Data Science Specialization – Course by Johns Hopkins University

Level: Beginner
Duration: 7 Days
Delivery: Online
Certification: Yes
Cost: 49
Course Provider: Johns Hopkins University


Johns Hopkins University’s Data Science Specialization aims to teach students the concepts and tools required to become a data scientist, including asking the right questions, making inferences, and publishing results.

Training Course Content

The specialization has nine courses designed to teach students the fundamentals of data science, plus a capstone project that displays to potential employers the skills the students acquired. The courses may be taken in the suggested order or according to the student’s preference.

Here is an overview of each course:

  • Course 1: The Data Scientist’s Toolbox

This gives students an introduction to the core tools and ideas that data scientists use, such as, version control, markdown, git, GitHub, R, and RStudio.

  • Course 2: R Programming

This course teaches how to program in R and how to use it for effective data analysis.

  • Course 3: Getting and Cleaning Data

Students will learn how to obtain data from the web, from APIs, from databases, and colleagues in different formats. Plus, it teaches how to clean data to accelerate downstream data analysis tasks.

  • Course 4: Exploratory Data Analysis

Before developing more complex statistical models, data scientists first use exploratory techniques for summarising data, which will be taught in this course.

  • Course 5: Reproducible Research

As data analyses become more complex, the need for reproducibility also grows. This course teaches students the concepts and tools behind modern data analyses in a reproducible manner.

  • Course 6: Statistical Inference

Students will learn how to draw conclusions from data using techniques such as statistical model and data-oriented strategies.

  • Course 7: Regression Models

Regression analysis, least squares, and inference using regression models—considered as the most important statistical analysis tools in a data scientist’s toolkit—will be taught in the course.

  • Course 8: Practical Machine Learning

The course offers the essentials of machine learning, including training and tests sets, overfitting, and error rates.

  • Course 9: Developing Data Products

The basics of creating data products using Shiny, R packages, and interactive graphics are discussed in this course.

  • Course 10: Data Science Capstone

Students will create a usable data product that will showcase their skills to potential employers in the capstone project.

Who Is It For?

As a beginner specialization, this is ideal for both students and professionals with no prior knowledge of data science but would like to pursue a career in this field or expand their skill set.



About the Provider

Johns Hopkins University aim to educate its students and cultivate their capacity for lifelong learning to foster independent and original research and bring the benefits of discovery to the world.

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User Reviews

· October 17, 2018

Run by a major university, I was expecting big things. I wasn’t disappointed either. This was a very comprehensive course that has put me on the path to becoming a successful data scientist.

· October 31, 2018

Instructors could've been a bit clearer imo

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