Details

Data science is a key part of the world of STEM. You may have heard it described as the “sexiest job of the twenty-first century”. In this collection of tutorials, we’ll take a look at the field of data science, the key skills, and provide an introduction to using Python and R for data science.

Description

Data science is a key part of the world of STEM (science, technology, engineering, and mathematics). You may have heard it described as the “sexiest job of the twenty-first century”. There’s a soaring demand for data scientists, and the role involves a wide variety of skills. However, the day-to-day job of a data scientist can be a mystery.

In this collection of tutorials, we’ll take a look at the field of data science, how you can use data science skills in your own day-to-day work, how to best learn key data science skills, and provide an introduction to using Python and R for data science. This collection contains:

Data Science for Everyone by Mona Khalil
Python for Data Science: an Introduction to Pandas by Claudio Ribeiro
Introduction to R and RStudio by Bruno Skvorc
How to Import Data and Export Results in R by Shaumik Daityari
Exploratory Data Analytics in Python: Car Imports into the US by Shaumik Daityari

Creators

Mona Khalil

Cláudio Ribeiro

Cláudio Ribeiro is a software developer, traveler, and writer from Lisbon. He's the author of the book An IDE Called Vim. When he is not developing some cool feature at Kununu he is probably backpacking somewhere in the world or messing with some obscure framework.

Bruno Skvorc

Bruno, a web developer from Croatia, is SitePoint's senior PHP editor by day, and a developer evangelist for Diffbot.com by night. A big fan of working on treadmill desks, he spends the most of his days walking through various projects and problems, and then exposing them on SitePoint.com. In his free time (i.e. when his beagle is asleep) he swims, #stronglifts, plays (board) games and paints miniatures.

Shaumik Daityari

Shaumik is an optimist, but one who carries an umbrella. He is currently pursuing his MBA at IIM Lucknow, after completing his M.Tech at IIT Roorkee. A co-founder of The Blog Bowl, he loves writing.

Reviews

Harold One Feather

Data science is about making the information workflow interesting, these authors have exceeded that, making this book a must read by the novice and professional. There is so much new data science research, making cleaning data using tools like R, Python, RStudio and Jupyter easy to do using the workflow described in the text. This book is short, but meaningful as data science is 80% data cleaning and 20% mathematical algorithms, clearly written in the quickest adaptive learning techniques, no extra words and such fluff. R and Python are the two leading data science programming languages covered thoroughly in this text. Following the simple examples in the book, installing R, Python, RStudio and Jupyter is easy. While it is difficult to choose sides, the book covers R a little than is usual in a beginning data science book. I do agree with this as R scales across platforms slightly better than Python. But Python is best used as a 80s Vega-Matic instant salad tosser for data cleaning using pandas and Jupyter, there is no comparison in data science in terms of ease of Python use. However, Python is a general purpose programming language as compared to R which is focused mostly on data science, machine learning, predictive analytics, data mining and Artificial Intelligence. Overall this books exceeds expectations in a beginning data science book, I can't wait for more from the authors.

Data Science: An Introduction