Launch Your Career in Data Science
A ten-course introduction to data science, developed and taught by leading professors.
Offered by John Hopkins University via Coursera
COURSE DESCRIPTION
This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.
COURSES
The Data Scientist’s Toolbox
R Programming
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Developing Data Products
Data Science Capstone
CERTIFICATION
When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.
LEARNING OUTCOMES
Use R to clean, analyze, and visualize data.
Use GitHub to manage data science projects.
Navigate the entire data science pipeline from data acquisition to publication.
Perform regression analysis, least squares and inference using regression models.
