Data Science Fundamentals : Pocket Primer, Paperback by Campesato, Oswald, Br...

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
Book Title
Data Science Fundamentals : Pocket Primer
ISBN
9781683927334
Category

About this product

Product Identifiers

Publisher
Mercury Learning & Information
ISBN-10
1683927338
ISBN-13
9781683927334
eBay Product ID (ePID)
22050382777

Product Key Features

Number of Pages
450 Pages
Language
English
Publication Name
Data Science Fundamentals Pocket Primer
Subject
Intelligence (Ai) & Semantics, General, Data Processing, Programming Languages / Python
Publication Year
2021
Type
Textbook
Author
Oswald Campesato
Subject Area
Mathematics, Computers
Series
Pocket Primer Ser.
Format
Trade Paperback

Dimensions

Item Weight
29 Oz
Item Length
9 in
Item Width
6 in

Additional Product Features

Intended Audience
Scholarly & Professional
Dewey Edition
23
Grade From
College Graduate Student
Dewey Decimal
005.74
Grade To
College Graduate Student
Table Of Content
1: Working with Data. 2: Introduction to Probability and Statistics. 3: Linear Algebra Concepts. 4: Introduction to Python. 5: Introduction to NumPy. 6: Introduction to Pandas. 7: Introduction to R. 8: Regular Expressions. 9: SQL and NoSQL. 10: Data Visualization. Index.
Synopsis
As part of the best-selling Pocket Primer Series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular ......, As part of the best-selling Pocket Primer Series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 and linear algebra Provides a thorough introduction to data visualization and regular expressions Covers NumPy, Pandas, R, and SQL Introduces probability and statistical concepts Features numerous code samples throughout Includes companion files with source code and figures
LC Classification Number
QA76.9.D32

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