Post-Master's Certificates in Data Science

Sep 25, 2019

A post-master's certificate in data science can prepare students to step into the growing field of data science with specialized theoretical and practical skills in applied mathematics, statistics, and computer science. In this article, students can find general program information and admission requirements for a post-master's data science certificate.

Data Science Program Information

A post-master's data science certificate enables students to analyze and articulate the intricate relationships within complex data sets and to create automated models that can interact with and resolve relevant problems. What follows is a detailed list of common courses in post-master's data science programs.

Data Science

A data science course concerns itself with introducing students to the concepts used to develop accurate and reliable statistical models to analyze data sets. Students may begin their studies by discussing ethical and regulatory issues related to the using analytics. Students may progress to more advanced probability theorems and computational statistics they can then integrate into the practice of effective data curation.

Data Visualization

In this course students can expect to gain the necessary practical and theoretical skills in how to create visual representations of data. In further study, they might encounter units covering specific methods of visualization, from scientific visualization to information and flow visualization. The study of psychology as it relates to perception and cognition could be covered as well.

Data Management

A data management course introduces principles for the effective access and extraction of information from databases and data sets. Students can expect a focus on database design and may also encounter SQL and NoSQL programming principles. Studies may also extend to policy-based management techniques that instead emphasize policy creation for managing databases.

Machine Learning

Machine learning courses introduce a variety of approaches to learning machine models, all for the purpose of analyzing and optimizing data sets. At first, students should expect to learn foundational statistical models and mathematical theorems they can apply to machine learning. From there, students will encounter more advanced units covering concepts from logistic regression to feed-forward neural networks and more.

Data Mining

Data mining is a fundamentally interdisciplinary topic and integrates many elements of the courses listed previously, including machine learning and data visualization. Students may start by developing algorithms that can organize data sets and databases towards some desired end. They can then expect to approach more advanced classification methods including, for example, Bayes Decision Theory and the Support Vector Machines method.

Post-Master's Certificate Admission Requirements

For applicants to a post-master's certificate program in data science, schools may seek a master's degree in data science or some other related degree (including but not limited to degrees in applied statistics or computer science), but often a master's degree from an accredited school will be sufficient. GRE scores are not often required in admissions, and students should also note schools in the United States may require additional testing if English is not a student's first language.

Schools may also look for specific course prerequisites, and in the case of data science these may include courses ranging from calculus and discrete mathematics to programming courses like Java and Python. In addition to prerequisites, schools often expect grades of B and above, roughly equivalent to a 3.0 GPA, in prior transcripts.

In order to apply to a post-master's certificate program in data science, prospective students should expect to have completed a graduate degree relevant to data science as well as any necessary pre-requisite courses depending on their school of choice. Upon admittance, students can expect their post-master's certificate to enable the further specialization of their skills in interpreting and curating data, all to resolve challenges within the growing interdisciplinary field of data science.

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