Practical statistics for data scientists : (Record no. 80375)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01978nam a22002057a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220826b2020 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788194435006 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 001.422 BRU-P |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bruce, Peter |
| 245 ## - TITLE STATEMENT | |
| Title | Practical statistics for data scientists : |
| Remainder of title | 50+ essential concepts using R and Python / |
| Statement of responsibility, etc. | Peter Bruce, Andrew Bruce and Peter Gedeck |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 342 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1475.00. |
| 500 ## - GENERAL NOTE | |
| General note | Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.<br/><br/>Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.<br/><br/>With this book, you’ll learn:<br/><br/>Why exploratory data analysis is a key preliminary step in data science<br/>How random sampling can reduce bias and yield a higher-quality dataset, even with big data<br/>How the principles of experimental design yield definitive answers to questions<br/>How to use regression to estimate outcomes and detect anomalies<br/>Key classification techniques for predicting which categories a record belongs to<br/>Statistical machine learning methods that "learn" from data<br/>Unsupervised learning methods for extracting meaning from unlabeled data. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | R (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantitative research |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Total Renewals | Full call number | Barcode | Checked out | Date last seen | Date last checked out | Price effective from | Koha item type |
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| Dewey Decimal Classification | 000-002 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 26/08/2022 | 2 | 3 | 001.422 BRU-P | 46130 | 26/08/2025 | 25/08/2023 | 25/08/2023 | 26/08/2022 | Books |