Practical Statistics for Data Scientists: 50 Essential Concepts, 1st Edition

Practical Statistics for Data Scientists: 50 Essential Concepts, 1st Edition

AUTHOR: Peter Bruce

PUBLISHER: O'Reilly Media

PAGES: 318

ISBN-10: 1491952962; ISBN-13: 978-1491952962

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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

About the Author
Peter Bruce founded and grew the Institute for Statistics Education at Statistics.com, which now offers about 100 courses in statistics, roughly a third of which are aimed at the data scientist. In recruiting top authors as instructors and forging a marketing strategy to reach professional data scientists, Peter has developed both a broad view of the target market, and his own expertise to reach it.

Andrew Bruce has over 30 years of experience in statistics and data science in academia, government and business. He has a Ph.D. in statistics from the University of Washington and published numerous papers in refereed journals. He has developed statistical-based solutions to a wide range of problems faced by a variety of industries, from established financial firms to internet startups, and offers a deep understanding the practice of data science.

Book Category

Android Developer / Asp.Net / Asp.Net MVC / Blockchain / C# / C++ / Computer Science / Database / Game Developer / Java / JavaScript / jQuery / Linux / Maven / MS Sql / MySQL / Networking / Oracle / PHP / Python / Spring / VB.Net / Visual Studio / Web Developer

HRMS & Payroll Web Application

Timekeeping System and Timesheet Processing

Paper timesheets daily time record is to track when employees start and end their work hours a day, but today's technologies used biometric finger scanner or other digital device to record daily time attendance In/Out. read more »

HRMS Employee Shift Schedule

Fixed shift schedule in every employee from Monday to Sunday is required during the processing of timesheet to get man-hour distribution report. This module designed a flexible shift because rest day work can occur in any days, just example of working in Shopping Mall that operates 7 days a week. read more »

HRMS Employee Online Account

Employee can check their available vacation leave, sick leave, pay slip or ability to work at home. Filing of overtime work, request temporary shift schedule and leave benefits is a convenient way using a paperless online form. read more »

Employee Self-Service Online Filing Form

Delegate work to your employees and get more productive right away with a smart self-service dashboard user account for every employee. Modern HR management tools can help you remove the daily routine task that the system can handle it automatically and easy. read more »

Broken Time Schedule Data Entry and Approval Form

Broken time schedule is not suitable on automating work distribution need module data entry to handle the accurate computation that system get the computed hours during the timesheet processing. The logic of computation is time difference for every time in/out entry that excess of regular 8 hours is overtime work. The approver decide if the overtime work is required to deduct 1 hour OT hour lunch break or OTND lunch break before it approve. read more »