Pengenalan Visualisasi Data pada Studi Geotermal

Have you ever experienced when you prepared complete data for the presentation, but your audience is still confused and doesn't understand what we want to explain? This problem doesn't necessarily come from the audience, it could be that your way of presenting the data is not optimal, so the message you want to explain is difficult for the audience to accept. Then what is the best way to deliver data? The answer is data visualization. This video will reveal the introduction of data visualization.

In this video, we will discuss how do we transform a data set to be more representative. We called it Data Transformation into visuals.

Colour is an important aspect of data visualization. In addition to making data visualization interesting, color can also be used to convey information. Unfortunately, color is often misused. Sometimes, using the wrong color can make our data difficult to understand/generate wrong information. So, how do we use color properly?

Plotly is a data visualization software that uses the Python programming language. This software is open-source/free. This video will show you an introduction to using Plotly to create and beautify plots.

Plotly is a data visualization software that uses the Python programming language. This software is open-source/free. This video will show you an introduction to using Plotly to create and beautify plots.

We will reveal the advantages and disadvantages of line plots. At the end of the video, it is briefly explained how to visualize the line plot using Plotly with the production well test dataset.

We will discuss line plot visualization using Plotly in more detail using a dataset of geothermal power generation capacity in the world including visualization with more than one line, customization of graphic templates, and adding annotation options for easy interpretation

In this video, you will discover two commonly used charts for categorical data, which are pie charts and bar charts. In the geothermal industry, they’re also used to visualize non-technical data. However, most of us didn’t aware of when we have to use the bar or the pie chart. You will discover the different features of them as well as the strength and the weakness of using each of them.

In this video, you will discover the tutorial on how to create a pie chart and bar chart using Python! We will also explain the visualization’s pro tips in both charts and seeing what’s the strength or weaknesses of each chart.

Statistical is divided into two types, descriptive and inferential statistics. In the geothermal aspect, statistics that are often used are descriptive statistics, which convert complicated rows of data into plots that are easy to understand and analyze. For the purposes of data visualization using scatter plots, lines, bars, while data analysis uses error bars, histograms, boxplots, and histogram studies, and heat maps.

Statistical is divided into two types, descriptive and inferential statistics. In the geothermal aspect, statistics that are often used are descriptive statistics, which convert complicated rows of data into plots that are easy to understand and analyze. For the purposes of data visualization using scatter plots, lines, bars, while data analysis uses error bars, histograms, boxplots, and histogram studies, and heat maps.

In this video, you will discover the use of scientific charts in the geothermal industry. Scientific charts are mostly used for technical purposes. One of the examples is a ternary plot, a plot commonly found at geochemical analysis. You will understand how the geochemist this type of plot to understand the geothermal resource’s condition. Worry not, for you who do not have a geoscience background, we will guide you in easy way!

In this video, you will discover how to create a ternary plot as one of scientific charts using Python and Plotly library using geothermal dataset. You will also discover the visualization’s tips and tricks.