FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title School of Data Handbook
- Author(s) SchoolOfData.org
- Publisher: SchoolOfData.org (2013)
- Hardcover/Paperback N/A
- eBook HTML
- Language: English
- ISBN-10/ASIN: N/A
- ISBN-13: N/A
- Share This:
This book is something like a traditional textbook it will provide the detail and background theory to support the Data Science courses and challenges. It will guide you through the key stages of a data project. These stages can be thought of as a pipeline, or a process.
The Handbook should be accessible to all learners. It comes with a Glossary explaining the important terms and concepts.
Reviews, Rating, and Recommendations: Related Book Categories:- Data Analysis and Data Mining, Big Data
- The R Programming Language
- Statistics, Mathematical Statistics, and SAS Programming
-
Introduction to Statistics and Data Analysis: A Case-Based Approach
This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains hands-on exercises with real data - mostly from social sciences. It presents four key ingredients of statistical data analysis.
-
Introduction to Statistics and Data Analysis (Geoffrey M. Boynton)
Build a solid foundation in data analysis, This guide starts with an overview of statistics and why it is so important. Be confident that you understand what your data are telling you and that you can explain the results to others!
-
Data Analysis with Python (Numpy, Matplotlib and Pandas)
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide, using Python. Equipped with the skills to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
-
An Introduction to Spatial Data Analysis and Statistics in R
This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for collecting and using data with location attached.
-
An Introduction to Spatial Data Analysis and Visualization in R
This book provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.
-
Data Mining with R: Learning with Case Studies (Luis Torgo)
Introduce the reader to the use of R as a tool for performing data mining and statistical computing and graphics. The large set of available packages make this tool an excellent alternative to the existing (and expensive!) data mining tools.
-
An Introduction to R and Python for Data Analysis
This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.
-
Python for Data Analysis: Pandas, NumPy, and Jupyter
The focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.
-
Python for Econometrics, Statistics, and Data Analysis
This book is designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research for econometrics, statistics or general numerical analysis using Python.
-
Analyzing US Census Data: Methods, Maps, and Models in R
This book introduces readers to tools in the R programming language for accessing and analyzing Census data from the United States Census Bureau and shows how to carry out demographic analyses in a single computing environment.
-
The Fundamentals of People Analytics: With Applications in R
Human capital is an organization?s most important asset. Address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using open-source software.
-
Introduction to Statistical Data Analysis with R (Matthias Kohl)
The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.
-
Metalearning: Applications to AutoML and Data Mining
This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.
-
Kafka: The Definitive Guide: Real-Time Data and Stream Processing
Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.
-
Making Sense of Stream Processing: Behind Apache Kafka
This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.
-
Hands-On Data Visualization: From Spreadsheets to Code
This book takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for anyone who wants to take data out of spreadsheets and turn it into lively interactive stories.
-
Fundamentals of Data Visualization: Informative Figures
This book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures, teaches you the elements most critical to successful data visualization.
:
|
|