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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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JSON XML learn Today for data analytics jobs

JavaScript Object Notation (JSON) was invented by Douglas Crockford as a subset of JavaScript syntax to be a lightweight data format that is easily readable and writable by both humans and machines. In general, JSON is considered terse when compared to other interchange formats.

 After you become familiar with JSON, you will find it fairly easy to read complex JSON data structures. Even though JSON is based on a subset of the JavaScript programming language, it is considered language independent.

JSON XML

The flexibility of XML has made it increasingly prevalent in programming environments. Unlike the Unix® world, where configuration files are usually text files with either tab-delimited name/value pairs or colon-separated fields, configuration files in the open source world are often XML documents.

Most well-known application servers also use XML-based configuration files. The Ant utility relies on XML-based files for defining tasks.

Data Integration

A tremendous amount of data in the business world and scientific community does not use the JSON or XML format. To give you some perspective, roughly 80% to 90% of all software programs were written in either COBOL or Fortran™ in the early 1990s (and NASA scientists were still using Fortran in 2004).

Therefore, data integration and migration can be a complex problem. The movement toward XML as a standard for data representation is intended to simplify the problem of exchanging data between systems.

You probably already know that XML is ubiquitous in the Java world, yet you might be asking yourself one question: What's all the fuss about XML? In broad terms, XML is to data what relational theory is to databases; both provide a standardized mechanism for representing data.

XML Documents

A nontrivial database schema consists of a set of tables in which there is some type of parent/child (or master/detail) relationship in which data can be viewed hierarchically.

An XML document also represents data in a parent/child relationship. One important difference is that database schemas can model many-to-many relationships such as the many-to-many relationships that exists between a student's entity and a class's entity.

XML documents are strictly one-to-many, with a single root node. People sometimes make the analogy that XML is to data what Java is to code; both are portable, which means you avoid the problems that are inherent in proprietary systems.

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