# Are you a Budding Data Analyst?

**Are you a Budding Data Analyst? Grab your Tips for your Best Data Visualization.**

Here is the blog for beginners who start with Data Analysis and are looking for better Visualization.

Before going to the tips, we should know what Data Visualization is and then why it is necessary.

**What is Data visualization?**

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

**Data visualization, why it is necessary?**

1 Data visualization helps tell stories by curating data into a form that is easier to understand, highlighting the trends and outliers.

2. Good visualization tells a story, removing the superfluous from data and highlighting useful information.

3. Data Visualization allows us to process information faster and use that information to boost productivity and results.

4. Effective Data Visualization is *the key for go through Big Data. It can solve any data inefficiencies and easily and instantly absorb vast amounts of data.*

*5. *A good visualization *used to track performance, monitor customer behavior, and measure the effectiveness of processes, for instance*

6. An Effective visualization helps to take smart decisions or enlighten users about the true performance of their businesses.

**So here are the Tips to create Effective Visualization:**

**1. An Indicators show one KPI, clearly**

A ** key performance indicator** (KPI) is a measurable value that demonstrates how effectively a dataset is achieving key objectives.

**2. Line charts display trends. ...**

Line charts connect individual data points in a view. They provide a simple way to visualize a sequence of values and are useful when you want to see trends over time, or to forecast future values.

**3. Bar charts break things down, simply. ...**

** Bar charts** enable us to compare numerical values like integers and percentages. They use the length of each bar to represent the value of each variable.

**4. Column charts compare values side-by-side. ...**

Column charts are useful for **showing data changes over a while or for illustrating comparisons among items**. In column charts, categories are typically organized along the horizontal axis and values along the vertical axis.

**5. Pie charts clearly show proportions. ...**

A pie chart helps organize and show data as a percentage of a whole. True to the name, this kind of visualization uses a circle to represent the whole, and slices of that circle, or “pie”, to represent the specific categories that compose the whole.

**6. Area charts compare proportions. ...**

An area chart is a line chart where the area between the line and the axis is shaded with color. These charts are typically used to represent accumulated totals over time and are the conventional way to display stacked lines.

**7. Pivot table easily present key figures. ...**

Pivot is one such feature in Tableau as you can use it to streamline your data into rows or columns that will make for easy deductions and conclusions from them. Tableau has this affinity of making your data ‘tall’ rather than ‘wide’ as opposed to other platforms therefore, the preferred data Pivot in Tableau is gotten from columns to rows.

**Pivot Table:**

**8. Scatter charts: distribution and relationships.**

The scatter plot is a visualization used to compare two measures. More aspects of the data set can be expressed through the use of shape, color, and size within the scatter plot, also reference lines can be added to express correlation. Scatter plots offer a good way to do ad hoc analysis.

**9. Bubble charts: understand multiple variables**

Bubble charts display data as a cluster of circles. Each of the values in the dimension field represents a circle whereas the values of measure represent the size of those circles. As the values are not going to be presented in any row or column, you can drag the required fields to different shelves under the marks card

**10. Tree map display hierarchies, compare values**

A tree map is a visualization that nests rectangles in hierarchies so you can compare different dimension combinations across one or two measures (one for size; one for color) and quickly interpret their respective contributions to the whole. When used poorly, tree maps are not much more than an **alternative pie chart**.

**11. Polar charts show relationships between multiple variables.**

Polar Area Chart or Coxcomb chart looks similar to the Pie chart, however, the angle of all the slices is equal and the length of the slice that extends radially from the center represents quantity.

**12. Area/scatter maps show geographic data**

**A map view with one data point is created**. Since a geographic role is assigned to a Country, Tableau creates a map view. If you double-click any other field, such as a dimension or measure, Tableau adds that field to the Rows or Columns shelf, or the Marks card, depending on what you already have in the view.

**13. Funnel charts display a pipeline, typically for sales figures**

A tableau funnel chart is **a type of visualization that represents linear workflows in decreasing order**. It visually represents the progression of a business process and helps the user get a systematic view of various data values.

**14. For advanced users: Fisheye/Cartesian distortion to zoom in on details**

**Fisheye distortion**

This chart magnifies the local region around the mouse while leaving the larger graph unaffected for context.

Circular fisheye is only one of many possible distortion functions. Two disadvantages of circular distortion are that it compresses (rather than magnifies) the area near the circumference of the circle and that it requires curved grid lines to show the distortion accurately. The latter makes it unsuitable for visualizations that have quantitative position encodings, such as scatterplots.

**Cartesian distortion:**

It is a different function that magnifies continuously to avoid local minimization. Furthermore, they demonstrate applying the distortion to each dimension separately, resulting in *Cartesian distortion*:

With this technique, straight lines parallel to the *x* or *y* axis remain straight even after distortion. This means you can use standard axes in conjunction with fisheye distortion in scatterplots:

**Zoom in on:**

** Bolder Visualization makes better decisions.**

**Thanks to the following sites and are useful for this blog:**

**https://makeavizz.com**