Big Data, Cloud & DevOps

Regression is flexible and certain regression models can handle correlated data. However, it is always important to check the assumptions of a given technique and to make sure that your analytic strategy is appropriate for your data.

This post is for those readers with little or no biology, but having data science skills and who are interested in working on biological domain problems. It is for you to understand how you can tackle this problem without reading a textbook

For all of human history, we’ve built machines and tools to help us do our work more quickly and efficiently. Not only have these tools made our manual labor faster and more efficient, but they’ve also increased safety for workers. Until recently, however, machines weren’t used to help with human-centric professions like social work. With

As a Data Scientist or Machine Learning Engineer, it is important to be able to deploy our data science project. This article l shows you how you can quickly build a simple data-driven web app using the streamlit Python library in just a few lines of code.

If data is the lifeblood of every business, it’s all the more critical for startups looking to weather a downturn. Here’s how startups can use data and analytics to power through the current crisis.

As the top brands have already embraced the power of Big Data, now it’s your turn to move ahead with implementation of big data analytics in your organisation.

It is incredible how fast data processing tools and technologies are evolving. And with it, the nature of the data engineering discipline is changing as well.

As the quantum technologies start to become a reality, this cybersecurity quandary has recently taken the form of a true cat vs. mouse game. Since it’s still to early to predict how this hack & slash story will unfold, let’s trace it back to its origins to know what happened so far and what is happening now.

This article shows how to build a simple regression model in Python. It is a detailed and visual step-by-step walkthrough.

Data was fully governed by IT and data preparation would be limited to extraction, cleansing, and transformation functions to turn raw data into a form that can be readily used for business purposes.

Given the trend to move beyond centralised datacentres to distributed environments, how can security professionals ensure such setups are just as secure as the traditional centralised model?