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Whether you are new to data science or are preparing for a master's degree in data science or business analysis, you need to learn more. This guide collects the top data science resources on the web in 2018 for learners at all stages. Online courses and tutorials

Dataquest data science research

Social Welfare data Science-this website is managed by the Center for data Science and Public Policy at the University of Chicago. It highlights the socially beneficial data science projects of non-profit organizations, government organizations and social organizations.

(https://dssg.uchicago.edu/projects/)

International Journal of data Science and Analysis-this online journal welcomes experimental and theoretical discoveries and their practical applications in data science and advanced analysis. This is the first scientific journal in the field of data science and analysis.

(https://link.springer.com/journal/volumesAndIssues/41060)

CODATA data Science Journal-data Science blog

Edwin Chen's blog data Science eBook

Trip to data scientists: interview more than 20 amazing data scientists-when author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than 20 interviews, she asked leading data scientists about the starting point of the field and the future of the industry.

(https://www.amazon.com/Journey-Data-Scientist-Interviews-Scientists/dp/1548984248)

Learn Python in a difficult way-the latest Python 3 update, which is for beginners to finally learn how to code, although primitive but still the most popular way. The hard way to learn Python takes you from absolute zero to being able to read and write basic Python, and then learn about other books about Python.

(https://learnpythonthehardway.org)

O'Reilly Free data Science Library-this library brings together the best data insights from O'Reilly editors, authors, and Strata speakers in one place, so you can learn more about data science and the latest developments in big data.

(https://www.oreilly.com/data/free/)

Bayesian reasoning and Machine Learning-this book is intended for students with backgrounds in computer science, engineering, applied statistics, physics and bioinformatics who want to acquire machine learning knowledge. The author uses layman terminology and low-level algebra and calculus to introduce the basic concepts of reasoning.

(http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf)

Data Mining Guide-this free book uses a do-and-learn approach to explain basic data mining techniques. The data mining guide introduces practical data mining, collective intelligence and the establishment of recommendation systems.

(http://guidetodatamining.com)

Interpretable Machine Learning-this online book is about making machine learning models and their decisions explainable. It applies to machine learning practitioners, data scientists, statisticians and anyone else interested in making machine decisions more human.

(https://christophm.github.io/interpretable-ml-book/)

Data Science Manual-the data Science Manual brings together in-depth interviews with 25 accomplished data scientists about their insights, stories and suggestions. Although this book is not a tutorial on the subject of data science, it provides practical career insights for a variety of industries.

(http://www.thedatasciencehandbook.com)

The Art of data Science-this book describes the process of analyzing data. The author developed a background for managing data analysts and conducting their own data analysis.

(https://leanpub.com/artofdatascience)

Data Analysis Manual-this manual provides insight into the data science industry by visiting cutting-edge data scientists, data analysts, CEO, managers and researchers.

(https://www.teamleada.com/handbook)

Numsense! Layman's data Science: no Mathematics added-as the title implies, this book breaks data science and omits mathematical terminology for people from all backgrounds. Aspiring students, enterprising business professionals or others eager to learn can find tutorials and easy-to-understand explanations.

(https://www.amazon.com/Numsense-Data-Science-Layman-Added-ebook/dp/B01N29ZEM6)

D3 Tips and Tips v4.x-this book contains tips and tricks for using d3.js (4th Edition), one of the leading data visualization tools on the web. It is designed to get you started and move forward.

(https://leanpub.com/d3-t-and-t-v4)

Data mining algorithms in R-those who know the programming language R and want to know more about data mining will benefit from this WikiBook. Understanding how the algorithm works will help increase your understanding of data mining.

(https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R)

Data mining technology: for marketing, sales, and customer relationship management, version 3-learn to use data mining for marketing or sales purposes. You will get advice on improving the response rate of direct marketing activities, identifying new customer bases, and assessing credit risk.

(https://www.wiley.com/en-us/Data+Mining+Techniques%3A+For+Marketing%2C+Sales%2C+and+Customer+Relationship+Management%2C+3rd+Edition-p-9780470650936)

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