Blog
Firebase: how to integrate a real-time database in Python
Firebase is a Google Cloud product that can. be used to build web applications quickly and easily. Among its features is the ability to create a NoSQL realtime database. Let’s find out how to interact with the database in a python program.
MongoDB 6.0: new features to improve applications
The new version of MongoDB provides new features to both improve the efficiency of some operations and increase developer productivity. Switching, therefore, to MongoDB 6 is an excellent choice!!!
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.
