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NPS Data Science Certificate Program

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Announcement for the Winter 2020 Cohort

Click here for info on our Certificate.

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The goal of the certificate program is to provide education in the use of data science methods to gain insights from large, complex data sets.

Evaluate the concepts associated with managing large data sets, including cloud computing and split-merge distributed processing
Categorize and apply common algorithms for machine learning and evaluate their advantages and disadvantages
Apply standard tools to format and process data for machine-learning and data mining implementations
Evaluate the results of machine learning and data mining programs and propose ways to improve their utility in specific applications

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The eight data science certificate educational skills requirements are categorized as Basic Computation, Basic Statistics and Data Analysis, Large Data, Machine Learning, Assessment, Supervised Learning, Unsupervised Learning, and Practice.  Details below explain the interpretation of these skills.

Possess the mathematical and computer programming skills required to conduct data science projects, and be able to use computers to aid in data analytics.

The graduate will be well-versed in the fundamentals of statistics and data analysis for applications to machine learning and data mining problems.

The graduate will be able to identify, evaluate, and apply the concepts associated with managing large data sets, including cloud computing and split-merge distributed processing.

The graduate will know when and how to apply common machine learning tools, both supervised and unsupervised, and understand their strengths and weaknesses.

The graduate will be able to evaluate the results of machine learning algorithms, and propose ways to improve their performance and utility in specific applications.

The graduate will understand the use of the supervised methods of regression and classification, know when to implement them, and be able to apply basic and advanced techniques in appropriate settings including for large data sets.

The graduate will be aware of common unsupervised methods and be able to use them particularly for large data sets of high dimension.

The graduate will gain experience working on all aspects of a data science study, and demonstrate the ability to conduct independent analytical studies.

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Courses in the Certificate Program can be taught either on-campus or via Distance Learning.

Please follow the link for further information about the NPS Data Science Certificate Program 

or contact Kevin J. Maher, JD.

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Data Science Certificate (Curriculum 268)

Please see the catalog for a full description of the certificate.

Certificate Objective

Provide education in the use of data science methods to gain insights from large, complex data sets. Students will be able to:

  1. Evaluate the concepts associated with managing large data sets, including cloud computing and split-merge distributed processing
  2. Categorize and apply common algorithms for machine learning and evaluate their advantages and disadvantages
  3. Apply standard tools to format and process data for machine-learning and data mining implementations
  4. Evaluate the results of machine learning and data mining programs and propose ways to improve their utility in specific applications

Course Matrix

Please click the links below for a full description of each course.

CY3650 Cyber Data Management and Analytics (Q1)
OS4106 Advanced Data Analysis. (Q2)
CS4315 Introduction to Machine Learning and Data Mining. (Q3)
OS4118 Statistical and Machine Learning (Q4)

Educational Skills Requirements:

  1. Basic Computation. Possess the mathematical and computer programming skills required to conduct data science projects, and be able to use computers to aid in data analytics.
  2. Basic Statistics and Data Analysis: The graduate will be well-versed in the fundamentals of statistics and data analysis for applications to machine learning and data mining problems.
  3. Large Data. The graduate will be able to identify, evaluate, and apply the concepts associated with managing large data sets, including cloud computing and split-merge distributed processing.
  4. Machine Learning. The graduate will know when and how to apply common machine learning tools, both supervised and unsupervised, and understand their strengths and weaknesses.
  5. Assessment. The graduate will be able to evaluate the results of machine learning algorithms, and propose ways to improve their performance and utility in specific applications.
  6. Supervised Learning. The graduate will understand the use of the supervised methods of regression and classification, know when to implement them, and be able to apply basic and advanced techniques in appropriate settings including for large data sets.
  7. Unsupervised Learning. The graduate will be aware of common unsupervised methods and be able to use them particularly for large data sets of high dimension.
  8. Practice: The graduate will gain experience working on all aspects of a data science study, and demonstrate the ability to conduct independent analytical studies.