The simplest concept of data science is the retrieval of viable insights from raw data. Data analytics offers useful knowledge on the basis of vast volumes of structured data called big data. Data science, or data-driven technology, blends various areas of work in mathematics and software to analyze data for decision-making purposes.
The word data science has persisted for the best part of the past 30 years and was first used as a replacement for computer science in 1960. About 15 years later, the term was used to describe the survey of data processing techniques used in various applications. Data analytics was founded as a separate discipline in 2001. The Harvard Business Review published an article in 2012 describing the role of data scientist as the best job of the 21st century.”
Data is taken from various industries, networks and websites, including mobile phones, social media, e-commerce pages, health surveys and Internet searches. The increase in the volume of data available opened the door to a new area of research focused on big data, large data sets that lead to the development of improved operating resources in all industries. Continuously growing access to data is possible thanks to developments in technologies and collection techniques. Individuals purchasing habits and behavior may be tracked and forecasts taken on the basis of the information obtained.
However ever-increasing data is unstructured and needs interpretation in order to make appropriate decisions. This method is complicated and time-consuming for businesses – hence the rise of data analytics. Data science involves tools from various backgrounds to collect data sets, process and gather information from the data set, derive relevant data from the data set and translate it for decision-making purposes. The fields that make up the world of data science include mining, analytics, computer learning, interpretation and programming.
Data mining applies algorithms to the complicated data collection that expose patterns that are then used to retrieve valuable and meaningful data from the set. Statistical methods or predictive analytics use this extracted data to gauge incidents that are expected to occur in the future on the basis of what data has proven to have occurred in the past.
Machine learning is an artificial intelligence technique that manages a mass volume of data that humans will not be able to handle throughout their lifetime. Machine learning strengthens the decision model provided under predictive analytics by comparing the probability of an occurrence occurring to what really happens at a forecast moment.
Data analytics requires a plethora of subjects and fields of specialization to create a holistic, comprehensive and refined look at raw data. Data scientists must be trained in everything from data processing, arithmetic, analytics, advanced computation and simulation, so that they can accurately sift through the muddled mass of knowledge and convey only the most vital bits that will help fuel creativity and performance.
Data scientists also rely heavily on artificial intelligence, especially its sub-fields of machine learning and deep learning, to construct models and make predictions using algorithms and other techniques. Data science typically has a five-stage life-cycle consisting of capture, maintain, process, communication, analysis.
- Capture: data collection, data processing, receipt of signals, data extraction.
- Maintain: data storage, data washing, data staging, data analysis, data architecture.
- Process: Data mining, clustering/classification, data modeling, data description.
- Communication: data reporting, data visualization, business intelligence, decision making.
- Analyze: exploratory/confirmatory, statistical analysis, regression, text mining and qualitative analysis.
Data science lets us accomplish some of the main priorities that were either not feasible or took much more time and resources only a few years ago, such as,
- Detection of anomaly (fraud, disease, crime, etc.),
- Classifications (in an email server, this could mean that emails are categorized as “important” or “junk”)
- Forecast (sales, revenue and customer retention)
- Detection of trend (weather patterns, financial market patterns, etc.)
- Acknowledgement (facial, voice, text, etc.)
- Recommendations for (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)
In comparison, there are few examples of how organizations use data analytics to innovate in their markets, develop new goods and make the environment around them much more effective.
Data science has contributed to a variety of breakthroughs in the healthcare field. With a massive network of data currently accessible across everything from EMRs to health databases to personal fitness trackers, medical practitioners are discovering new ways of understanding sickness
Tesla, Ford and Volkswagen are also introducing predictive analysis in a new wave of autonomous vehicles. This vehicles use thousands of tiny cameras and sensors to relay information in real time. Using machine learning, statistical analytics and data analytics, self-driving vehicles can adapt to speed limits, prevent risky lane shifts, and even carry passengers on the fastest lane.
UPS is turning to data analytics to optimize performance, both internally and along its distribution paths. The company’s On-Road Integrated Optimization and Navigation (ORION) technology uses data-driven mathematical modeling and algorithms that create optimized routes for delivery drivers based on terrain, traffic, construction, etc.
Do you ever wonder how Spotify seems to recommend the right song you’re in the mood for? Or how can Netflix know what shows you’re going to love to binge? Using data analytics, the music distribution giant will deliberately curate lists of songs depending on the genre or band you’re actually in. Really in cooking lately? The data aggregator of Netflix acknowledges your need for culinary inspiration and recommends specific shows from its comprehensive list.
Computer learning and data analytics saved the finance sector millions of money and unquantifiable periods of time. For example, JP Morgan’s Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and retrieve critical data from about 12,000 commercial credit agreements a year. Owing to data analytics, what will take over 360,000 man-hours to complete is now over in a couple of hours. In addition, Fintech firms including Stripe and Paypal are investing extensively in data analytics to develop machine learning solutions that easily identify and avoid fraudulent activity.
Data science is useful in every field, but it can be the most important in cyber defense. International cyber-security company Kaspersky uses computer science and deep learning to identify more than 360,000 new malware samples on a regular basis. It is critical for our safety and security in the future to be able to rapidly discover and learn new cybercrime tactics through data analytics.