10 Classic Algorithms and Methods of Data Science

Discover key concepts for more than 10 classic algorithms and methods, including:

• A / B Test

• Detection of anomalies

• Rules of Association

• Clustering

• Decision trees and random forests

• Regression Analysis

• Analysis of social networks

• Neural networks

To help you revisit what you've learned, we've also included:

• Cheat sheets comparing pros and cons of algorithms

• Summary of the final chapters

• List of glossaries of commonly used terms

What is this book for?

You! This is a great opportunity to learn more about data science, technology and technology.

Why should I be interested in data science?

You saw how the data revolutionized the way we live and work. Google uses it to generate queries and queries. Recently, data science algorithms were also used to boost the development of digital personal assistants and standalone vehicles. Understanding how these algorithms work is crucial to assessing our potential impact on our future.

How will this book add value to my work?

Data science algorithms can be applied across a wide range of fields, whether for customer profiles or medical diagnostics, these algorithms can provide critical information to help you make more strategic decisions.

What do I have to say?

"... Having become acquainted with the work of Annalyn Ng and Kenneth Soo some time ago, it is no surprise that the book offers its title promise. It has a high level of quality and is not limited to, in any way, but is not limited to this, and is abbreviated and concise. "

A / B Test

The A / B test is a method for comparing two versions of a Web page or one that is used to determine which one is the best. AB is the most commonly used statistical analysis of the variability of a statistical analysis.

Anomaly detection

Anomaly detection is the identification of data points, items, observations or events that do not conform to the given pattern of a particular group. These anomalies occur very rarely, but can mean a large and significant threat, such as cybercrime or fraud. Detection of anomalies in the presence of an anomaly. Anomaly detection is also known as detection of aberrant values.

Rules of Association

An antecedent is an item found in the data. A consequent is an item found in combination with the antecedent. Association rules are created by analyzing data for frequent patterns and then using support and trusting the criteria to identify the most important relationships.

Decision Trees and Random Forests

Decision trees are a type of model used for both classification and regression. Trees respond to sequential questions. The model behaves with "conditions that ultimately produce a specific outcome. This is a great place to relax and unwind. Random forests or random decision forests are a set of methods for classification, regression, and other tasks, which are used to build a multitude of decision trees. ) of individual trees. Random decision forests correct the habit of decision trees to overcome in their training set.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes to estimate the relationships between variables. ... Regression analysis is also used to understand which independent variables are related to the dependent variable and to explore the forms of these relationships.

Social Networking Analysis

The analysis of social networks (ANS) is the process of studying social structures through the use of networks and graphic theory. It characterizes network structures in terms of nodes (individual actors, people or things on the network) and the links, borders, or links (relationships or interactions) that connect them.

Neural networks

Neural networks have wide appeal to many researchers because of their proximity to the structure of the brain, a feature not shared by more traditional systems.

In an analogy to the brain, an entity made up of interconnected neurons, neural networks consist of interconnected processing elements called units, which respond in parallel to a set of input signals given to each. The unit is the equivalent of its brain counterpart, the neuron.