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Data Science

Roger D. Peng, PhD, Leo Porter, Ilkay Altintas, Jeff Leek, PhD, Brian Caffo, PhD, Rav Ahuja, and Joseph Santarcangelo

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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What's inside

Ten courses

The Data Scientist’s Toolbox

(0 hours)

In this course, you will learn the main tools and ideas in the data scientist's toolbox. You will get an overview of the data, questions, and tools that data analysts and data scientists work with. The course has two components. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

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R Programming

In this course, you will learn how to program in R and how to use R for data analysis. You will learn how to install and configure software for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

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Getting and Cleaning Data

Before you can work with data, you have to get some. This course covers obtaining data from the web, APIs, databases, and colleagues. It also covers data cleaning and making data "tidy". Tidy data speeds downstream data analysis tasks. The course also covers the components of a complete data set, including raw data, processing instructions, codebooks, and processed data.

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Exploratory Data Analysis

This course covers the essential exploratory techniques for summarizing data. These techniques can help inform the development of more complex statistical models and eliminate or sharpen potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics.

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Reproducible Research

(0 hours)

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 so that others may verify the findings and build upon them.

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Statistical Inference

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

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Regression Models

Linear models relate an outcome to a set of predictors using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

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Practical Machine Learning

One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

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Developing Data Products

A data product is the output from 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 creating data products using Shiny, R packages, and interactive graphics.

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Data Science Capstone

(0 hours)

The capstone project will allow students to create a 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, and academic partners.

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Learning objectives

Use r to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.

Use github to manage data science projects.
Perform regression analysis, least squares and inference using regression models.

Use r to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use github to manage data science projects.
Perform regression analysis, least squares and inference using regression models.

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Time

76 hours

Level

Introductory

Via

Coursera

Institution

Johns Hopkins University

Instructors

Roger D. Peng, PhD

Leo Porter

Ilkay Altintas

Jeff Leek, PhD

Brian Caffo, PhD

Rav Ahuja

Joseph Santarcangelo

Level

Introductory

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