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FAQ of my students

Hello everyone

My name is Dina, I have been teaching data analytics for the Techord program at the Code:me programming School for two years now. I was a little confused when I was asked to write a blog post, and decided that I would start directly from the “FAQ of my students" section.

When we studied Power BI and Excel, many people wondered why we use some graphs for certain data and other graphs for others. As I understand it, it is necessary to use this particular diagram, and not another one.

Therefore, today I want to answer the following question: * “What are the main charts I use for analytics and reporting?”*

*Chart/Graph* is a valuable tool for visual representation of data, making it easier to understand trends, patterns and relationships.

In general, the choice of graphs for data visualization depends on several factors, including the type of data, the purpose of the visualization and the audience it is intended for. For example, you can use line graphs to display time series, and bar charts to compare categorical data. It is also important to take into account the readability and clarity of the graph for users. We'll talk about this in another post.

Let's take a detailed look at the main types of graphs and charts, what they are used for and when.

1. *Line Chart*

- *Purpose:* Displaying trends and changes over time.

- *Application:* It displays data that follows a sequence or trend, such as stock prices over time, temperature changes, profit changes, or trends in any numerical data over months or years.

2. *Bar Chart*

- *Purpose:* Compare categories or display individual data.

- *Application:* Displaying comparisons between different groups or categories. It is useful for displaying changes over time or comparing multiple items, for example, comparing sales figures of various products and displaying survey results by category.

3. *Histogram (Histogramm)*

- *Purpose:* Displaying the distribution and frequency of numeric data.

- *Application:* Visualization of the frequency of data in certain ranges or intervals. This is useful for understanding distribution and identifying patterns or outliers, for example, a market share display illustrating the proportions of budget allocation.

4. *Pie Chart*

- *Purpose:* Displaying parts of the whole or percentages.

- *Application:* Illustrates the proportional or percentage distribution of categories in a dataset. It is effective when demonstrating relative sizes or percentages in a simple and understandable format, for example, a display of market share illustrating the proportions of budget allocation.

5. *Scatter Plot*

- *Purpose:* Show the relationship or correlation between two variables.

- *Application:* Visualization of the correlation of two variables. It helps to identify patterns, clusters or trends between variables, for example, displaying the correlation between height and weight, analyzing the relationship between advertising costs and sales.

These are the main charts that I use for analytics and reporting. Of course, with the development of technology and AI, the number and variety of diagrams is growing. However, to begin with, I think it's better to hone these diagrams and then move on to more complex and interesting ones.

I hope it was helpful.

By the way, if you have a question that you want a detailed answer to, feel free to ask in the comments. I will be glad to help.

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Спасибо за полезную статью.


Информативный ответ на вопрос, который интересует многих начинающих аналитиков данных. Спасибо