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#Data Science

The problem with traditional anonymisation

Synthetic data: A new frontier for data science

Now with the GDPR in effect, businesses have to be careful about protecting data. Traditional anonymisation often isn’t truly anonymous, and ultimately individuals can be identifiable. One way of adding an extra level of sophisticated anonymisation to data is introducing synthetic data. In this article, find out what synthetic data is and how it can be used.

Machine learning is now seen as a silver bullet for solving all problems, but sometimes it is not the answer.

The Limitations of Machine Learning

Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.

Big data is everywhere, but how can we use it?

Big data in a nutshell

What are the challenges of big data? How can organizations use its benefits to generate ROI? Vaishnavi Agrawal gives an overview of everything big data – from customer relationship management to fraud detection and cost reduction.

Interview with David Wyatt, Vice President EMEA at Databricks

For AI success, developers should collaborate more efficiently with data scientists & engineers

The future is bright for AI. Whether data science can change the world or not, that remains to be seen. One thing is sure though: developers should collaborate more efficiently with data scientists & engineers. We talked to David Wyatt, Vice President EMEA at Databricks about the challenges when approaching AI initiatives, the role of Spark in this field and the next step for data engineering and data science teams.