Josep Ferrer

4 days ago | f.mtr.cool | Josep Ferrer

Image by Editor | ChatGPTEvery day, the average person generates around 3GB of data according to the Data Never Sleeps report by Domo. That’s an overwhelming amount, and it raises a pressing challenge: How do we manage it all? You’ve probably been asked countless times how to analyze or process data efficiently. But there’s another equally critical question we often overlook: How do we store it properly?

1 week ago | f.mtr.cool | Josep Ferrer

Violin plots are a powerful and elegant way to visualize the distribution of a continuous variable across different categories. By combining the features of box plots and kernel density estimates, violin plots reveal not just summary statistics, but also the full distribution shape and variability of the data in a single, compact graphic. Today, I want to explore with you how to create violin plots using Seaborn, a Python visualization library built on top of Matplotlib.

2 weeks ago | f.mtr.cool | Josep Ferrer

Image by Editor | MidjourneyStatistics is the science of collecting, analyzing, interpreting, and presenting data. Some of these processes are key actions in the field of data science, where statistics form the backbone of every major step. From exploring and understanding datasets to building reliable machine learning models and making data-driven decisions, the field of statistics is integral.

3 weeks ago | f.mtr.cool | Josep Ferrer

Image by Editor | MidjourneyWhen working with large datasets in data science and machine learning projects, memory management becomes a crucial concern. Efficient memory usage can significantly impact both the speed and scalability of your applications. In this article, we’ll explore how to handle large arrays efficiently using NumPy, a foundational library for numerical computing in Python.

1 month ago | f.mtr.cool | Josep Ferrer

Image by AuthorWhat Is a Time Series? A time series refers to a series of observations collected at a specific time interval and goes through to periods that are equally spaced. In contrast to typical datasets, which maintain an arbitrary order, time series data has an ordered temporal dimension, which makes it a special case when analyzing data. Common examples may include stocks with daily prices or e-commerce sales.