Data Science Essentials in Python: Collect - Organize - by Dmitry Zinoviev

By Dmitry Zinoviev

Go from messy, unstructured artifacts kept in SQL and NoSQL databases to a neat, well-organized dataset with this fast reference for the busy information scientist. comprehend textual content mining, laptop studying, and community research; technique numeric info with the NumPy and Pandas modules; describe and study info utilizing statistical and network-theoretical equipment; and notice genuine examples of information research at paintings. This one-stop resolution covers the fundamental facts technology you would like in Python.

Data technological know-how is likely one of the fastest-growing disciplines by way of educational examine, pupil enrollment, and employment. Python, with its flexibility and scalability, is readily overtaking the R language for data-scientific tasks. hold Python data-science strategies at your fingertips with this modular, speedy connection with the instruments used to obtain, fresh, research, and shop data.

This one-stop answer covers crucial Python, databases, community research, ordinary language processing, parts of computing device studying, and visualization. entry dependent and unstructured textual content and numeric information from neighborhood documents, databases, and the net. set up, rearrange, and fresh the information. paintings with relational and non-relational databases, facts visualization, and straightforward predictive research (regressions, clustering, and selection trees). See how common info research difficulties are dealt with. and take a look at your hand at your individual options to quite a few medium-scale tasks which are enjoyable to paintings on and glance solid in your resume.

Keep this useful fast consultant at your facet even if you are a pupil, an entry-level info technology specialist changing from R to Python, or a professional Python developer who does not are looking to memorize each functionality and option.

What You Need:

You desire a respectable distribution of Python 3.3 or above that comes with no less than NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. an excellent distribution that meets the necessities is Anaconda, on hand at no cost from for those who plan to establish your individual database servers, you furthermore mght desire MySQL ( and MongoDB ( either applications are loose and run on home windows, Linux, and Mac OS.

Show description

Read or Download Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value PDF

Similar data modeling & design books

Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization

This ebook constitutes a suite of study achievements mature sufficient to supply an organization and trustworthy foundation on modular ontologies. It offers the reader an in depth research of the state-of-the-art of the study zone and discusses the new ideas, theories and methods for wisdom modularization.

Advances in Object-Oriented Data Modeling

Until eventually lately, details structures were designed round diversified enterprise features, equivalent to bills payable and stock regulate. Object-oriented modeling, by contrast, buildings platforms round the data--the objects--that make up many of the enterprise services. simply because information regarding a selected functionality is proscribed to at least one place--to the object--the approach is protected against the results of swap.

Introduction To Database Management System

Designed in particular for a unmarried semester, first direction on database structures, there are four features that differentiate our ebook from the remaining. simplicity - commonly, the expertise of database structures could be very obscure. There are

Extra resources for Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value

Example text

Load(iFile) You can store more than one object in a pickle file. The function load() either returns the next object from a pickle file or raises an exception if the end of the file is detected. You can also use pickle to store intermediate data processing results that are unlikely to be processed by software with no access to pickle. report erratum • discuss Chapter 2. Core Python for Data Science • 28 Your Turn In this chapter, you looked at how to extract data from local disk files and the Internet, store them into appropriate data structures, extract bits and pieces matching certain patterns, and pickle for future processing.

Start mysql on the shell command line: c:\myProject> mysql -u root -p Enter password: Welcome to the MySQL monitor. Commands end with ; or \g. «More mysql output» mysql> Enter all further instructions at the mysql command-line prompt. 2. Create a new database user (“dsuser”) and password (“badpassw0rd”): CREATE USER 'dsuser'@'localhost' IDENTIFIED BY 'badpassw0rd'; 3. Create a new database for the project (“dsdb”): CREATE DATABASE dsdb; 4. * TO 'dsuser'@'localhost'; Now, it’s time to create a new table in an existing database.

Report erratum • discuss Comprehending Lists Through List Comprehension • 15 Unit 6 Comprehending Lists Through List Comprehension List comprehension is an expression that transforms a collection (not necessarily a list) into a list. It is used to apply the same operation to all or some list elements, such as converting all elements to uppercase or raising them all to a power. The transformation process looks like this: 1. The expression iterates over the collection and visits the items from the collection.

Download PDF sample

Rated 4.65 of 5 – based on 43 votes