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Statistics for Data Science by Miller, James D. Paperback / softback Book The
FREE US DELIVERY | ISBN: 1788290674 | Quality Books
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A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Item specifics
- Condition
- ISBN
- 1788290674
- EAN
- 9781788290678
- Release Title
- Statistics for Data Science
- Artist
- Miller, James D.
- Brand
- N/A
- Colour
- N/A
- Book Title
- Statistics for Data Science
About this product
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1788290674
ISBN-13
9781788290678
eBay Product ID (ePID)
240824641
Product Key Features
Number of Pages
286 Pages
Language
English
Publication Name
Statistics for Data Science
Subject
Programming Languages / General, Machine Theory, Mathematical & Statistical Software, Probability & Statistics / General, Data Processing
Publication Year
2017
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Length
92.5 in
Item Width
75 in
Additional Product Features
Intended Audience
Trade
Synopsis
Get your statistics basics right before diving into the world of data science Key Features No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Book Description Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. What you will learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically Who this book is for This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. ]]>, Get your statistics basics right before diving into the world of data science About This Book - No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; - Implement statistics in data science tasks such as data cleaning, mining, and analysis - Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn - Analyze the transition from a data developer to a data scientist mindset - Get acquainted with the R programs and the logic used for statistical computations - Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more - Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis - Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks - Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples
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- f***i (321)- Feedback left by buyer.Past monthVerified purchaseBook was as described and was well packaged.
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Seller feedback (1,561,479)
- f***i (321)- Feedback left by buyer.Past monthVerified purchaseBook was as described and was well packaged.
- e***h (287)- Feedback left by buyer.Past monthVerified purchaseBook arrived quickly and in the condition described. Thank you!
- 9***9 (7)- Feedback left by buyer.Past monthVerified purchaseItem arrived quickly and as described. Good purchase.