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MAE and RMSE are some of the most common error metrics for regression problems. Despite being used for the same task (understanding the errors in your predictions) there are important differences between the two. Choosing the right metrics for your model can make a huge difference in your ability to solve a problem. The algorithms you’ll use t...

Supervised learning is perhaps the most common form of machine learning task in use nowadays. This form of learning makes use of a labeled training dataset to create a model that predicts a target of interest. If this line makes absolutely no sense to you, don’t worry. After reading this article you will have a clear understanding of supervised...

In the previous articles, we learned a definition of data science and why so many companies are investing in this field. We also learned about some of the most common scenarios where data science is the right tool for the job. In this article (the last in the series) we will learn a basic framework for organizing the tasks in a data science pro...

In the previous article, we learned what types of problems are a good fit for a data-science solution. Now we will tackle the why: we will discuss the main reasons for using data science in the industry, and how companies benefit from using the power of data to improve their performance. Data is everywhere The digitization of information enab...

In the previous article of the series, we answered our first question: what is data science and which contributions led to its creation? In this article we will explore another important question: When should I use data science? DS has lots of incredibly useful applications in fields as diverse as finance, forest planning, and genetics resear...

I wanted to write a series of articles that explore data-science from a high-level perspective. These articles aim to answer the following questions: WHAT data science?: What is data science and how it came to be? When exactly did it become a field of knowledge and what developments lead to its formation? WHEN data science?: When should yo...

You can read the previous article in the series here: replication mechanisms It’s time we talk about an important property of real-world replication: replication lag. In the first article of the series, we mentioned that there are two forms of replication: synchronous and asynchronous. Synchronous replication waits until every follower has app...

You can read the previous article in the series here: replication mechanisms We’ve been talking about replication log and change stream in the last two articles in this series. This is the mechanism the leader uses to communicate changes to the followers. As you can imagine, there are different implementations with their advantages and disadvan...