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Statistics for Data Science

More Information
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
About

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.

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
Page Count 286
Course Length 8 hours 34 minutes
ISBN9781788290678
Date Of Publication 17 Nov 2017

Authors

James Miller

James Miller is an innovator and accomplished Sr. Project Lead and Solution Architect with 37 years experience. of extensive design and development across multiple platforms and technologies. Roles include leveraging his consulting experience to provide hands-on leadership in all phases of advanced analytics and related technology projects, providing recommendations for process improvement, report accuracy, adoption of disruptive technologies, enablement, and insight identification.
Author: Statistics for Data Science, Mastering Predictive Analytics w/R, Big Data Visualization, Learning Watson Analytics, Implementing Splunk, Mastering Splunk, 5 Guiding Principles of a Successful Center of Excellence, and TM1 Developer's Certification Guide.