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Johns Hopkins University

Data Science: Statistics and Machine Learning

Johns Hopkins University via Coursera Specialization

Overview

Build models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material. The five courses in this specialization are the very same courses that make up the second half of the Data Science Specialization. This specialization is presented for learners who have already mastered the fundamentals and want to skip right to the more advanced courses.

Syllabus

Course 1: Statistical Inference
- Offered by Johns Hopkins University. Statistical inference is the process of drawing conclusions about populations or scientific truths from ... Enroll for free.

Course 2: Regression Models
- Offered by Johns Hopkins University. Linear models, as their name implies, relates an outcome to a set of predictors of interest using ... Enroll for free.

Course 3: Practical Machine Learning
- Offered by Johns Hopkins University. One of the most common tasks performed by data scientists and data analysts are prediction and machine ... Enroll for free.

Course 4: Developing Data Products
- Offered by Johns Hopkins University. A data product is the production output from a statistical analysis. Data products automate complex ... Enroll for free.

Course 5: Data Science Capstone
- Offered by Johns Hopkins University. The capstone project class will allow students to create a usable/public data product that can be used ... Enroll for free.

Courses

Taught by

Brian Caffo, PhD, Jeff Leek, PhD and Roger D. Peng, PhD

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