"Exploring Math for Programmers & Data Scientists"

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Book	Author(s)	Year	Publisher
Exploring Math for Programmers & Data Scientists	Paul Orland	2021-06-29	Simon and Schuster
Math for Programmers	Paul Orland	2020	Manning Publications
Data structures for programmers	Milton Rosenstein	1988	Wiley
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists	Janert, Philipp K	2010;2011	O'Reilly Media
Math projects for young scientists	Thomas, David A.	1988	F. Watts
Probability for Data Scientists	Juana Sanchez (author)	2019	Cognella Academic Publishing
Exploring Math	Donna Borst; Vanessa Filkins	August 2000	Fearon Teacher AIDS
Math Refresher for Scientists and Engineers	John R. Fanchi	2006	Wiley-IEEE Press
Math for Scientists: Refreshing the Essentials	Natasha Maurits; Branislava Ćurčić-Blake	1st ed. 2017	Springer International Publishing
Basic math for social scientists: concepts /	Hagle, Timothy M.,	©1995.	Sage Publications,
Math refresher for scientists and engineers	John R. Fanchi	2000	Wiley
Basic math for social scientists: concepts /	Hagle, Timothy M.	[1995], ©1995.	Sage Publications,
Math refresher for scientists and engineers	John R. Fanchi	1997	Wiley
D3 for the Impatient: Interactive Graphics for Programmers and Scientists	Philipp K. Janert	2019	O'Reilly Media
Visual Analytics for Data Scientists	Natalia Andrienko; Gennady Andrienko; Georg Fuchs; Aidan Slingsby; Cagatay Turkay; Stefan Wrobel	1st ed. 2020	Springer International Publishing
Exploring Elementary Math	Keedy	August 1970	Holt Rinehart & Winston
Essential Math for Data Science	Thomas Nield	2022	O'Reilly
Object-oriented C++ data structures for real programmers /	Harrington, Jan L.	[2002], ©2002.	Morgan Kaufmann/Academic Press,
Data Analysis Techniques for Physical Scientists	Claude A. Pruneau	2017-10	Cambridge University Press
Quantitative data analysis for social scientists /	Bryman, Alan.; Cramer, Duncan,	1994.	Routledge,
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Showing 1 to 20 of 100 entries

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Chapter	Author(s)	Book	Book Authors	Year	Publisher
04 Promo Exploring Math for Programmers and Data Scientistspdf		Exploring Math for Programmers & Data Scientists	Paul Orland	2021-06-29	Simon and Schuster
Math for Programmers		Math for Programmers	Paul Orland	2020	Manning Publications
Math for Programmers		Exploring Math for Programmers & Data Scientists	Paul Orland	2021-06-29	Simon and Schuster
Math for Programmers - back		Math for Programmers	Paul Orland	2020	Manning Publications
Math for scientists		Exploring the world of mathematics	John Hudson Tiner	2004	Master Books
Math for Programmers Crash Course		Practical MLOps: Operationalizing Machine Learning Models	Noah Gift, Alfredo Deza	2021	O'Reilly Media
Toolboxes for Data Scientists	Laura Igual; Santi Seguí	Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications	Laura Igual; Santi Seguí	1st ed. 2017	Springer International Publishing
Knowledge for data scientists		Data science for undergraduates: opportunities and options /	National Academies of Sciences, Engineering, and Medicine; Division of Behavioral and Social Sciences and Education; Board on Science Education; Division on Engineering and Physical Sciences; Committee on Applied and Theoretical Statistics		National Academies Press
Statistics for Data Scientists		SAS for R Users - Data Science and Business Analytics	Ajay Ohri	2019	John Wiley & Sons
Data Scientists		Integrated Advertising, Promotion, and Marketing Communications, Global Edition	Kenneth Clow, Donald Baack	2021	Pearson
""Data Scientists""		Creating a data-driven organization: practical advice from the trenches	Anderson, Carl	2015	O'Reilly Media
Data Scientists		Competing with Unicorns: How the World's Best Companies Ship Software and Work Differently	Jonathan Rasmusson	2020	Pragmatic Bookshelf
Data Scientists		Introducing MLOps: How to Scale Machine Learning in the Enterprise	Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann	2020	O'Reilly Media
Data scientists		What is data science?: a beginner's guide to key concepts and pitfalls	Mike Loukides	2011
Data Scientists		Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD	Jeremy Howard, Sylvain Gugger	2020	O'Reilly Media
Data Scientists		Data science from scratch: the #1 data science guide for everything a data scientist needs to know: Python, linear algebra, statistics, coding, applications, neural networks, and decision trees	Steven Cooper	2018	Steven Cooper
Data Scientists	Thilo Stadelmann; Kurt Stockinger; Gundula Heinatz Bürki; Martin Braschler	Applied Data Science: Lessons Learned for the Data-Driven Business	Martin Braschler; Thilo Stadelmann; Kurt Stockinger	1st ed. 2019	Springer International Publishing
Engineers, scientists, programmers, and other odd people		The Dilbert Principle: A Cubicle's-Eye View of Bosses, Meetings, Management Fads & Other Workplace Afflictions	Scott Adams	1996	HarperBusiness
An Approach for Data Scientists	A Ohri	R for Cloud Computing: An Approach for Data Scientists	A Ohri	2014	Springer New York
Best Practices for Data Scientists		Machine Learning for High-Risk Applications	Patrick Hall	2021	O'Reilly Media, Inc.
Chapter	Author(s)	Book	Book Authors	Year	Publisher

Showing 1 to 20 of 100 entries

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