Hands-On Data Science with R BOOK FULL FREE PDF 2022.

 Hands-On Data Science with R :

Techniques to perform data manipulation and mining to build smart analytical models using R             

BOOK FULL FREE PDF 2022.

Data Science with R:Description:

Software R is an indispensable tool for statistics, data visualization and data science in both the academic and business world. 

This is explained by its three main qualities: it is free, very complete and constantly growing. Recently, it has been able to adapt to enter the era of big data and to allow to collect and process heterogeneous data of very large dimensions (from the Web, textual data, etc.). 

This article is divided into two main parts: the first focuses on the operation of software R while the second implements about thirty statistical methods through sheets.

These sheets are each based on a concrete example and scan a wide range of techniques for processing data. 

This book is aimed at beginners as well as regular users of R.

It will allow them to quickly produce simple or elaborate statistical graphs and treatments. Data Science, data science in French, refers to the production of knowledge from the manipulation and processing of huge volumes of data.

For the exploitation and processing of all these data, the data scientist uses complex statistical models as well as programming languages such as python or R.

R is a programming language and free software for statistics and data science supported by the R Foundation for Statistical Computing. 

During this course to learn Data Science with R online, you will be accompanied by Amandine Velt, data scientist expert in data manipulation techniques and R language. 

You will start this course with the installation and presentation of the software and R language before taking your first steps with R and then you will discuss the R matrices. 

Afterwards, you will learn to master R data frames as well as the basics of R programming. You will also discover advanced data manipulation with DPLYR and advanced data visualization with GGPLOT. 

 Finally, you will end this course with a practical case of data science in which you will apply machine learning algorithms.

Following this course to learn data science with R online, you will have in mind all methods and techniques to manipulate and interpret data with the software R and soon, the notions of functions, Matrices, vectors, GGPLOT and DPLYR will have no secrets for you.

Data Science:

In general terms, data science is the extraction of knowledge from datasets. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and ideas from many structural and unstructured data. It is often associated with big data and data analysis. She uses techniques and theories from many fields in the context of mathematics, statistics, computer science, theory and information technology. These include: probabilistic models, machine learning, statistical learning, computer programming, data engineering, pattern recognition, data visualization, prophetic analytics, uncertainty modeling, data storage, geovisualization, data compression and high-performance computing. Methods that adapt to mass data are particularly interesting in data science, although discipline is generally not considered to be limited to these data. Data science is a discipline that relies on mathematical, statistical, computer science (primarily a “science of digital data”) and data visualization tools. It is in full development, in academia as well as in the private and public sectors. Moore in 1991 defined statistics as the science of data6 (a definition taken up by others, including James T. McClave et al. in 19977) and U. Beck in 20018 contrasts the science of data with the science of experience, seeing a growing dissociation between these two types of science, which he believes would tend to encourage a society of risk management within a «civilization of danger».

Role of the data scientist:

The primary objective of the data scientist is to produce methods for sorting and analysing mass data and more or less complex or disjointed sources of data in order to extract useful or potentially useful information. To do this, the “data scientist” carries out its activities in 4 steps: The data mining, Data cleaning/formatting (data wrangling) Data processing Classical treatments (mathematical functions) Machine learning processes Data visualization The exploitation of results Dashboards and decision support tools (which can be integrated on websites) Publications of research results (internal to the company, or public) The datascientist is therefore often called upon to manipulate statistics, signal processing. He is therefore interested in the classification, cleaning, exploration and analysis of more or less interoperable databases.

History:

data science was coined at the 2nd Franco-Japanese Symposium on Statistics held at the Université Montpellier II (France). Participants recognized the emergence of a new discipline at the heart of which are data of all origins, sizes, types and structures. This activity must be based on accepted concepts and principles of statistics and data analysis while fully exploiting the growing power of computer tools. In 2001, William Cleveland took up much of the same ideas in a 2001 programmatic article entitled “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics”, which outlines this emerging discipline. This discipline is the result of the emergence and development of databases and the Internet and also responds to the increasing complexity and exponential volume of the number of digital data available in the world (infobededness). It has received a lot of attention lately thanks to the growing interest in "big data". However, data science is not limited to the study of databases that can be described as 'big data'.

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