The Impact of Cloud Computing on Data Science and Analytics Careers
The advent of cloud computing has transformed the landscape of data science and analytics. Gone are the days of traditional data storage and management methods, as organizations have now shifted towards cloud computing. This shift has had a profound impact on the work of data scientists and analytics professionals, and it's crucial for professionals in the field to stay ahead of the curve.In this blog, we delve into the impact of cloud computing on data science and analytics careers and offer tips for professionals looking to stay ahead of the latest advancements and developments in the industry so that they can ensure a strong career trajectory.
Familiarity with Cloud Technology
To keep pace with the dynamic world of data science and analytics, it's vital to have a comprehensive understanding of cloud computing. Even if you're not currently utilizing cloud technology in your work, taking online courses and learning how to work on the cloud on your own will prepare you to talk fluently about the cloud in a job interview and also to be prepared to adapt to a cloud environment if you take a new role where cloud is the standard. Additionally, it's important to broaden your knowledge of cloud computing beyond just one provider. By familiarizing yourself with multiple cloud providers, such as AWS, GCP, and Azure, you'll be equipped to smoothly transition to a new cloud platform if need be. If you want to land a job that utilizes a different cloud provider, it is important that you have confidence in your ability to quickly switch over and that you can make potential employers confident of the same.
Change How You Code
The shift to cloud computing has also brought changes to the way data science professionals need to write code. Traditionally, data scientists have written code that was "efficient enough" for on-premises equipment. This means that even if processes were slow and inefficient, as long as they didn't interfere with other processes and completed in a reasonable amount of time, they were considered successful. However, this approach will quickly become expensive on the cloud, where computing resources are metered and charged by resource usage. To address this, data scientists must now think about coding efficiency and work closely with operations and deployment teams who specialize in tuning for scale. This collaboration will help ensure that data science processes run efficiently and cost-effectively in the cloud.
Take Data Governance & Security Seriously
Thirdly, it's crucial for data science professionals to take governance and security seriously when working in the cloud. In the past, data scientists would work with data in their own private sandboxes, which were protected by a corporate firewall from outsiders and also limited visibility to only those approved internally. However, the ease of data sharing and exchange in the cloud means that proper protocols must be followed to prevent sensitive information from being accidentally exposed. A permissions mistake behind a firewall exposes data to other employees who shouldn’t see it, but a permissions mistake on a cloud environment can expose data much more broadly to outsiders as well. This means that it's more important now than ever before to be diligent in ensuring that data is handled appropriately and securely on cloud platforms, as the consequences of a security breach can be far more severe than on an internal network.
Don’t Reinvent The Wheel
One of the biggest advantages of cloud computing for data science and analytics is the constant availability of new tools and packages. The cloud environment is always evolving, with new functionality being added all the time. This means that if you're facing a specific problem, there's a good chance that someone else has already developed and shared a solution to it (or to something very similar to it). To stay on top of these developments, it's important to monitor the latest offerings and take advantage of new functionality that can help make your work more efficient. Rather than spending time custom coding a solution to a problem, it's often possible to simply plug in or moderately customize a pre-existing tool or package that will save you time and effort. By keeping up to date with the latest advancements in cloud computing, you'll be able to be more efficient and productive, which will get you noticed by the right people in your organization.
Future of Cloud Computing in Data Science & Analytics
As a data science professional, it is imperative to stay informed on cloud technology and understand how it impacts the way you should do your work. This includes adapting your coding practices to be more efficient and cost-effective, prioritizing data governance and security, and staying current with the latest advancements in the field. By familiarizing yourself with multiple cloud providers and tapping into the latest cloud computing developments, you can position yourself to employers as someone who is at the forefront of this dynamic and rapidly evolving technology.This post was developed with input from Bill Franks, internationally recognized thought leader, speaker, and author focused on data science & analytics.