A Brief Introduction to Data Science. Data Science, Big Data, Data, and the Data Science process. So this article could serve as an intro to Data Science, and it could be a refresher to it as well. Nonetheless, I hope you learn something from it and have fun reading it.
Full series Part 1 - What is Data Science, Big data and the Data Science process Part 2 - The origin of R, why use R, R vs Python and resources to learn Part 3 - Version Control, Git & GitHub and best practices for sharing code. Part 4 - The types of questions you ask in Data Science Part 5 - The ability to design experiments to answer your Ds questions Part 6 - Big Data, it's benefits, challenges, and future
This series is based on the [Data Science Specialization_](https://www.coursera.org/specializations/jhu-data-science) offered by John Hopkins University on Coursera. The articles in this series are notes based on the course, with additional research and topics for my own learning purposes. For the first course, [Data Scientist Toolbox_](https://www.coursera.org/learn/data-scientists-tools), the notes will be separated into 4 parts. Notes on the series can also be found [here_](http://sux13.github.io/DataScienceSpCourseNotes/)._
Data Scientist’s have the ability to find patterns and insights in oceans of data, akin to an astronomer looking out into the deep space with telescopes to find new planets and galaxies and black holes in the midst of billions of stars and other galaxies. Data Science, fundamentally like science, is used to answer questions about the world by combining different fields, ie Mathematics, Computer Science, Philosophy, etc., along with distinct methodologies and novel technology to augment and enhance our ability to answer them well.Whether you’re a beginner who’s new to Data Science, or you’re a working Data Scientist, it’s always good to go back to the central theme of what Data Science is all about. It’s easy to get sidetracked or distracted by new tools or the monotony of work that we forget the principal notion of Data Science and the amazing possibilities that it creates. So this article could serve as an intro to Data Science, and it could be a refresher to it as well. Nonetheless, I hope you learn something from it and have fun reading it.
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
In this article, see the role of big data in healthcare and look at the new healthcare dynamics. Big Data is creating a revolution in healthcare, providing better outcomes while eliminating fraud and abuse, which contributes to a large percentage of healthcare costs.