Plotly Go: advanced visualization in Python
Visualizing data is critical to better understand the data and analysis performed. There are several tools, free and paid, that allow you to create fantastic dashboards. However, it is possible to write a few riches in Python to get great results and be more flexible depending on the project of interest. Let’s find out how to create interactive Scatter Bubble charts with Plotly Go on a real project.
Clustering: a real project to explore data
Clustering is a very powerful tool for grouping data. There are many algorithms that can be applied, so the choice is always difficult. In addition, all clustering algorithms require parameters to work. By means of a real case study, applied to real estate data, we will combine PCA, hierarchical clustering and K-means to provide optimal clustering solutions.
BigQuery and spreadsheets: how to integrate them?
We all use Google services to manage email, write documents, create presentations, and do calculations. But have you ever wondered how these tools can be used to increase your work productivity? With Google’s simple spreadsheets and powerful BigQuery data warehouse, we can analyze large amounts of data without having to be an expert. Let’s find out how to easily integrate these two tools for our projects.
Hierarchical clustering: how it works
Clustering algorithms allow data to be grouped according to their inherent characteristics. There are many algorithms that have been developed over the years. Hierarchical clustering, thanks to a graphical representation called a dendogram, makes it possible to visualize at a glance the composition of clusters and interpret their characteristics. Let us find out, step by step, how it works and how to interpret the results obtained.
K-Means: how it works
Clustering algorithms allow data to be grouped according to their inherent characteristics. There are many algorithms that have been developed over the years. K-Means is certainly the most popular and simple one. Let’s find out, step by step, how this method succeeds in identifying clusters of data using only the number of groups that need to be extracted.
DBSCAN: how it works
Clustering algorithms allow data to be grouped according to their inherent characteristics. There are many algorithms that have been developed over the years. Among the most famous ones, we cannot forget DBSCAN. Let’s find out, step by step, how this method succeeds in identifying clusters of data of any shape and size thanks to only two parameters.
ChatGPT: what it is and what the revolutionary new text generation tool can generate
ChatGPT is OpenAI’s flagship product. This chatbot, which is completely free, allows us to interact with artificial intelligence to generate ideas, articles, code, and more. Let’s discover its features and some possible application areas.
Elasticsearch: aggregation pipeline
Elasticsearch offers the ability to extract statistics and data sets using aggregation functions. In many contexts, however, it is necessary to concatenate the results of these analyses to obtain more refined results. Let us see how aggregation pipelines allow us to obtain the results of our interest.
Elasticsearch: bucket aggregations [part 2]
With Elasticsearch’s bucket aggregations we can create groups of documents. Having seen in the previous article aggregations based on keyword type fields, we will now focus on other functions oriented to other data types. In particular, we will use aggregations for definitions of numeric ranges, dates, or groups based on georeferenced data.
Elasticsearch: bucket aggregations [part 1]
With Elasticsearch’s bucket aggregations we can create groups of documents. In this article we will mainly focus on aggregations based on keyword type fields in indexes. We will use several examples to understand the main differences between the available aggregation functions.