# Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

In the field of business analytics, it’s important to be able to effectively handle and analyze numerical data. This could include sales figures, customer data, or any other type of numerical information that is relevant to your business. In order to make informed decisions based on this data, it’s essential to have a way to effectively store, manipulate, and analyze it.

In R, there are several numeric classes that are used to store and manipulate numerical data. These classes include integer, double, and numeric. Each of these classes has its own specific storage mode, which determines how the data is stored and manipulated in memory.

The integer class is used to store whole numbers, such as the number of products sold. This class is stored in an integer storage mode, which is optimized for fast, efficient storage of whole numbers. When working with the integer class, it’s important to keep in mind that this class can only store whole numbers, and not fractional numbers.

The double class is used to store fractional numbers, such as sales figures or customer data. This class is stored in a double-precision floating point storage mode, which is optimized for fast, efficient storage of fractional numbers. The double class is often used in business analytics, as it provides a high level of precision and accuracy, allowing you to make informed decisions based on your numerical data.

The numeric class is a combination of the integer and double classes, allowing it to store both whole and fractional numbers. This class is stored in a fixed-precision decimal storage mode, which provides a high level of accuracy and consistency, while still allowing for fast and efficient storage. The numeric class is often used when you need a balance between accuracy and efficiency in your data storage and manipulation.

In addition to the numeric classes, there are also several storage modes available in R, each with its own advantages and disadvantages. For example, the double-precision floating point storage mode is optimized for fast and efficient storage of fractional numbers, but may not provide the level of accuracy that you need for certain business analytics applications. The fixed-precision decimal storage mode provides a high level of accuracy, but may be slower and less efficient in terms of storage.

When working with numerical data in R, it’s important to choose the right numeric class and storage mode for your specific needs. This will depend on the type of data that you’re working with, as well as the level of precision and efficiency that you require. By choosing the right numeric class and storage mode, you can effectively store, manipulate, and analyze your numerical data, and make informed decisions based on your data.

In a nutshell, I would like to say that the numeric classes and storage modes in R are an essential tool for business analytics. By choosing the right class and storage mode for your specific needs, you can effectively store, manipulate, and analyze your numerical data, and make informed decisions based on your data. Whether you’re working with sales figures, customer data, or any other type of numerical information, the numeric classes and storage modes in R provide a flexible and powerful way to handle your data.

# R for Business Analytics – Numeric classes and storage modes

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# Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

## Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

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`Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.`

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

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