Cloud computing and data analytics are two technologies that go hand in hand. Both are essential elements for critical business decisions and influence revenue generation by businesses. Cloud computing means the delivery of computing services like servers, networking, software analysis, database and intelligence over the internet (cloud). For instance, communication tools like WhatsApp, calendars and emails have a cloud-based infrastructure. Data scientists analyse various types of data (structured, unstructured, semi-structured) stored in the cloud.
India’s public cloud services (PCS) market including IaaS, PaaS and SaaS based models stood at $2.2 billion in the first half of 2021. The overall market is expected to reach $10.8 billion by 2025, growing at a CAGR of 24.1% from 2020-2025. This is because the Cloud will be the pillar for innovation and digital transformation adopted by Indian enterprises. Cloud computing helps a data scientist use platforms like Windows Azure that may offer access to frameworks, tools and programming languages. They are also comfortable in using MapReduce tools like Hadoop to store data as well as retrieval tools like Hive and Pig.
You can get a clear understanding of cloud computing and data analysis by joining GeekLurn’s Data Science Architect Program. It ensures exposure to key concepts like Analysis Modules, Testing, Statistical Computing, Real Analytics and Hadoop Development along with Artificial Intelligence and Deep Learning. The course guarantees 100% placement, 1-on-1 career mentorship, easy financing options (EMIs begin once you get a job), project certificates and a chance to participate in live webinars to understand technical terms. You can expect a cohesive learning experience with experts, mentors and workmates and resolve all project-related queries.
Here is a detailed guide to help you understand data science vs cloud computing.
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Data Science: Why is it important?
It is widely used in various industries like finance, policy work, healthcare, gaming, logistics and digital marketing. Data scientists can support powerful business decision making, with modeling, interpreting and finally deployment. In-depth knowledge of cloud computing vs data science is critical for data professionals to be able to perform a series of tasks, like model testing, training, and mining, as well as use tool kits provided by Azure or AWS.
The larger part of the data science process is performed on local computers. It is done by installing R and Python along with the IDE. The procedures involve gaining data, wrangling, munging, transforming, parsing and cleaning, testing and approving, tuning and enhancing models and deliverables. A cloud-based virtual machine-like AWS or Elastic Beanstalk is used by individuals to offload the computing work, rather than using the local development machine of data scientists. There are multiple clouds-and-service-based offerings that are accessible from eminent vendors. These can function with tools like Jupyter Notebook.
Should I learn Cloud Computing or Data Science?
Cloud computing is an important component in the field of big data analytics. All cloud providers offer a specific set of tools for data scientists.
| Tools | Skill Sets |
| GCP | Google BigQuery, Vision / Speech / Translate / Natural Language API for data extraction and transformation, Cloud Dataflow, Cloud Dataprep, Apache Beam and Data Studio and Tensorflow |
| AWS | Data Pipeline, S3, EC2, Redshift and Database Migration Service. A few popular customers are S&P Global Ratings and Standard Chartered Bank, The Guardian and Nielsen. |
| Microsoft Azure | AzureSQL, AzureTable and AzureBlob, HDinsight and AzureML. All these tools can be integrated with Power BI and Microsoft Excel to make results easily visible and accessible for users with unique tech skills |
From a career point of view, are you trying to choose cloud computing or data science? Well, both are necessary and come with their own sets of benefits. In fact, giants like Google, Amazon and Microsoft are trying to combine cloud computing and data science. Consider mastering both by taking up a course that makes you industry-ready by equipping you with everything that is needed for a successful career in data science.
Skills for Cloud Computing
A few core skills include data security, data analysis, project management, business concepts, technical knowledge like HTML, Linux, Web services and API, networking skills and computing fundamentals. Consider platform exercise, selecting the right services, managing a network, maintaining databases, adapting to new technologies and roles, securing the cloud environment, migrating data, estimating cost and workload and designing distributed systems to become proficient in cloud computing. It is a vast field, and a deep understanding of these skills may help you become successful as a cloud engineer.
The bottom line:
The adoption of the cloud picked up in the months during the COVID-19 lockdowns. The use of cloud services continues to grow, and companies will require software architects with cloud and data science skills. So, data scientists would do well to hone their cloud computing skills to advance their careers. Consider learning cloud computing vs data science to understand how both are unique yet interlinked.
Neel Neeraj
Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
