Data Transformation - Understanding why the “Unsexy” component of a data scientist’s job could be one of the most important and cool parts
Introduction When we talk about data science, we are biased to start with sexy aspects like Artificial Intelligence (AI), Machine Learning (ML), predicting the future, and so on. Like our brain, which tries to simulate the future from past experiences, ML algorithms work and learn from the past, and it’s our job to provide them with the best possible learning experiences. Did you ever wonder how you could close your eyes right now and experience the rush of that speech you are going to deliver to a broad audience tomorrow? In the first half of this article, we will look at simple use cases that will help us understand why data transformation is essential and how it can help our classifiers perform better. In the latter half, we will visually understand why performing a log-transformation on the exponential feature will allow us to use linear regression to model it.
Your Data Architecture: Simple Best Practices for Your Data Strategy. Don't miss this helpful article.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories.
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
So here is my first blog regarding the data visualization with matplotlib in python. In this article we will cover the basic of the visualization with matplotlib.
In this post, we'll learn Getting Started With Data Lakes.<br><br> This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that's designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You'll also explore key benefits and common use cases.