The process of obtaining information and deriving insights out of raw datasets
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Data science is the approach of obtaining information and deriving insights out of raw datasets. It is a combination of different tools, machine learning concepts, and algorithms to find hidden patterns in the raw data.
Data science is typically used for making decisions and forecasts using machine learning, descriptive analytics, and predictive analytics. Data is taken from various industries, networks, and websites, including mobile phones, e-commerce pages, social media, internet searches, and health surveys. However, the raw data lacks structure and needs interpretation to make insights on which decisions can be based.
Data science refers to obtaining information and deriving insights out of raw datasets.
Data science is typically used for generating insights and predictions from raw information to make decisions and forecasts and uses machine learning, prescriptive analytics, and predictive analytics.
Corporations can use data science to innovate in their markets, develop innovative goods, and predict future patterns of behavior and problem solve.
Data Science Lifecycle
Different stages in the lifecycle of data require different programs, skill sets, and techniques. Data science typically includes the following main lifecycle stages:
Capturing data: Involves the acquisition of data, entry of data, signal reception, and extraction of data.
Maintaining data: Involves data cleansing, data processing, data warehousing, and data architecture.
Processing data: Involves data mining, data modeling, data classification, and data summarization. In this stage, techniques and methods to draw relationships between variables are determined.
Analyzing data: Involves predictive analysis, text mining, regression, and qualitative analysis.
Uses of Data Science
Below are a few examples illustrating how corporations use data science to innovate in their markets, develop innovative goods, and predict future patterns of behavior and problem solve.
Data science is a major contributor to a variety of breakthroughs in the healthcare field. With a massive network of data currently accessible across everything from EMRs (electronic medical records) to health databases to individual fitness trackers, health professionals are discovering new ways of understanding sickness. Data science also helps in practicing preventive medicine, diagnosing disorders more efficiently, and exploring new care options.
The finance industry saved millions of dollars and an unmeasurable length of effort with data science and machine learning. Financial institutions use Natural Language Processing (NLP) to process and retrieve crucial data from over 12,000 commercial credit contracts a year. What would’ve taken over 360,000 hours of physical labor to complete is now completed in a couple of hours using data science tools.
Data science is useful in every field; however, it exerts the most significance in the practice of cybersecurity. International cybersecurity company Kaspersky uses computer science and machine learning to regularly identify more than 360,000 new malware samples.
Knowing and learning new cybercrime techniques through data science quickly is important for our protection and security in the future.
Automotive companies like Tesla, Volkswagen, and Ford are also introducing predictive analysis in a new wave of autonomous vehicles. The vehicles use thousands of sensors and tiny cameras to transmit information in real-time.
Using machine learning, data science, and statistical intelligence, self-driving vehicles can adapt to speed limits, prevent risky lane shifts, and even carry passengers in the fastest lane.
Data Science and Business Intelligence
Business Intelligence (BI) essentially analyzes historical data to find perspective and insight for explaining various business patterns. Here, BI allows us to take data from internal and external sources, plan and run queries on it, and build dashboards to interpret past data such as quarterly sales analysis or market share. BI is used to understand the historical performance of a business.
In comparison, data science is a more forward-looking approach that focuses on evaluating historical or present data to make future predictions on outcomes and behaviors. It asks broad questions on “how” and “what” incidents arise.
While BI is designed to manage stable and highly organized data, data science can manage high-volume, high-speed, and multi-structured, dynamic data from multiple data sources.
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