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Johns Hopkins University

Data Science Capstone

Johns Hopkins University via Coursera


The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.


  • Overview, Understanding the Problem, and Getting the Data
    • This week, we introduce the project so you can get a clear grip on the problem at hand and begin working with the dataset.
  • Exploratory Data Analysis and Modeling
    • This week, we move on to the next tasks, exploratory data analysis and modeling. You'll also submit your milestone report and review submissions from your classmates.
  • Prediction Model
    • This week, you'll build and evaluate your prediction model. The goal is to make your model efficient and accurate.
  • Creative Exploration
    • This week's goal is to improve the predictive accuracy while reducing computational runtime and model complexity.
  • Data Product
    • This week, you'll work on developing the first component of your final project, your data product.
  • Slide Deck
    • This week, you'll work on developing the second component of your final project, a slide deck to accompany your data product.
  • Final Project Submission and Evaluation
    • This week, you'll submit your final project and review the work of your classmates.

Taught by

Jeff Leek, Roger Peng and Brian Caffo


3.0 rating, based on 4 Class Central reviews

4.5 rating at Coursera based on 1201 ratings

Start your review of Data Science Capstone

  • Dwayne D completed this course.

    The positive: Completion of this project requires most of the skills you will have learned in completing the prequisite courses. The negative: If you're "like me" (inexperienced with NLP), you should start reading up on the basics (enough to know the...
  • Anonymous

    Anonymous is taking this course right now.

    So disappointing, it feels somewhat unrelated to the material covered in the 9 courses in the Data Science Specialization, so I didn't feel adequately prepared for tackling the Capstone even though I carefully completed all pre-req courses. Also, the level of complexity of the problem (ie having to read multiple academic papers on NLP and computational linguistics) is not appropriate for a course that should be focused on teaching general, practical, applicable data science skills.
  • Colin Khein completed this course.

  • Mark Henry Butler completed this course.

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