#Data Science

Last week's highlights

Weekly Review: Plans for Java 16, Spring Boot 2.3, data science coding tips & more

Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, Spring Boot 2.3 arrived and we took a look at Java’s migration to Git and GitHub, which keeps getting closer. Read on for more highlights including better coding habits for data scientists as well as interviews on Nordic language BERT models and the JavaScript framework Crank.js.

Interview with the creators of Julia

“Julia is comparable to Python for simple machine learning tasks and better for complex ones”

The initial release of the Julia programming language was eight years ago, in 2012. We spoke to the four creators of the language, Dr. Viral B. Shah, Dr. Jeff Bezanson, Stefan Karpinski and Prof. Alan Edelman, to find out whether Julia has been able to live up to their high expectations. They also went into detail about the various use cases Julia is applied to today, how the language compares to Python, and where it is headed in the future.

Interview with pandas developer Tom Augspurger

“The 1.0 release does not mean a conclusion, or even slowing down, of pandas’ development.”

pandas has reached the milestone version 1.0.0. The Python library for data analysis and manipulation has already been around for 12 years and is being used in production, so what led to this decision now? We spoke to Tom Augspurger from the pandas developer team. He shared some insights on the new release, his personal highlights and where pandas is headed in the future.

Watch Steph Locke's JAX London session

Data science fundamentals

How do data scientists make their predictions? It isn’t magic, it’s technology. In this session from JAX London, Steph Locke gives you a look behind the curtain at the process and fundamentals that data scientists employ. You will learn about the basics of data science and how to add it to your own technical field.

Couldn't make it to ML Conference 2019 in Berlin? No problem!

Data to the Rescue! Predicting and Preventing Accidents at Sea – Livestream of the ML Con Keynote

In the second keynote at ML Conference 2019 in Berlin, Dr. Yonit Hoffman dives into the topic of data science: How can data science and machine learning help prevent accidents at sea that cost lives, money and environmental destruction? Here’s the livestream of the keynote if you couldn’t make it to Berlin this year.

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.

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