Data Science

What Is Data Science?

Data science is an incorporative method of extracting actionable insights from the enormous and ever-increasing pile of data collected and created by everyday organizations. Data science includes gathering data for analysis and processing, implementing advanced data analytics, and displaying the results to indicate patterns and allow stakeholders to make informed decisions. Data preparation includes cleansing, aggregating, and manipulating it to be ready for processing. Data analysis requires the development and use of algorithms, analytics, ML and AI models. It’s partly filtered by software which goes through data to find patterns and later transforms these patterns into predictions that help business decision-making. The accuracy of these estimations must be validated through specially designed tests and experiments. And the forecasts and predictions should be shared through the proficient use of data visualization tools that can allow anyone to see the patterns and apprehend trends. As a result, data scientists need computer science and refined programming skills beyond those of a typical data analyst. A data scientist must be capable to do the following:

• Use mathematics, statistics, and the scientific method
• Use a broad spectrum of tools and techniques for analysing and preparing data
• Draw insights from data using specific analytics and artificial intelligence (AI), including machine learning, neural science and deep learning models
• Develop applications that automate data processing and calculations
• Illustrate results to convey the meaning of data interpretations to decision-makers and stakeholders at every tier of technical knowledge and understanding
• Describe processing results that can be used to solve business problems

This blend of skills is rare, and it’s plenty of proof that the demand for data scientists is currently high.  

The data science lifecycle

The data science lifecycle—also referred to as the data science pipeline—contains anywhere from five to sixteen overlapping, continuing functions. The processes are familiar to the definition of the lifecycle terms that include the following:

• Capture: This is the real-time collection of raw structured and unstructured data from all relevant sources by all-inclusive methods—from manual notations and periodic reports to web scraping for data capture from systems and devices throughout.
• Prepare and maintain: This implicates inputting the raw data into an agreed-upon format for analytics or machine learning or deep learning models. This usually includes filtering and reformatting the data, using data integration technologies to combine the data into a data warehouse, data lake, or another unified store for analysis is the final step of the process.
• Preprocess or process: Here, data scientists examine inclinations in data algorithm, patterns, ranges, and allocations of values within the data to examine and summarize the data’s suitability for use with real-time analytics, predictive machine learning algorithms, and/or deep learning software.
• Analyze: This is where most of the data discovery happens—where data scientists conduct statistical analysis, predictive analytics, regression, machine learning and deep learning algorithms, and more to extract acute details of fluctuations from the prepared data.
• Communicate: Finally, the understandings are shared as reports, charts, and other data visualizations that clearly state and explain the insights—and their impact on the business—which makes it easier for decision-makers to understand. A data science programming language such as R or Python already includes components for generating visualizations; alternatively, data scientists can use extensive visualization tools for even more customisation.

 

Data science and cloud computing

Cloud computing is allowing many data science benefits to be utilized by small and midsized organizations. Data science’s foundation is the analysis and recognition of extremely large data sets; the cloud shines in this area where access to storage infrastructures is possible and data science processes are capable of handling these large amounts of data with ease. Data science also integrates running machine learning algorithms that demand massive processing power; the cloud provides the high-performance computing that’s required for the task. An equivalent on-site hardware would be expensive for many enterprises and research teams, but the cloud provides access to these systems at an impressive value by allowing affordable per-use or subscription-based pricing. Cloud infrastructures can be accessed from anywhere in the world, making it possible for multiple groups of data scientists to share access to the data clusters they’re working on—even if they are situated in different countries. Open-source code is widely used in data science toolsets. When they’re deployed in the cloud, teams don’t need to install, configure, maintain, or update them locally. Most cloud companies also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing easy access to the means for meaningful insights that this discipline is making available.  

Data science use cases

There’s a long list of enterprises that could potentially gain from the intelligent tools data science is creating. Nearly any business approach can be transduced to a more efficient operation through data-driven optimization, and nearly every means of customer satisfaction can be enhanced with more suitable targeting and personalization. Here are a few known use cases for data science and AI:

• A global bank developed a mobile app that offers on-the-spot application of loan requests and gives real-time time scores using machine learning based on credit risk models and a hybrid cloud computing algorithm that is both powerful and secure.
• An electronics manufacturer is working on ultra-powerful 3D-printed IoT modules that could guide tomorrow’s driverless vehicles. The software is completely designed with data science and analytics solutions as a base and it works towards enhancing its real-time object detection capabilities.
• Robotic process automation (RPA) provider developed a predictive business simulation mining solution that reduces event handling times between 15% and 95% for its client companies. The solution is trained with machine learning algorithms to understand the data and workings of customer emails, helping service teams to deal it issues that are most demanding and urgent.
• A digital media management company created a viewer analytics platform that allows its clients to observe the trends of what’s engaging TV audiences in real time. The solution utilises deep analytics and machine learning to analyse real-time insights into viewer preferences.
• An urban law authority created statistical analysis tools to help officers to prevent crime and distribute its resources. The data-driven solution provides reports and analysis charts to augment situational readiness for field officers.
• An intelligence-based healthcare company developed a program enabling seniors to survive independently for longer. Packaging sensors, machine learning, analytics, and cloud-based processing, the system monitors for unusual graphs in health monitoring and alerts closed ones while conforming to the strict privacy standards that are mandatory in the healthcare industry.