Practical Data Science With Jupyter (Record no. 43264)

MARC details
000 -LEADER
fixed length control field 02055nam a2200217Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230309s9999 xx 000 0 und d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789389898064
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 GUP
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gupta, Prateek
245 ## - TITLE STATEMENT
Title Practical Data Science With Jupyter
250 ## - EDITION STATEMENT
Edition statement 2
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Bpb Publications
Place of publication, distribution, etc New Delhi
Date of publication, distribution, etc 2021
300 ## - PHYSICAL DESCRIPTION
Extent 341
520 ## - SUMMARY, ETC.
Summary, etc Solve business problems with data-driven techniques and easy-to-follow Python examplesKey FeaturesEssential coverage on statistics and data science techniques.Exposure to Jupyter, PyCharm, and use of GitHub.Real use-cases, best practices, and smart techniques on the use of data science for data applications.DescriptionThis book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.What you will learn Rapid understanding of Python concepts for data science applications.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book

No items available.