Monday, September 26, 2022


Cloud computing is a technology that uses the internet for storing and managing data on remote servers and then accessing data via the internet. To put it simply, ‘Cloud’ means internet and ‘Cloud computing is the delivery of computing services over the internet. It enables its users to rent physical data servers, storage, databases & computing power from cloud providers under a pay-as-you-go payment scheme. One such example is Google cloud.

How cloud computing helps data science?

Cloud technology works as a centralized and accessible platform that enables data scientists to flexibly manage multi-structured data in real-time. Cloud computing enables faster collaboration; hence data scientists and engineers can easily view, share and process data across a cloud-based platform. It’s becoming increasingly vital for not just the software developers but in the field of big data analytics, it’s quite handy for data scientists who are digging into large datasets.

Moving ahead, while we’ve been talking about Cloud computing becoming a widespread technology, it becomes imperative to understand the data science skills that are critical for Cloud Computing.

Key skills for a thriving career in Cloud computing:

The move to the cloud has picked up since the pandemic hit, as the companies have shifted to support remote workforces with video conferencing software such as Microsoft Teams along with collaborated products like Google Docs. Data science skills are an important part of the day-to-day work of a cloud engineer or cloud architect.

Embracing data science skills, it’s important to understand the connection between Cloud computing and Data scientists. Though it is evident from the discussion above that Data scientists and Cloud computing are inseparable. It is critical to understand the connection between the two. Imagine a small company that wants to use data analytics to improve its services and gain an edge over its competitors. But it doesn’t have the financial resources to store large amounts of data in local servers for data analysis. Here, Cloud computing acts as a savior.

Big data and analytics application workloads are on the rise and with the recent rapid growth in cloud infrastructure, these folks need to understand the new virtualized infrastructure within their clouds, as it is now underlying and controlling their workloads. Analysis and storage are the two most important challenges for organizations, large or small. Data science and cloud computing essentially go hand in hand. And to get in-depth knowledge of Data science, one must enroll themselves for a worthy Data science certification. A qualified data scientist is likely to work with structured, semi-structured, and unstructured data along with a varied set of data irrespective of the size, format, etc. They’d also analyze them to draw credible insights. Given that the storage is now much cheaper and the open-source platforms and tools are available for data science professionals, the cloud is the key.

  • Cloud computing helps data scientists to use platforms like Windows Azure, which provides access to programming languages, tools, and frameworks, both for free as well as for a fee.
  • Data scientists use MapReduce tools to store and retrieve data & also write programs.
  • Given the size of data sets and the availability of tools and platforms, understanding cloud is not just pertinent but critical for data scientists.
    Processing data and shifting it to cloud organizations avails two benefits- tackling large sets of data for decision making & reducing the overall cost of infrastructure. Let’s delve into the vital Cloud computing skills marked as essentials in today’s big data times:
  • Programming Languages
  • Knowledge of mainstream programming languages and the ability to write code to build, deploy and manage applications quickly are core capabilities for cloud architects, engineers and administrators like Java, SQL, Python, JavaScript, etc.
  • Platform expertise
  • Gaining a thorough understanding of the cloud service platform marketplace is a good starting point by familiarizing yourself with AWS, Azure & GCP.
  • Selecting the right services and managing an integrated environment dictates an efficient data scientist.
  • Maintaining databases
  • Cloud databases can reside anywhere within a cloud infrastructure and one needs to have skills that extend beyond what is required to manage databases that run in traditional on-premises data centers.
  • Network management
  • In the world of cloud computing, centralized computing resources are shared among number of users, as a result, more & more of core network management tasks and functions are being pushed into the cloud. This calls for an understanding of the virtual network mechanism.
  • Securing cloud environment
  • The cloud is vulnerable to attacks and compromises as the threat surface is massive, which calls for security. As a result, the demand for cyber security talent well outpaces its supply and the ongoing rise shows a trend to continue in future.
  • Migrating data
  • The best cloud professionals would treat each application on its own merit and be prepared to work with business stakeholders to chart the best course forward.
  • Automating key tasks
  • Cloud professionals need to be well versed in the mechanics of a business’s cloud architecture and the different components that interact with or depend on one another.
  • Designing distributed systems is another imperative a cloud computing engineer must focus on.
  • Adapting to changes
  • Cloud environments are extremely fluid, demanding an expert comfortable in functioning in a continually flux atmosphere and can stay on top of constant changes to the underlying infrastructure.
  • Estimating cost and workload
  • Ability to determine and monitor cost and workload estimation are valued skills. It enables the identification and elimination of any features that are not being used.
  • Measuring and analysing metrics
  • Expertise in metrics and analytics and understanding which metrics should be applied to specific cloud services, will land you in good stead. That’s simply because these skills enable the demonstration of ROI of a business’s cloud technology.
  • Alongside these technical qualifications, one must also be good at soft skills to succeed.

Having talked about these crucial skills that are critical in making a credible data scientist, a worthy reputed data science certification plays an important role in making the same. There’s an array of data science certifications ranging from the one provided by Google & USDSI™ that you can choose from as this decision can make or break your career in Data sciences & cloud computing.




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