Companies use their data to accelerate business growth and overtake their competitors. To achieve this, they invest a lot in their ETL (extract-transform-load) operations, which take raw data and transform it into actionable information. It’s no wonder, then, that ETL testing is a crucial part of a well-functioning ETL process, since the ETL process generates mission-critical data.

Random forest is one of the most widely used machine learning algorithms in real production settings.

The decision tree algorithm - used within an ensemble method like the random forest - is one of the most widely used machine learning algorithms in real production settings.

Cluster analysis is a process used in artificial intelligence and data mining to discover the hidden structure in your data. There is no single cluster analysis algorithm. Instead, data practitioners choose the algorithm which best fits their needs for structure discovery. Here, we present a comprehensive overview of cluster analysis, which can be used as a guide for both beginners and advanced data scientists.

Linear regression, alongside logistic regression, is one of the most widely used machine learning algorithms in real production settings. Here, we present a comprehensive analysis of linear regression, which can be used as a guide for both beginners and advanced data scientists alike.

Imagine going to work only to find that your inbox is flooded with customers telling you how happy they are with your software. People are in such a hurry to download your app, you need to scale your servers to meet the demand before the infrastructure crashes. Your phone rings: it’s a tech journalist trying to book an interview with you about your company's growth. This is the dream for every business owner and entrepreneur. But the reality is often in stark contrast to the scenario above.

Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.

Over the last 100 years alone, artificial intelligence has achieved what was once believed to be science fiction: cars that drive themselves, machine learning models that diagnose heart disease better than doctors can, and predictive customer analytics that lead to companies knowing their customers better than their parents do. This machine learning revolution was sparked by a simple question: can a computer learn without explicitly being told how?

Back in the old days, marketing was ridden with a lot of guesswork. Sometimes, unexpected campaigns brought new leads and converted prospects into customers. Other times, the best-designed campaigns flopped, the market remained unmoved and all you could hear after the launch of a campaign was silence. Data-driven marketing rose from the pains of this insecurity and took on the overwhelming growth of data for its support.

Being more productive than your super competitive peer group is hard. Being 10 times more productive might sound like an impossibility, an exaggeration.... or even a myth (unicorn, you say?). A 10x data scientist is literally 10 times more productive than the average data scientist. The skillsets of these data scientists create better career opportunities, higher peer recognition, and more interesting projects to work on.