Jupyter Notebook: user’s guide
The development of data analytics pipelines by Data Scientists requires several skills. Having an easy, intuitive, and interactive development environment is critical. Jupyter Notebook is an open source web application that allows you to create and share interactive textual documents, containing objects such as equations, graphs and executable source code in different languages. Let’s discover its main features.
Chatterbot: create a chatbot in python
Chatbots are a technology that allows you to automate interaction with users. Leveraging the latest artificial intelligence technologies, conversations turn out to be more and more real. Examples of chatbots evolution are virtual assistants like Alexa, Cortana and Siri. Let’s find out how you can develop a simple chatbot in Python using the Chatterbot library.
AutoML Vision: image classification
Developing classification models for unstructured data, such as images or text, is not an easy task. In many cases, very specific development skills are required. Let’s find out how it is possible, using AutoML Vision from Google Cloud, to create an image classification model without writing a line of code but only selecting images for our model.
Google Cloud: introduction to the platform
Big Data is one of the most profound and pervasive evolutions of the digital world. A trend that is destined to remain and to profoundly affect our lives and the way we do business. Managing them requires very powerful computing infrastructures. The big giants of the Web, including Google, Amazon and Microsoft, provide their data centers and platforms to address the challenges offered by Big Data. Let’s find out about the computing power provided by Google Cloud through some case studies.
MongoDB 5: the new features
MongoDB is the most widely used NoSQL database in the world. Its continuous growth is due to the continuous development of new features. Version 5, released at the end of July 2021, introduced some very interesting new features. In this article we will analyze the most relevant and most useful in their daily use.
Pandas and Bokeh: create interactive graphics
Analyzing data also requires graphing the data or the results from the analysis performed. Many libraries in Python provide useful tools for visualization, but the plots produced are static. The Pandas Bokeh library is a great alternative for creating interactive plots and including them in web projects. Let’s find out how to use it and the results we can achieve through some examples.
Coding for kids: first steps in programming
Coding for kids and teens? It seems almost impossible and yet there are many tools that teach little ones to have a first approach with the logic of programming.
Programming has become an essential skill for tomorrow’s adults.
PandasGUI: Graphical user interface for analyzing data with Pandas
Pandas is the most used library by data scientists to analyze data. But if you are not an expert programmer or simply want to explore your data in a simple and intuitive way you can use PandasGUI. This is a library that allows you to view and interact with Pandas dataframes with a simple mouse click.
Celery and Django: Creating Asynchronous Tasks
Nowadays, data processing and analysis is increasingly required within web applications. Unfortunately, the required execution time can sometimes be too large to handle requests asynchronously. In this tutorial we discover how to use Celery in a Django project to create asynchronous tasks for our needs.
Neo4j: guide to use a graph database
Neo4j is the leader in graph databases. Through Cypher queries, it is possible to optimally manage a graph representation of our data and discover interesting correlations between them. Let’s find out how to use it with some simple query examples and some advanced use case suggestions.
