Setiawan Hadi

Source : https://www.mygreatlearning.com

What is Data Science?

You must have wondered, ‘What is Data Science?’, Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing data set. The information extracted through data science applications are used to guide business processes and reach organisational goals.

Scope of Data Science

One of the domains that data science influences directly is business intelligence. Having said that, there are functions that are specific to each of these roles. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences. A Business Intelligence expert picks up where a data scientist leaves – using data science reports to understand the data trends in any particular business field and presenting business forecasts and course of action based on these inferences. Interestingly, there’s also a related field which uses both data science, data analytics and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies take data driven decisions.

Data scientists analyse historical data according to various requirements, by applying different formats, namely:

• Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.
• Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.

Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data.

Data scientists are skilled professionals whose expertise allows them to quickly switch roles at any point in the life cycle of data science projects. They can work with Artificial Intelligence and machine learning with equal ease. In fact, data scientists need machine learning skills for specific requirements like:

• Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company.
• Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

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