Various specializations within data science will define your responsibilities. Each one has its demands, training, and qualifications to keep in mind. This article will serve as a brief guide into the big three data science career paths: data analyst, data engineer, and data scientist.
It is a challenge to make AI-driven models transparent. They are a blackbox and can cause serious issues. The aim of a glassbox is to provide greater transparency in how a model is operating and how its outputs have been reached.
Data science and machine learning in Jupyter Notebooks can lead to complicated code, making it hard to improve your projects. In this article, you will learn how to reduce complexity in your code, why it’s important to get your code out of Jupyter Notebooks as soon as possible, and how to keep your code clean.
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.
In this talk from the Machine Learning Conference, Kamil Kaczmarek and Jakub Czakon focus on practical guidelines and tips on how to set-up and maintain smooth collaboration in data science projects. Discover how to best track and collaborate in data science.
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.
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.
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.
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.