Applied Data Science

Langmore, Ian; Krasner, Daniel

dc.contributor.author	Langmore, Ian
dc.contributor.author	Krasner, Daniel
dc.date.accessioned	2023-07-01T01:22:45Z
dc.date.available	2023-07-01T01:22:45Z
dc.date.issued	2013
dc.identifier.uri	${sadil.baseUrl}/handle/123456789/3732
dc.description	141 p. (PDF)	sm
dc.description.abstract	This book focuses more on the statistics end of things, while also getting readers going on (basic) programming & command line skills. It doesn’t, however, really go into much of the stuff you would expect to see from the machine learning end of things.	sm
dc.language.iso	en	sm
dc.publisher	Columbia University	sm
dc.subject	Programming prerequisites	sm
dc.subject	History and culture	sm
dc.subject	The shell	sm
dc.subject	Streams	sm
dc.subject	Standard streams	sm
dc.subject	Pipes	sm
dc.subject	Philosophy	sm
dc.subject	Version control with git	sm
dc.subject	Online materials	sm
dc.subject	Basic git concept	sm
dc.subject	Common git workflows	sm
dc.subject	Linear move from working to remote	sm
dc.subject	Merge conflicts	sm
dc.subject	Simple shell scripts	sm
dc.subject	Template for a python CLI utility	sm
dc.subject	Notation	sm
dc.subject	Linear regression	sm
dc.subject	Logistic regression	sm
dc.subject	Models behaving well	sm
dc.subject	Text data	sm
dc.subject	Processing text	sm
dc.subject	Python RE module	sm
dc.subject	The python NLTK library	sm
dc.subject	Naive bayes	sm
dc.title	Applied Data Science	sm
dc.type	Book	sm