Title -51福利 Data Science Certificate Program

51福利 Data Science Certificate Programs 

Distance Learning (Curriculum 268); Resident (Curriculum 269)

Cert_Flyer

Call for Applicants:

Join the next iteration of the Distance Learning (Curriculum 268) Data Science Certificate offering.

Be a part of the Winter 2025 Cohort

(Application deadline is 13 October 2024)

on our Certificate.

or contact LTC Rob Froberg.

The Goals of the Certificate Program

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

Data Science Certificate Educational Skill Requiements

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.

DS Cert Link or Contact

Please follow the link for further information about the  

or contact LTC Rob Froberg.

Data Sciences Certificate Program Requirements

Data Science Certificate (Curriculum 268)

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.




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.