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Across the data science field, professionals with a master’s degree are eligible for lucrative, in-demand careers. According to the Bureau of Labor Statistics, computer and information scientists earn an annual mean wage of $122,840, and these positions are projected to grow by 16% between 2018 and 2028; both of these numbers are significantly higher than the national average for all jobs. Data science professionals are also in demand in manufacturing industries such as navigational, measuring, electromedical, and control instruments.
The benefits of a master’s degree across the data science field are undeniable. Data science graduates with a master’s degree are eligible for more lucrative, in-demand jobs—they tend to earn higher paychecks and they’re projected to enjoy more career stability than those with just a bachelor’s degree. There’s no denying it – a master’s degree is what you need to excel in the lucrative data science field.
The program at Drexel University is housed within the university’s College of Computing and Informatics.
There are three elective tracks in the program: Analytics, Mining and Algorithms, Visualization and Communication, and Management and Accountability. The topics covered in each track include Machine Learning, Human-Computer Interaction, and Privacy. The program finishes with a capstone project where students can apply their learning to a practical, real-world project with a total of 45 credits.
IIT’s Master of Data Science program, in keeping with its status as a leading technical school (ranked by US News & World Report for Engineering, Computer Science, Math, and Physics), prepares students from various backgrounds for careers in data science with rigorous theoretical and practical training.
Technical subjects are not the only things covered in the courses, as communications, project management, and ethical practices are also included.
In addition to the program of study, the capstone practicum involves working with companies throughout the Chicago-area in various industries. This program is available both online and part-time, making it accessible to anyone who needs to keep working while obtaining a degree.
In the University of Missouri’s Data Science and Analytics Program, students are part of a cohort-based program that emphasizes campus community through on-campus executive sessions each year, along with weekly cooperative tasks.
Courses are taken two at a time in eight-week online modules designed to build upon one another up to 34 credits total. The Industry Advisory Board is a partnership between companies such as Boeing, IBM and The New York Times that provides students with networking opportunities.
Columbia University’s MSDS degree can be completed part-time or full-time. It includes 21 credits of required and core courses, as well as a capstone project.
All required courses cover the usual topics (e.g. machine learning). The capstone consists of a semester-long data science project sponsored by a faculty member or a local organization. Students may choose to study entrepreneurship or to explore a subject in Columbia’s Data Science Institute. These diverse research centers work on everything from health analytics to cybersecurity to smart cities and new media. There are also working groups on frontiers in computing and materials discovery analytics.
This MSDS sets itself apart by offering two distinct paths for students: the technical and the decision-maker paths. The technical curriculum includes courses in topics like analysis of algorithms, cloud computing, and security for networked systems.
The decision-maker track focuses on project management and managing organizational change through data. You can earn the degree on campus, online, or in a hybrid format. 12 credits are completed online, while 18 credits are completed on campus in the blended format.
Bloomington has a strong reputation in computer science and quantitative analysis, and there are plenty of research opportunities available to students. The School of Informatics & Computing operates 15 research centers, including those that deal with bioinformatics, cybersecurity, social informatics, and more.
The Johns Hopkins data science master’s degree program is a rigorous program that focuses on topics in both mathematics and computer science.
Most of the 10 total courses are offered online, so students could complete the degree entirely off-site. This program will provide students with a solid grounding in both data science theory and applications.
The Northeastern University data science master’s degree consists of 32 semester hours and prepares students for work in data science or to begin a doctorate program.
Northeastern’s core curriculum is supplemented by a large number of elective courses from partnering colleges as well as departments at Northeastern. Northeastern offers master’s students the opportunity to participate in a co-op program. Through this program, students with up to 12 months of professional experience can apply their knowledge outside the classroom.
The MAS program is available for early- to mid-career data professionals who work with massive datasets.
This might mean people who have a technical leadership track already or those who are considering a shift to data science. Courses are offered alternate weekends, on a Friday/Saturday schedule, with instructional materials available online. Most students are experienced engineers with two years of experience.
This career-focused program offers a basic knowledge of data science fundamentals, including programming, analysis, and systems, as well as machine learning. Students in their second year will be expected to work in teams to design a data science project, write a final report, and explain their findings in an oral presentation. A demo of the working prototype may be presented as well.
The UCSB Data Science 42 credit master’s degree is included in the Master of Arts in Statistics program.
This degree can be the first step towards obtaining a doctorate, or to a career in data science. All students are expected to complete a Data Science Project either assigned or chosen by themselves. This program offers a solid foundation in statistics along with the computational tools for applying these statistics to real-world data.
A master’s degree in data science at University of Michigan exposes students to the statistical and computational methods required to identify relevant datasets and draw insights from them.
There will be 43 credits offered in topics including machine learning, database management, and applied regression.
Students will choose a capstone course in one of eight practical topics from epidemiology to statistical consulting at the end of the program.
This professional master’s program emphasizes teamwork and integration. In the initial semesters, students take courses in languages, computation, and linear modeling.
Once students have mastered the basics, they can proceed to practice, application, and the all-important capstone project.
UVA’s Data Science Institute enables graduate students to work on interdisciplinary big data projects, take advantage of training programs and high-speed computational resources, and participate in data integration, systems biology, ethics, and law related research. The University of Virginia hosts Datapalooza every year, a celebration of data-driven research, resources, and outreach.
UW’s accelerated Master of Statistics program targets working Wisconsin professionals—especially those who wish to move up the leadership or career ladder.
Curriculum includes stats courses, models, complex data analysis, visualization and communication skills. During the summer semester, students accomplish a practicum focused on change management within a complex organization. This kind of degree packs a lot into a short span.
You can also explore the more flexible online MSDS. No matter if you live in Wisconsin or out of state, tuition is held to a flat rate, and courses have no set meeting times. All the usual hot topics in data science are covered in this program – statistical methods, data mining, prescriptive analytics, etc. Its Virtual Lab offers free access to software tools and programming languages including R, Python, SQL Server and Tableau.
The Bay Path University program offers two tracks to meet the needs of all students.
In the generalist track, you will receive a well-rounded education in data science that will help you to be a valuable member of an applied data science team. A specialist track takes a deep dive into algorithms, their theory and applications.
Students on all tracks participate in a comprehensive curriculum, including a capstone project in which students apply what they have learned to earn 36 credit hours.
Cabrini University offers a master’s degree in data science that covers four essentials areas: software and programming, business and management, data warehousing and visualization.
Among required training courses are topics such as data visualization, machine learning, natural language processing, and project management. The program culminates in a capstone or internship in which students are able to put their knowledge into practice. 36 credit hours.
With Carleton College’s Data Science Master’s program, graduate students can concentrate in data science in one of the 13 participating master’s programs.
Data Science training is provided to students in many disciplines by this multi-department collaboration. It accepts 40 students a year from various departments in this competitive program. Students enrolled in this specialization will complete a thesis, coursework, or a final project depending on the Master’s program. The Business Analytics MBA program also requires internships.
For more than a decade, the MCDS has been exploring the design, engineering, and deployment of very large information systems. Hands-on experience is important here.
There are scores of research projects underway at CMU, and students are expected to contribute. You can work individually or in a team on your capstone project. You can also get a summer internship from companies like Apple, Amazon, eBay, Google, UBS and more. Experience in the field is not required for admission, but relevant work is highly valued.
A Graduate Program in Statistics (MPS) at Cornell is a Professional Science Master’s (PSM) – an accreditation for programs that combine advanced training in science and math with highly-valued business skills.
All students take common core courses (such as applied stats), but Option II students focus much more on data science.
Cornell’s program relies heavily on a two-semester-long MPS project with an industry partner. Past large-scale data analytics projects have included a collaboration with a health company to create a system that offered personalized article recommendations for anonymous users.
Working with comScore to get a sense of users’ ages and genders through browsing activities. It’s the kind of degree graduates need in order to work in government or industry as a statistician.
The Duke program offers students an interdisciplinary education in data science in 42 credits.
Core courses like machine learning and data visualization are paired with electives from Duke’s many graduate programs. Moreover, the degree includes a practical year-long capstone during which students collaborate closely with a non-academic stakeholder and a Duke professor to complete a project.
In Harvard’s three semester, 12-course program, all the skills needed to apply data science to any field are provided.
Students learn skills such as building and understanding statistical models, visualizing and communicating data, developing computational pipelines using commonly used tools, collaborating with a team, and analyzing the results. The degree includes one research project or semester-long independent study, one in which students are able to apply their knowledge and skills.
In today’s digitized economy, data science is a basic skill that is invaluable to successful business. It’s unlike any other field in that it merges technology, math, business, and even artistry to help a business improve profitability and drive growth. Businesses need data scientists who can capture and analyze data, and translate them into actionable insights.
Data science is the discipline of extracting meaning from data to reveal trends and patterns that may not be immediately obvious. It involves gathering data, cleaning, transforming and bringing it to life using visualization. The result is a powerful story: one that’s backed by the numbers.
The Admissions Requirements for Data Science programs are very similar, as there are a lot of data science degrees that have already been defined by the academic community. Personal touches may differentiate programs, but in general you will need to have some background in statistics, mathematics, or computer science. In addition to your academic preparation, you will be asked to submit references and writing samples demonstrating your ability to conduct research and create meaningful outcomes.
To ensure that you do not miss out on the opportunity to gain acceptance into a graduate level data science program, it is important to check the school website for updated information regarding GRE/GMAT scores. Most schools offer at least a master’s degree in data science, although some may offer shorter programs or certificate programs.
Electives are an enjoyable and necessary part of the NYU data science program. When it comes to picking electives, students will need to figure out what type of data science they would like to pursue. Electives are available in different areas which include 1) Statistics for Data Science 2) Computer Science for Data Science 3) Business for Data Science 4) Financial Technology & Tech Entrepreneurship 5) Health for Data Science 6) Sales, Marketing, Operations & Advertising Analytics .
A lot of data science master’s programs last between 18-24 months of full-time coursework. The two biggest factors here are the intensity of the program and the availability of financial aid.
Data scientists and analysts are big data experts toiling in statistical analysis, modeling, predictive analytics, and machine learning. Using these skills to analyze large amounts of data, they’re able to identify trends and patterns that can provide insight into business operations. To do so, data scientists use a wide variety of specialized tools, including programming languages and complex analytics software. Often working alongside statisticians, physicists, mathematicians, and programmers, they can be employed by industries such as healthcare, banking, finance, insurance and government.
Average Salary: The national average salary for a Data Scientist is $115,323 in United States.
Machine learning scientists develop methods for analyzing data and improving the user experience. Data is often collected from users through sensors or software that runs on phones, tablets, and other devices. Once that happens, machine learning algorithms determine ways to personalize app features and the range of ads people see within apps. Machine learning scientists might work in research and development teams to develop new algorithms. Or they might work individually as consultants or independent researchers, studying videos and images of humans to help computers better understand human language.
Average Salary: The national average salary for a Machine Learning Scientist is $133,627 in United States.
Machine Learning Engineer methods for analyzing data and improving the user experience. Data is often collected from users through sensors or software that runs on phones, tablets, and other devices. Once that happens, machine learning algorithms determine ways to personalize app features and the range of ads people see within apps. Machine learning scientists might work in research and development teams to develop new algorithms. Or they might work individually as consultants or independent researchers, studying videos and images of humans to help computers better understand human language.
Average Salary: $128,373 / yr
Applications architects are usually responsible for the overall design and development of a business’ applications that are used by users to carry out their jobs. This includes not only looking at the design of what the applications look like but also how well it works together with other applications that are used in the business, and how it interacts with users to achieve their tasks. Apps architects also work on designing and building components that can help make sure applications can carry out certain tasks. These architects are usually focused on keeping up with the latest technology in an industry they’re working in so they can be a resource for developers and other employees within their organization.
Average Salary: $133,627 / yr
A data architect is a position that’s usually filled by an IT professional with more than ten years of experience. These engineers are tasked with creating data models, business rules, and database structures to create a database that’s scalable and flexible enough to meet the needs of any possible future development. They’re also responsible for researching trends in database programming, and ensuring that all technologies are secure. In addition, they oversee that all business systems are working optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.
Average Salary: $114,836 / yr
Business intelligence (BI) developers are business and technology professionals responsible for the development, implementation, and maintenance of business intelligence platforms that facilitate streamlined decision-making. Data analysts, data architects, or project leaders work with BI developers in order to understand their needs and provide them with the tools they require to build powerful visualizations and analytic applications.
Average Salary: $89,957 / yr
Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. Data analysts also aid in the decision-making process by preparing reports for organizational leaders which effectively communicate trends and insights gleaned from their analysis. Strategies looking at the big picture are important as data analysts must help their company to understand its strengths and weaknesses while anticipating changes that require them to adjust certain strategies. For example, if a company has been able to use Facebook targeted ads effectively, then a data analyst may suggest placing more of an emphasis on social media marketing instead of investing in online advertisements across other platforms.
Average Salary: $68,091 / yr
Data engineers are responsible for building, maintaining, and enhancing the systems that transform raw data into aggregated information. They create tools and processes that allow multiple departments to access relevant data at the right time from the right place. They work with multiple stakeholders across an organization to organize data flows and perform batch/real-time processing. Data engineers also have a deep understanding of a company’s analytic needs to troubleshoot pipeline failures.
Average Salary: $111,410 / yr
An enterprise architect’s vision and task is to manage and align the evolution of an enterprise’s strategic business objectives with the IT systems required to execute upon them. Enterprise architecture is focused on defining how information technology can help the business to achieve its goals, whereas project architecture or technology architecture focuses on realizing a specific solution that has been committed to.
Average Salary: $114,836 / yr
Statisticians are hired to provide critical analysis and valuable insight into the success of a wide range of businesses. The work of statisticians is extremely diverse, and involves being able to accommodate new information and changing goals. Statisticians study data both in its raw form as well as after experiencing changes through the application of mathematical models and inference tools.
Average Salary: $86,759/ Year
Earning a master’s degree in data science can prepare learners for a variety of lucrative career opportunities in the data science field. Graduating students can pursue careers as statisticians, business intelligence analysts, data analysts, big data engineers, database administrators, data architects, and machine learning engineers.
Data scientists are required to hold a master’s degree, although some pursue doctoral degrees in computer and information science. Students should research the different master’s programs in data science to find the program that is best suited to their educational needs and professional goals.
Data science professionals with advanced degrees receive higher salaries and career advancement opportunities. A master’s degree provides specialized training in a particular field, building on a student’s previous knowledge.