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.
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10 courses
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Projects
Designed to help you practice and apply the skills you learn.
Certificates
Highlight your new skills on your resume or LinkedIn.
Commitment: 1-4 hours/week
Subtitles: English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew, Japanese
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components
Subtitles: English, French, Chinese (Simplified), Vietnamese, Japanese
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language
Subtitles: English, French, Chinese (Simplified), Vietnamese, Russian
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also
Subtitles: English, Vietnamese, Chinese (Simplified)
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques
Commitment: 4-9 hours/week
Subtitles: English, Vietnamese
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code
Subtitles: English, Vietnamese
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of des
Subtitles: English, Vietnamese
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit.
Subtitles: English
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical
Subtitles: English
A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creat
Upcoming session: Nov 26
Commitment
4-9 hours/week
Subtitles: English
The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government
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Roger D. Peng, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
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