Online Course – Johns Hopkins University Certified Professional Internship in Statistics and Machine Learning

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Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Drawing statistical conclusions
  • Regression models
  • Machine learning
  • Data product development
  • Building a data product using real-world data
  • Creating a portfolio that demonstrates mastery of the material

What you will learn in the course

Courses for which the course is suitable

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Product Key Data
  • Machine learning expert
  • Statistical Analyst
  • Algorithm developer
  • Data Project Manager
  • Data Science Consultant

Internship – 5-part course series

Build models, draw conclusions, and deliver interactive data products.

This specialization continues and expands the material learned in the Data Science: R Fundamentals course. It covers topics such as:

  • Drawing statistical conclusions
  • Regression models
  • Machine learning
  • Data product development

In the final project, you will apply the knowledge learned by building a data product using real-world data. At the end of the courses, learners will receive a portfolio that demonstrates their mastery of the material.

The five courses in the training

The five courses in this training are the same courses that make up the second half of the Data Science specialization. This specialization is intended for learners who have already mastered the basics and want to move directly to the advanced courses.

The Practical Learning Project

Each course in the “Data Science: Statistics and Machine Learning” training includes a practical assignment, which is graded by peers. To obtain the training certificate, you must successfully complete the practical assignment in each course, including the final thesis project.

Details of the courses that make up the specialization

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Additional Information

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