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.
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.
When hiring a data scientist there are a few things that you need to take into account. Yuvrajsinh Vaghela presents four important questions that will help you hire the perfect data scientist for your team.
The usage and importance of Python have been growing year after year, especially with the data analytics and data science community. In this article, Disha Gupta offers a quick demo of how to implement Python libraries.
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.
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.
How do Data Science and DevOps fit together? In this article, Richard Gall explains why integrating Data Science with your DevOps can lead to a better and smarter business.
