Data Scientist and Cloud Computing

Introduction

Data Scientist is a role in which data professionals analyze and process data to produce meaningful insights. This can range from statistical analysis of financial statements to developing computer algorithms that predict trends, detect defects, or recommend the best route to a particular location (Yang et al., 2017 p.14). Data Scientists look for patterns in large data sets and find ways to use them as tools for decision-making. To succeed in this field, it takes strong analytical skills and the ability to manage and manipulate large amounts of information quickly. One of the necessary skills is knowledge of cloud computing and how to leverage it to keep things moving. Cloud services have gained popularity in the recent past. Local servers make data storage, and management costs must be considered as more computing power arises in crucial business areas. It can be challenging to place the server where it is needed, especially when it must be backed up properly and its data needs to be available 24 hours a day without interruption. Cloud services reduce these costs and enable operations to run efficiently (Sunyaev, 2020, p.196). The research examines how data scientists leverage cloud computing in their workflow and how these services are accessed by data scientists as a method to optimize their efforts.

Processes Used by a Data Scientist

Data scientists use a variety of tools at their disposal, from big data analytics platforms to databases. To understand these processes, it is essential to examine the data scientist job description and some of the skills they are required to possess. The job description of a data scientist varies depending on the company and its industry. Most common, though, is that they must have strong analytical skills and be able to quickly process large amounts of information. Other things to look for include customer focus which is the ability to prioritize different customer segments and offer new services that will benefit all of them rather than just one part of the market. Also, they need the ability to prioritize different customer segments and offer new services that will benefit all of them rather than just one part of the market. Another skill is data management. Good data engineering skills and knowledge about processing large amounts of information. Good data engineering skills and knowledge about processing large amounts of information. Cloud computing – Understanding how to utilize cloud services is necessary for this field (Cao, 2017 p. 10).

Data analytics platforms are essential for data scientists. The most common software data scientists use a platform that allows them to analyze large amounts of data with different methods, including visually. Google Analytics, Tableau, and IBM Big Sheets are some examples. A database is a structure that stores information in a way designed for processing with computers. Databases include many different elements like tables, columns, queries, and views. Data visualization is also a necessary process. Visualization of the data helps identify patterns and recommend which products would best suit each customer segment. It also helps in the analysis process. Data mining – using various methods to extract information from large data sets. Knowledge of data mining algorithms, statistical analysis, and clustering (Cao, 2017 p. 12).

Data scientists must have good knowledge of programming languages for different purposes, such as writing scripts for data processing or developing algorithms to find patterns in the information. Basic knowledge of Java and Python programming languages is essential, especially when working with big data. Scripting language such as PERL is also a necessary skill for this role. Experienced data scientists may also be able to program in languages like C++ and R. Other skills applicable to a successful data scientist include Knowledge of SQL – Structured query language helps them find information quickly using queries and perform database management duties.

Data Scientists and Cloud Computing

Data scientists use several tools while they perform their job process. One tool that data scientists often use is a cloud service. It is a computing service hosted in the Internet “cloud” that provides easy, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, and storage). Cloud services are usually pay-as-you-go and use little configuration or maintenance from the user’s side. One popular cloud service used by many different types of professionals is Amazon Web Services (AWS). Amazon Web Services provides a pool of available resources to other clients. Users can access these resources from anywhere on the planet. There is no physical data center hosting the cloud services; it is accessible from any device. AWS provides a large number of services and tools, including Data storage, Infrastructure as a Service – Infrastructure like servers, network equipment, Backup Management, and Managed file storage – Files are created easily in real-time through tools like Amazon S3 and other features like auto-analysis and compression to make room for more data

Applications running on Amazon Web Services can be used by data scientists as well as many others. A few of the most common are Amazon Elastic MapReduce (EMR) – A web service that provides access to a hosted Hadoop cluster. It is used for processing large amounts of data for different purposes. For example, suppose data scientists want to process information about customer reviews on Amazon products. In that case, they can use this tool and gain valuable insights into how customers feel about specific products and services and make recommendations.

Cloud database services like Amazon RDS or MySQL enable data scientists to build and run database instances on demand without needing to maintain the database server themselves (Sunyaev, 2020, p.196). Another common scenario is using database tools like Amazon RedShift to store big data sets and retrieve them when needed. Some of the data storage options include:

• Relational Database Service – A managed service that makes it easy to set up, operate, and scale relational databases in the cloud.
• Simple Storage Service – An object storage web service that offers highly scalable, low-latency storage at a competitive price.

Data Scientists are also involved in big data analytics (Ahmed et al., 2017 p.460). Big data refers to the availability of large, uncompressed amounts of raw data that are generally unavailable for analysis. Data scientists must have a robust cloud computing and mathematics background to take advantage of their methods. One of the most critical skills they learn is statistics, which is used in their work. Statistical models help them identify patterns and predict future events by finding statistical trends or relationships between variables. Some standard statistical models data scientists use include linear regression – A linear model used to find relationships between two random variables like age and income. Linear regression helps predict a particular variable’s value by using inputs and outputs. Logistic regression is a predictive model that uses ordinal data, allowing for multiple values of a second variable (like age) to be utilized. It then uses the model to predict one of many values for the third variable, like, income. Machine learning – A type of statistical modeling that uses algorithms to make predictions about future events based on past data. Artificial neural networks – These networks can operate in two ways: feed-forward and recurrent. Feed-forward networks are fully connected, meaning they have the input as the first layer and the output as the last layer. Recurrent networks have feedback loops, meaning they have a layer that is connected to themselves. These networks are used to carry out pattern recognition. Clustering – This method groups individual data points into groups according to similarities and is used to visualize and understand large datasets differently (Ahmed et al., 2017 p.462). They can be unsupervised or supervised learning methods, but the goal of clustering data is similar across all data clusters.

Cloud computing is necessary for these processes because it uses the processing power of many servers to provide on-demand usage and flexibility. By using this environment, companies can operate more effectively and efficiently than if they were to deploy their own data storage or other IT infrastructure. Cloud computing helps businesses reduce IT costs because they do not need to purchase hardware, software licenses, or maintenance. Cloud computing also provides a fast and easy way for businesses to expand their enterprise by allowing multiple employees access simultaneously from any location (Srivastava and Khan, 2018 p. 18).

Additionally, data scientists can also use the cloud for data sharing. This means that all the data will be stored in a single, accessible place so that everyone can access and retrieve it anytime from any place around the world. This is useful in cases where multiple people have to work on one project and share their results, which helps them work more efficiently and quickly. Cloud computing has become a necessary skill for data scientists, providing them with a wide range of tools to create innovative products while keeping costs low.

Furthermore, cloud computing helps businesses in other ways. It provides an environment that can scale up or down as necessary. This means the business does not have to upgrade or replace hardware as their needs change continually. It also lets customers quickly increase storage space or processing speed without any additional effort on their parts. Data scientists can get started more quickly because they do not need to set up infrastructure, like hardware and software. This also allows them to experiment with new approaches more quickly, which is essential for a field where innovation is at the core of every decision made. The cost savings and flexibility of cloud computing allow businesspeople to spend their money on other aspects of their companies instead of paying for expensive IT needs like servers, software licenses, and maintenance contracts (Srivastava and Khan, 2018 p. 19).

Conclusion

The demand for data scientists is expanding daily, and the role of these IT professionals and their services is becoming more critical. Cloud computing is a crucial factor in their work because it lets them do their jobs more efficiently and conveniently. Any business that wants to be successful in today’s world needs to utilize the power of data scientists because they have the skills to take the company in the right direction. Using cloud computing resources ensures businesses can focus on what matters most to them and let someone else maintain their IT infrastructure so that they never have to worry about running out of space or processing power again. Data-driven companies also need to know their target customers, so they can develop targeted solutions to meet the needs of each sector. They need to adopt the “user-centric” approach for any business. Data scientists are an essential part of this process because they help gather requirements for products and services and then use machine learning techniques to predict what target customers will want based on their past behavior. As more businesses adopt the cloud and start using data science, more applications will be created, which will further help both fields to grow and expand. Since the cost of computing has dropped significantly due to cloud computing, everyone can now access high-quality enterprise-level software like machine learning at a fraction of its previous prices.

References

Ahmed, E., Yaqoob, I., Hashem, I.A.T., Khan, I., Ahmed, A.I.A., Imran, M. and Vasilakos, A.V., 2017. The role of big data analytics in the Internet of Things. Computer Networks, 129, pp.459-471.

Cao, L., 2017. Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), pp.1-42.

Srivastava, P. and Khan, R., 2018. A review paper on cloud computing. International Journal of Advanced Research in Computer Science and Software Engineering, 8(6), pp.17-20.

Sunyaev, A., 2020. Cloud computing. In Internet computing (pp. 195-236). Springer, Cham.

Yang, C., Huang, Q., Li, Z., Liu, K. and Hu, F., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), pp.13-53.