Johns Hopkins University

Durartion of the Course

7 months

Course Fee


Mode of Learning

Self-Paced with Assessments

What is The Course All About?


  • Comprehensive and Well-Structured: Covers a broad range of data science topics in a logical and accessible manner.
  • Taught by Experts: Instructors are knowledgeable and experienced professors from Johns Hopkins University.
  • Practical Application: Emphasis on hands-on projects and real-world data analysis.
  • Flexible and Affordable: Self-paced learning with a reasonable subscription fee.


  • Limited Live Interaction: Lacks live lectures or Q&A sessions for real-time engagement.
  • Limited Career Guidance: No personalized career support or mentoring is provided.
  • Requires Some Programming Knowledge: Assumes learners have some familiarity with programming concepts, preferably in R.
  • Peer-Reviewed Assignments: Feedback on assignments may not be as consistent or detailed as expert feedback.
Primary Focus: Skill Acquisition & Knowledge Acquisition

Curriculum & Skills:

  • Data Science: Data collection, cleaning, exploration, analysis, visualization, and interpretation.
  • R Programming: Fundamentals, data structures, functions, packages (ggplot2, dplyr).
  • Statistics: Probability, statistical inference, regression analysis, machine learning algorithms (linear regression, logistic regression, decision trees, random forests).
  • Tools: RStudio, GitHub, and other relevant data science tools.

Who is the Course Meant For?

  • Beginners with a basic understanding of programming concepts, preferably in R.
  • Professionals from various fields seeking to transition into data science.
  • Individuals interested in gaining a comprehensive understanding of the data science pipeline.

Accreditations and Rankings

Johns Hopkins University
Foundational (for beginners)

Skills: What does this program cover?

  • Depth and Breadth of Topics Strong
  • Case Studies Strong
  • Tools Strong
  • Capstone Projects/Real World Application Strong
Skills: 27 /30

Faculties: Who are the experts behind the course?

  • Academic/Industry Exp. Strong
  • Teaching Experience Strong
  • Industry Recognition Strong
Faculties: 18 /20

Delivery Methods: How Is the program delivered?

  • Live Interaction Very Limited
  • Self-Paced Strong
  • Assessment Strong
  • Community Moderate
  • Collaborative Limited
Delivery Methods: 18 /30

Career Assistance: How does the program support career growth?

  • Mentoring Very Limited
  • Personalized Assistance Very Limited
  • Alumni Network Moderate
  • Industry Partnerships Not Applicable
Career Assistance: 5 /15

Student Satisfaction and Feedback: What Do Students Say About the Course?

  • Feedback & Improvement Strong
Alumini Feedback: 5 /5

Benchmark Labels:

Strong (75-100%): This indicates the course demonstrates exceptional strengths in this area, exceeding expectations.
Moderate (50-74%): This signifies the course performs adequately in this area, covering essential aspects.
Limited (25-49%): This highlights areas where the course could significantly improve to provide a more comprehensive learning experience.
Very Limited (1-24%): This indicates the course offers minimal coverage or resources in this area.
Not Applicable: This indicates that this criteria is not available for this course.

No Reviews found!

Alumni Feedback Form

Skills: What does this program cover?

Faculties: Who are the experts behind the course?

Delivery Methods: How is the program delivered?

Career Assistance: How does the program support career growth?

Acquired new skills in my area of expertise
Gained practical hands-on experience through assignments
Built a professional network by connecting with instructors and fellow learners
Expanded career opportunities and job prospects
Opened doors to new industries or job roles
Secured a new job through the course
Prepared for professional certifications or examinations
Stayed updated with industry trends and advancements
Accessed resources and materials for future reference
Received a promotion
Received a pay raise
Other (please specify)