Preparations are well underway for the 2018 edition of Data Natives– the data driven conference of the future, hosted in Dataconomy’s hometown of Berlin. Data Natives brings together a global community of data-driven pioneers to explore the technologies that are shaping our world- from big data to blockchain, from AI

Linear regression is a basic tool. It works on the assumption that there exists a linear relationship between the dependent and independent variable, also known as the explanatory variables and output. However, not all problems have such a linear relationship. In fact, many of the problems we see today are

R is ubiquitous in the machine learning community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory

The R language is often perceived as a language for statisticians and data scientists. Quite a long time ago, this was mostly true. However, over the years the flexibility R provides via packages has made R into a more general purpose language. R was open sourced in 1995, and since

Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various

Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit’s work involves applying machine learning techniques to

Choosing the right language for data analysis can be almost as complicated as actually learning the language. For many reasons, R and Python are two of the most popular: R is often praised for its great features for data visualization, as it was developed with statisticians in mind; plenty of programmers love multi-purpose Python for its so-simple-a-child-could-do-it syntax. Why

This article originally appeared at Sharp Sight Labs. Follow Joshua Ebner, the founder of Sharp Sight Labs, on Twitter. Read more here. Over and over, when talking with people who are starting to learn data science, there’s a frustration that comes up: “I don’t know which programming language to start

One of most popular posts this year came from Ferris Jumah, a data scientist at LinkedIn, who mapped the most popular skills of data scientists by scraping LinkedIn profile data. One of the common comments amongst data scientists who came across this post- as with most of our posts focused

2014 has been a huge year in big data- and big data publishing. Viktor Mayer-Schoenberger and Kenneth Cukier re-published and added an extra chapter to their bestselling “Big Data”; Nate Silver graced the publishing world with his presence once more with the Best American Infographics of 2014. We’ve compiled a