**R-Algo Engineering Big Data** offers data science and machine learning R tutorials for big data analysis. R-Algo Engineering Big Data can help instruct users on the proper methods to visualize data for mining data, machine learning, data science, and related algorithms. Data topics discussed are **Artificial Intelligence** **(AI)**, **Big Data**, **Data Mining**, **Data Science** and **Machine Learning**. Many R tutorials are covered from basic to advanced knowledge of R. A few algorithms used in R tutorials are **Apriori**, **Artificial Neural Networks**, **Decision Trees**, **K Means Clustering**, **K-nearest Neighbors (KNN)**, **Linear Regression**, **Logistic Regression**, **Naive Bayes Classifier**, **Random Forests**, and **Support Vector Machine**.

# R Tutorials

R is a programming language that facilitates statistics and data mining. R tutorials are quick, easy-to-use tutorials that guide users through multiple statistical concepts that use R. R tutorials help show users how machine learning algorithms work and to analyze and visualize data with data analysis. Many R tutorials provide a basic explanation with plenty of explanations on each step to produce a valid output. R tutorials will then show real-world scenarios and how to process them in the R language. These tutorials are essential to understanding what is often viewed as a difficult and opaque field. R tutorials cover topics such as earthquake magnitude scale and class, technical analysis and data mining techniques for stock prices using quantmod, text mining and creating word clouds from SMS messages, vehicle MPG based on engine size and many more tutorials to help educate powerful methods of machine learning.

# Top Data Topics to Follow

Algorithms are tools used to solve problems inserted into machines and computers. Basically, algorithms are unambiguous pathways to answer a question. These functions use formal logic and break questions down into a series of basic steps. They are at the heart of computer processing and can be expanded to cover millions of data points. When found in machine learning and artificial intelligence, they are tools used to produce output from a set of input. In an artificial neural network, algorithms are attached to nodes. The algorithms often do not change as the network learns and grows. Instead, the algorithm is weighted differently as time goes on and the machine learns. Other algorithms that are discussed are Apriori, Decision Trees, K Means Clustering, K-nearest Neighbors, Linear Regression, Logistic Regression, Naive Bayes Classifier, Random Forests, and Support Vector Machine.

Artificial intelligence, also known as AI, is a step above basic computer technology. Traditional computers simply process the data inputted into them to form outputs. They are limited by the information implanted by the operator. Artificial intelligence works beyond the operator. It uses neural networks, programs, and algorithms to act independently of human beings. Artificial intelligence can produce work from either supervised or unsupervised learning programs. The artificial intelligence program is able to produce test runs thousands of times faster than operators can upload information and tweak variables. It is an incredibly efficient way to perfect the machine learning process. AI makes the impossible, possible for machines to learn from humans by experience. Machines are able to adjust to new inputs and the output are tasks similar to a human output.

Big data is a broad term that can mean a large variety of datasets. These large volumes of datasets can be structured and unstructured. At its heart, big data refers to amounts of data that cannot easily be processed by human beings. In part of analyzing the data, patterns must be found to create a connection across the data using machine learning algorithms or data visualization. Big data may involve quantitative or qualitative data of all kinds. Qualitative data often must be converted into quantitative data through the process of coding. Big data differs from traditional data in its effectiveness over a large scale. With typical data interpreted by humans, more data means slower analysis. Numbers can only be crunched over a long period of time. Big data requires more and more data to make accurate predictions. There are more detailed means and clearer regression patterns. It can expand far beyond the possibilities of people or simple computer calculations.

Data mining is the use of computer programs to analyze large datasets. Big data and data mining go hand in hand. These programs use algorithms to make sense of thousands or millions of entries. They may work with both quantitative and qualitative data. Data mining utilizes the information of big data and becomes more productive as data totals increase over time. The process identifies patterns, outliers, and statistical attributes to the data set. This information may also be relevant for predictions into the future. More data means a better prediction of how a big data spread will develop in any number of fields. All of this work can be uncovered using a basic data mining program. Data mining will help the process of connecting patterns and correlations within data to predict an outcome within a given algorithm.

Data science is the study of the structure and relationships of data. Data science also helps solve complex problems with the blend of machine learning algorithms and technology. The field can be either abstract or practical. However, practical data science is often applied through data mining and artificial intelligence. It involves looking at different relationships and detecting patterns throughout a massive array of data. The studies of data science bring in information from a number of different fields. Statistics plays a key role. It helps to identify percentages and guide predictions in the development of data. Formal logic and computer science also help guide data science. Computers help to organize and analyze data with the support of formal logic. The procedures of data science make sense of data in a disparate number of situations.

Machine learning involves learning procedures by computers outside of the actions of operators. It is the process by which artificial intelligence gains more information over time. Computers are allowed to respond to changing circumstances and learn over time. This learning may be in the form of artificial neural networks where nodes are weighted differently over time. Algorithms are the basis by which these machines learn and process data. Learning occurs on several scales as the computers involve shift their focus to process available information. Machine learning algorithms are based on pattern recognition methods and how computers can perform tasks without being programmed. Machine learning will revolutionize what we can do and what we thought was impossible, will become possible.