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

As a data analyst, you will often find yourself working with numerical data, such as sales figures, market trends, or financial metrics. This type of data is particularly important because it is used to make informed decisions and understand trends in your data. However, in order to make the most of this data, you need to be able to perform calculations on it. Tableau provides a range of features to help you work with numerical data, including numeric calculations, which allow you to perform calculations on numerical values in your data. In this article, we will take a closer look at Tableau numeric calculations and how they can be used by data analysts to make better decisions.

Tableau numeric calculations allow you to perform a wide range of calculations on numerical values in your data, including adding, subtracting, multiplying, dividing, and finding the average of numbers. With Tableau numeric calculations, you can perform these calculations on individual data points or on aggregated data, such as a sum or an average. This allows you to perform complex calculations and gain insights into your data that would otherwise be difficult to obtain.

One of the key benefits of Tableau numeric calculations is that they allow you to manipulate numerical data in a way that makes it easier to work with. For example, you may want to find the average sales per customer, or calculate the percentage of sales that come from a particular region. With Tableau numeric calculations, you can easily perform these manipulations and include the results in your visualization, so that you can work with your data in a more meaningful way.

Another benefit of Tableau numeric calculations is that they allow you to perform calculations that are not possible with traditional aggregations. For example, you may want to calculate the running total of sales, or determine the growth rate of a particular metric over time. With Tableau numeric calculations, you can easily perform these calculations and include the results in your visualization, so that you can make better decisions based on your data.

In addition to the benefits of Tableau numeric calculations, they are also easy to use, even for those with little to no experience with data analysis. Tableau’s intuitive interface and straightforward numeric calculation options make it easy to get started, so that you can quickly start working with your data and making better decisions based on your data.

In conclusion, Tableau numeric calculations are a powerful and versatile tool for data analysts looking to work with numerical data. Whether you need to perform basic arithmetic operations, such as adding and subtracting numbers, or perform more complex calculations, such as finding the growth rate of a particular metric over time, Tableau numeric calculations make it easy to get the information you need, so that you can make better decisions based on your data. With its intuitive interface and powerful numeric calculation capabilities, Tableau is the perfect tool for data analysts looking to get the most out of their numerical data.

# Tableau for Data Analyst – Tableau Numeric Calculations

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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

# Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
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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

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

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