nunesma / data_science

Repos related to Johns Hopkins University and Coursera's Data Science specialization

Data Science

Repos related to Johns Hopkins University and Coursera's Data Science Specialization.

Table of contents:

• Getting and Cleaning Data project
• Exploratory Data Analysis project
• Reproducible Research project
• courses - Data Science Specialization
• DataScienceSpCourseNotes

Getting and Cleaning Data

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 cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Exploratory Data Analysis

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 are also important for eliminating or sharpening 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. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Reproducible Research

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. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Data Science Specialization - (courses)

These are the course materials for the Johns Hopkins Data Science Specialization on Coursera

Data Science Specialization Course Notes - (DataScienceSpCourseNotes)

Compiled notes for all 9 courses of the Johns Hopkins Unversity/Coursera Data Science Specialization. The notes are all written in R Markdown format and cover all concepts convered in class, as well as additional examples compiled from lecture, exploration, StackOverflow, and Khan Academy. These documents are intended to be comprehensive sources of reference for future use.