Book

Practical statistics for data scientists : 50+ essential concepts using R and Python/2ND /ED

---

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.

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.

With this book, you?ll learn:

Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher-quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that "learn" from data
Unsupervised learning methods for extracting meaning from unlabeled data

---

Availability

Detail Information

Series Title

-

Call Number

001.422 BRU

Publisher

Beijing : O\'Reilly Media, Inc., Sebastopol, CA., 2020

Collation

xvi, 298 pages : illustrations ; 24 cm

Language

English

ISBN/ISSN

9781118-88-1354

Classification

001.422

Content Type

-

Media Type

-

Carrier Type

-

Edition

2nd Edition

Subject(s)

1. Computer Science 2. Software Engineering 3. Dat
Data Science Information Technology

Specific Detail Info

-

Statement of Responsibility

Bruce, Peter

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment