming to take advantage of all the program has to offer.

Nevertheless, most will offer bridge courses for students who do not meet this requirement exactly. Most, if not all, undergraduate degrees in STEM fields (Science, Technology, Engineering, and Mathematics) prepare students with the foundational knowledge necessary to embark on a master's degree in data science. The following are just some of the undergraduate degrees students possess.

• Bachelor of Science in Computer Science
• Bachelor of Science in Statistics
• Bachelor of Science in Mathematics
• Bachelor of Science in Systems Engineering

To be eligible for admission to a master's degree in data science, students must have previously completed a four-year bachelor's degree from an accredited institution. Most graduate programs will expect students to possess some knowledge of the field, providing a list of subjects and equivalent credits. In the case of data science, this will most likely include linear algebra, calculus, probability, and computation. Some universities will list it as an immovable prerequisite, others with the understanding that students who do not meet these criteria will enroll in the necessary courses upon gaining admission. These are the admissions requirements candidates for almost any data science master's degree may be expected to provide.

• Transcript of Records
• Letter of Recommendation
• CV
• GMAT or GRE test results
• Interview
• Personal Essay or Letter of Intent

Data science is a quickly expanding field, as big data, machine learning, data architecture, and database management become critical in solving the coming challenges for many industries. Countless sectors, even outside the tech world, are looking to implement data sciences as a way of improving existing pipelines and communications strategies.

The U.S. Bureau of Labor Statistics expects employment in computer sciences to grow at a rate of 11% between 2019 and 2029. As a technical field, however, data science requires expertise that is likely not achieved simply by studying for a bachelor's degree. Those who want to partake in the industry's projected growth should therefore consider studying for a master's degree in data science.

One way to help you choose between master's in data science programs is whether or not they have STEM distinctions. Studying in a STEM program will help to distinguish you from other job applicants, attesting to the quality of education you received to potential recruiters in the industry will be looking for.

Another important factor to consider when applying for a graduate program is what type of culminating experiences are offered. Some will encourage students to work on a thesis, which is an opportunity to delve deep into a subject they are passionate about but may also be time-consuming; many working professionals balancing a master's degree with work may not be able to commit to writing a thesis. They may instead be interested in a university that offers a practicum or real-world project in an organization of their choosing as the graduating requirement.

A master's degree in data science will usually be structured to combine both core and elective courses. In these foundational courses, students have a chance to become familiar with subjects such as statistics, machine learning, database management, data engineering, etc.

Along the course of the master's program, students can explore subjects that interest them the most with elective courses. The careful selection of elective courses can even set a particular career path in motion, as students gain further specialization in a specific field of data science or in its application into health, social science, finances, or engineering. To help round out the curriculum, many universities encourage students to embark on real-life projects, internships, or practicums as a graduating option.

Data Science Foundational Courses

The following is a sample of the foundational courses students may take during a graduate program in data science.

• Probability, Statistics, and Machine Learning: These courses are designed to help students become familiar with the mathematical models involved in data science and machine learning. With concepts such as conditioning, common distributions, confidence intervals, regression, among others, students become adept at exploring, visualizing, and applying complex mathematical models to their work.
• Data Engineering: This course typically delves into algorithms, data structures, and programming and their applications for computing.
• Database Management Systems: These courses aim to help students become familiar with the ways in which databases can be built for efficiency, safety, and usability. They learn about concepts such as database schemas, standard SQL statements, and relational algebra.
• Statistical Data Mining: Students typically learn about data mining, inference, and prediction, with supervised learning as its ultimate goal. Through supervised learning, students become adept at handling systems that receive input and interpolate the data to predict an outcome.

Data Science Specialist & Elective Courses

The choice of elective courses may help a student learn more about data science in different contexts, such as health, enterprises, finances, social sciences, etc. With this knowledge, students can better apply their skills in data management to specialized work. Others may choose to use their elective courses as an opportunity to learn more about up-and-coming technology in the field. Many universities encourage students to design their curriculum with the help of a faculty advisor. The following are some examples of elective courses one may find in a data science graduate program.

• Cybersecurity Privacy and Ethics
• Data Science Applications in Health, Finance, Engineering
• Data and Society
• Deep Learning

Licensure & Certification in Data Science

To find work in the field of data science, you do not need a license. However, many employers are interested in candidates who have earned certifications that can attest to their skills in particular areas of data management.

The most widely recognized certification is the Chartered Data Scientist, a distinction issued by the Association of Data Scientists. The certification is earned after passing an exam covering the foundations of data science.

The Data Science Council of America also issues a number of certifications for professionals, including:

• Big Data Engineering Certification
• Big Data Analytics Certification
• Senior or Principal Data Scientist

Having any one of these certifications can help distinguish you from other job applicants in a highly competitive profession.

Those interested in continuing their education after completing a master's degree in data science can enroll in a doctoral program. In a Ph.D. in Data Science program, students choose a particular topic that interests them and carry out extensive research on it. Oftentimes, this research sheds light on new areas of data science at the forefront of innovation. Some of these may be directed towards research into artificial intelligence, machine learning, cloud-based systems, and predictive algorithms. Doctoral candidates work under the guidance of a faculty advisor to arrive at a doctoral thesis of enough quality to be published in a peer-reviewed journal. Earning a Ph.D. prepares individuals to lead research teams and become teachers up to a graduate level.

Professional organizations in the world of data science bring together peers and colleagues at both a national and international level. These organizations help set industry standards, ethical practices, and corroborate an individual's expertise in the subject. Many host networking events that incentivize cooperation between members of the same industry. Members of a professional organization may also take part in conferences and other opportunities for continuing education. These are some of the most prominent professional organizations dedicated to data science and adjacent fields.

• Data Science Council of America : Apart from serving as an accrediting body for university programs, DASCA offers a number of certifications for its members. DASCA also hosts seminars and conclaves as a platform for innovative up-and-coming ideas in the world of data science.
• Association of Data Scientists : ADaSci is an international organization that provides members with access to certifications, webinars, and learning courses, a robust career center tool for those looking for employment, and a peer-reviewed journal concerning the latest developments in the field.
• American Statistical Association : One of the oldest and most respected professional organizations, the ASA has expanded to bring together statisticians and data scientists. With chapters all around the country, members have access to an interconnected community of like-minded peers, special discounts, two ASA journals, and learning opportunities.

Earning a master's degree can open many possible career paths in data science. The following are some examples.

• Machine Learning Engineer: A machine learning engineer develops algorithms that in turn help automate processes and reduce error through iterative machine learning. They can work in a multitude of industries, improving production lines, advancing user interactivity, or refining automated medical diagnosis systems.
• Data Center Manager : Data center managers head the teams responsible for implementing, maintaining, and upgrading servers and other equipment at a data center. They oversee a group of data processing technicians that work to ensure the proper functioning of complex information systems.

• Data Management Analyst : Data management analysts supervise an institution's information systems, ensuring that data is both secure and accessible to members of said organization. They help design and maintain the network systems that make internal communication possible.

Job Outlook for a Master's in Data Science

As part of a fast-growing industry, jobs in this field are expected to increase. Many can find work in the technology sector, helping push the boundaries of what is possible in the field. Others can work within corporations, institutions, and organizations to manage and protect information. The US Bureau of Labor Statistics predicts that employment in computer sciences will grow at a rate of 11% between the years of 2019 and 2029. This rate outpaces the BLS's prediction for all occupations, which is projected to grow at an average of 4% during the same time frame. One of the fastest-growing industries where those with a master's degree in data science may find work in the healthcare sector, where growing IT-related needs increase the demand for expertise.

How to Become a Machine Learning Engineer

A machine learning engineer will typically begin their career by studying a bachelor's degree in computer sciences, systems engineering, mathematics, or a related field. There they will acquire an understanding of algorithms, databases, programming languages, and other essential tools they will need to find employment within the industry. Entry-level jobs in the technology sector typically require a bachelor's degree, and will likely focus on data processing, database design, or coding, possibly with a focus on machine learning algorithms.

Eventually, positions in computer science and information research will require further specialization in machine learning and data science in general. To gain said specialization, many choose to study a master's in data sciences and similar fields. Finding work as a machine learning engineer may also require obtaining relevant certifications in data management and computer sciences to help set you apart from other job applicants.

The US Bureau of Labor Statistics predicts that employment in computer science research will grow at a rate of 15% between the years of 2019 and 2029. This surpasses not only the average for all occupations (placed at 4%) but the average for all computer occupations (at 11%). More and more industries are exploring machine learning as a way to make processes more efficient and seamless, explaining its considerable growth in the coming years. The BLS estimates that the median pay for computer research scientists is $126,830 per year.

How to Become a Data Center Manager

To become a data center manager, most professionals begin by studying a career in computer science, systems engineering, or electrical engineering. Work in data centers involves knowledge of both database management and hardware. Typically, professionals will find an entry-level position in a data center as a data technician, data analyst, or hardware maintenance.

As an individual grows their career, they may become interested in managerial positions. Many employers will be interested in candidates that have studied for a master's degree, possibly in data science. With a master's degree in hand, professionals show that they are prepared to tackle more advanced projects and gain more responsibilities. Further proof of that expertise may be a certification in data management.

The BLS estimates that employment of database administrators (data center managers among them) will grow at a rate of 10% from 2019 to 2029, a higher growth than all other occupations (estimated to be 4%). The median pay for database administrators is $98,860 per year according to the BLS.

The Free Application for Federal Student Aid (FAFSA) is an extremely valuable resource to find out what kind of financial aid programs you may be eligible for. By answering the questionnaire and completing the application, you are directed towards both federal and state aid that could help you achieve your goal of studying for a master's in data science.

Scholarships are another option to explore when looking for the funds to pay for a master's degree. The following are but a few examples of scholarships open to students interested in a master's in data science. Many scholarships may be used at any given college, but others will cater to one specifically. Be sure to check what options are available at your school of choice.

• ACM SIGHPC Computational and Data Science Fellowships : This scholarship is open to students accepted into a Data Science graduate program. The grant is specifically tailored for women or students from an ethnic background not typically represented in the industry.
• Ralph W. Shrader Graduate Diversity Scholarship: This scholarship is made available to full-time students (women or minority students) of STEM graduate programs in the US. Read their scholarship eligibility requirements for more information.