Johns Hopkins Data Science Specialization

Johns Hopkins Data Science Specialization

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★★★★★
(2 customer reviews)

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This course gives you a very good idea about:

  1. The different parts of the whole data science pipeline.
  2. And how to work through the whole data science pipeline using R.

If you had no programming experience, this course will be really hard! If you are new to the field of data science or analytics, this may not be the first resource you should go for. A decent understanding of statistics, ML techniques would be helpful in deriving the most out of this courses.

Specification: Johns Hopkins Data Science Specialization

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Modules

1. An introduction to the main tools and ideas in the data scientist's toolbox.
2. Learn how to program in R and how to use R for effective data analysis.
3. Learn the basics needed for collecting, cleaning, and sharing data.
4. Learn essential exploratory techniques for summarizing data.
5. Learn the concepts and tools behind reporting modern data analyses in a reproducible manner.
6. Learn statistical inference (the process of drawing conclusions about populations or scientific truths from data).
7. Learn regression analysis, least squares and inference using regression models.
8. Practical Machine Learning – This will cover the basic components of building and applying prediction functions with an emphasis on practical applications.
9. Developing Data Products – This course covers the basics of creating data products using Shiny, R packages, and interactive graphics.
10. Data Science Capstone – create a usable/public data product that can be used to show your skills to potential employers.

Upon completion

University credits

Graded quizzes & assignments

Capstone Project

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Photos: Johns Hopkins Data Science Specialization

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2 reviews for Johns Hopkins Data Science Specialization

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  1. ★★★★★

    Mohamad Rizwan –

    Completed Basic Data Science Specialisation taught by professors at Johns Hopkins University
    offered through Coursera after relentless learning efforts for more than one year. My first step to become Data Scientist… #datascience #deeplearning #artificialintelligence

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  2. ★★★★★

    Nathalie CEng –

    Delighted to have now completed my full data science specialisation with Coursera/John Hopkins University. It was a lot of hard work but I thoroughly enjoyed it! You can play with the text prediction app I developed as part of my Capstone project here: https://lnkd.in/dhqYxJV
    https://lnkd.in/gzifMsi

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    Expert Score 7.5
    Course Content
    7.5
    Learning Experience
    6
    Course Price
    8.5
    Credibility
    8
    Flexibility
    10
    PROS:
    • Course give a good foundation to Data Science.
    • Most of the DS topics are covered, making it one of the most “complete” in terms of delivering a DS pipeline.
    CONS:
    • They tried to cover topics as much as they can but didn't cover deeply.
    • Statistical Inference and Regression Models didn't provide basic foundations (lectures seem rushed). Some participants had to go multiple websites to learn basic concepts.
    • Practical Machine Learning will not teach you the Machine Learning algorithms from scratch but just teaches you to how to use different R packages to perform Modelling and Machine Learning tasks on your data.
    • The Capstone experience was mostly DIY with very little guidance from lectures and notes. You're pretty much on your own.
    • Some lecture material was sub-par (statistical inference) compared to Khan Academy.
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