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Johns Hopkins University – Whiting School of Engineering
Johns Hopkins University offers an online master of science in data science (MSDS) program in three different formats: online, online and on-site, and on-site. The program prepares students for a career in data science with real world examples and experience. The faculty members include practicing engineers and data scientists.
Admission requirements to the program include a bachelor’s degree from a regionally accredited college or university, a minimum grade point average of 3.0 on a 4.0 scale, official transcripts, and a completed online application form. GRE scores are not required to apply. Students with foreign credentials will have additional requirements to meet.
The program is made up of a total of ten courses that can be completed within five years. The curriculum strengthens students’ foundation in computer science, applied mathematics, and statistics. Courses include data science, data visualization, machine learning, algorithms for data science, principles of database systems, optimization, and computational statistics.
As part of this program, students become skilled in using computer science and mathematics to decode large sets of data, discovering relationships between different data sets, and creating models and techniques to solve real-world issues.
The University of Illinois offers an online master of computer science in data science. The fully online program trains students to glean useful insights from huge amounts of data using mathematics and computer science. Taught by faculty members with vast experience, the program is valuable for professionals looking to improve their decision making skills, using data to guide their decisions, and gather new insights. The program is delivered through the massive open online course platform, Coursera.
In order to apply for the program, students must have a bachelor’s degree in computer science as well as an undergraduate GPA of 3.2 or above. GRE scores are not mandatory but can be submitted for consideration.
The program comprises 32 credit-hours and focuses on the core areas of machine learning, cloud computing, data visualization, and data mining. Students pursuing the program delve into topics such as data visualization, database systems, introduction to data mining, cloud computing applications, methods of applied statistics, and applied machine learning.
On completion of the program, students are equipped to analyze big data sets, a and use mining and visualization methods to extract insights from data. Apart from pursuing various opportunities in data science, successful graduates can also go on to become data scientists.
University of California, Berkeley – School of Information
The UC Berkeley School of Information offers an online master of information and data science program. This program can be completed full-time, part-time, or in an accelerated format. While the courses are available online, students are expected to attend at least one in-person immersion of three to four days on UC Berkeley’s campus or another approved location.
In order to apply, students must have a bachelor’s degree from an accredited institution, a GPA of 3.0, GRE or GMAT scores, TOEFL scores for international students, considerable work experience, a statement of purpose, and letters of recommendation.
The program teaches students best practices in engineering and data collection. It consists of a total of 27 credit-hours. The curriculum includes topics such as Python for data science, statistics for data science, the fundamentals of data engineering, applied machine learning, data visualization, research design, and application for data and analysis.
The program helps students identify essential patterns in data using statistics and computers, decipher conclusions from data, and predict outcomes based on data. In addition, students also get a chance to understand the legal and ethical implications of using real-world data and how to better communicate one’s findings.
At the end of the program, graduates can pursue positions such as business data analyst, systems engineer, data architect, data analyst, data architect, solutions architect, and data scientist.
Harvard University – Extension School
Harvard University offers a degree in data science as part of its online master of liberal arts program. While most of the courses can be taken online, students must come to Cambridge for the pre-capstone course, where students get a chance to meet the faculty and use on-campus resources.
Admission requirements for the program include a bachelor’s degree from a regionally accredited institution, registration and completion of two graduate-level degree courses with a grade B or higher, a cumulative GPA of 3.0, a resume, official transcripts from every college or university attended, and proof of English proficiency for international students.
The program is made up of 12 courses. It provides students with skills and knowledge necessary for navigating a data-powered world. Some of the courses include data structures, advanced Python for data science, big data analytics, big data in healthcare applications, data mining for business, and introduction to data science.
The program gives students an introduction to the various methods and techniques involved in data science—helping them identify patterns in data, visualize large amounts of data, and use principles of data analysis to solve real-world problems.
Northwestern University – School of Professional Studies
Northwestern University offers a fully online master of science in data science program. Students have the option to pursue the general data science track or in one of four specializations: artificial intelligence, analytics and modelling, analytics management, and data engineering. The program helps students gain a better understanding of the workings and applications of predictive models. The faculty for the program includes data scientists, mathematicians, researchers, and computer scientists.
In order to apply for the program, students must have a bachelor’s degree from an accredited university and submit an online application, a 300-word statement of purpose, official transcripts from previously attended colleges and universities, two letters of recommendation, and current resume. Work or research experience in the field as well as GRE scores are not a requirement but can be submitted for consideration.
The curriculum comprises 12 courses. It includes topics such as decision analytics, an introduction to data science, generalized linear models, project management, foundations of data engineering, and data visualization. Students learn how to use systems and software for analytics and database management. Additionally, they learn how to derive insights from data, design plans to manage business problems, and assess data structures.
Apart from fundamental skills and knowledge of data science, the program also helps students develop leadership skills. On completion of the program, students can pursue roles such as computer systems analyst, database administrator, software developer, computer network analyst, data analyst, data scientist, data engineer, and data manager.
Maryville University offers an online master of science in data science (MSDS). The program does not require campus visits. The curriculum has been developed with the help of companies using big data across different industries, while the faculty are experienced leaders in data science.
Admission requirements to the program include a bachelor’s degree and a grade point average of 3.6 or higher, among others.
Made up of 36 credit-hours, the curriculum takes a deep dive into topics such as data visualization, deep learning, statistical design, forecasting principles, predictive modeling, machine learning, and math modeling. The program encourages students to supplement their theoretical knowledge with practical learning with the help of projects. Students learn how to analyze large sets of data, identify patterns in data sets, and use data to predict behavior and future outcomes.
On completion of the program, graduates can work in various types of small businesses, manufacturing companies, government agencies, financial firms, and large companies. Some of the roles they can pursue include data scientist, data analyst, financial analyst, and computer systems analyst.
Southern Methodist University offers an online master of science in data science (MSDS). Applicants must have a bachelor’s degree from a regionally accredited institution, GRE scores, a basic understanding of programming languages, and scores on the TOEFL exam for those whose native language is not English.
The program consists of 33.5 credit-hours. They study courses such as The statistical foundations for data science, applied statistics, file organizations and database management, data and network security, and data mining. Students are trained to apply mathematical principles, and also strengthen their skills in programming, machine learning, data mining, and management.
Some of the roles that graduates can pursue include marketing analyst, pricing analyst, research analyst, and data analyst.
Guangwei Fan, PhD – Maryville University
Dr. Guangwei Fan serves as the director of actuarial science, data science, and mathematics at Maryville University. With over 30 years of experience as a teacher, he has assisted many of his students to find employment and succeed in the field of data science.
Dr. Fan is a well-respected educator, researcher, and published author of Actuarial Excel II. His research work is focused on helping data scientists better communicate with their teams, while his ultimate goal is to increase the impact of mathematics across organizations all over the world. He also works actively towards helping his students realize their goals in the field of data science.
Aditya Parameswaran, PhD – University of Illinois
Dr. Aditya G. Parameswaran teaches computer science courses at the University of Illinois, including an introduction to database systems, advanced data management, and human-in-the-loop data management. Prior to joining the University of Illinois, he taught at the Massachusetts Institute of Technology.
Currently, Dr. Parameswaran researches and develops new tools for data analytics, with his larger goal being to help teams and individual employees harness data more efficiently and effectively. His work has been published in top-notch journals such as the Electronic Journal of Statistics and ACM Transactions on Information Systems. Professor Parameswaran has received several awards such as the Dean’s Award for Excellence in Research and the Outstanding Junior Faculty Award. He completed his PhD from Stanford University and his B.Tech from IIT Bombay.
Dr. James Spall, PhD – Johns Hopkins University
Dr. James C. Spall serves as the co-chair of the data science program at Johns Hopkins University. Some of the graduate courses he teaches include stochastic algorithms, Monte Carlo simulation, optimization, system identification, and neural networks.
Presently, his research explores topics such as parameter estimation, stochastic optimization, and Monte Carlo methods and simulation. A member of professional organizations such as the American Statistical Association and Sigma Xi, his research has been published in prominent journals such as International Journal of Control and the Journal of the American Statistical Association.
Notably, Professor Spall has won several awards such as the Professor Joel Dean Award for Excellence and the Excellence in Teaching Award. He completed his PhD from University of Virginia.
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineering faculty at top universities, highlighting their research in areas such as nuclear power, artificial intelligence, soft robotics, and semiconductor design. He’s also worked with leaders and subject matter experts at the Institute for Electrical and Electronics Engineers (IEEE), the California Department of Water Resources (DWR), the National Society of Professional Engineers (NSPE), and the Society of Women Engineers (SWE).
As with other engineering disciplines, mechanical engineering is complex, and the success of a mechanical engineering project can often be mission-critical. Given that, it is essential to learn from the best, and these professors represent the most accomplished and involved in the field, ready to inspire and impart their knowledge to a new generation of engineers.
As with other engineering disciplines, mechanical engineering is complex, and the success of a mechanical engineering project can often be mission-critical. Given that, it is essential to learn from the best, and these professors represent the most accomplished and involved in the field, ready to inspire and impart their knowledge to a new generation of engineers.
As with other engineering disciplines, mechanical engineering is complex, and the success of a mechanical engineering project can often be mission-critical. Given that, it is essential to learn from the best, and these professors represent the most accomplished and involved in the field, ready to inspire and impart their knowledge to a new generation of engineers.
As with other engineering disciplines, mechanical engineering is complex, and the success of a mechanical engineering project can often be mission-critical. Given that, it is essential to learn from the best, and these professors represent the most accomplished and involved in the field, ready to inspire and impart their knowledge to a new generation of engineers.
As with other engineering disciplines, mechanical engineering is complex, and the success of a mechanical engineering project can often be mission-critical. Given that, it is essential to learn from the best, and these professors represent the most accomplished and involved in the field, ready to inspire and impart their knowledge to a new generation of engineers.
