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.
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:
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.
This course teaches how to program in R and how to use it for effective data analysis.
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.
Before developing more complex statistical models, data scientists first use exploratory techniques for summarising data, which will be taught in this course.
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.
Students will learn how to draw conclusions from data using techniques such as statistical model and data-oriented strategies.
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.
The course offers the essentials of machine learning, including training and tests sets, overfitting, and error rates.
The basics of creating data products using Shiny, R packages, and interactive graphics are discussed in this course.
Students will create a usable data product that will showcase their skills to potential employers in the capstone project.
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.
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.
All fields marked with red asterisks are required fields.
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.
Instructors could've been a bit clearer imo