Tim Berners-Lee, the inventor of the World Wide Web that is enabling you to read this article, is reported as saying: “Data is a precious thing and will last longer than the systems themselves.” The Turing Award winner was prescient in that and in so many other ways. To an extent, the whole world now runs on data, and vast amounts are created every day. Current best estimates are that at least 2.5 quintillion bytes of data are produced every single day, adding to a vast store of information and potential value. To get to that value, you must have methods of working with huge and complex datasets in order to find patterns and meaning. That is the basis of data science, although it is a lot more complex when you delve into the details.
Taking an Online Master’s in Data Science will allow you to do just that, and could be the perfect way into an exciting, constantly expanding and ever-evolving field. A quality course like the one provided at Worcester Polytechnic Institute (WPI) will provide interpersonal skills like communication — essential because data scientists do not exist in a vacuum with their data. They have to communicate their insights with colleagues and stakeholders. However, at its core, the role of a data scientist is a technical one. That is why students must learn to work with cutting-edge technologies such as machine learning and data mining algorithms. Data science relies on technology to be able to carry out the advanced analysis techniques that are involved. At the same time, the insights and findings unearthed from the data can help improve existing technologies and innovate new ones. It is a kind of virtuous circle, with data at its ever-revolving hub.
What does data science involve?
Data science is a complex field that draws on many different disciplines and technologies. These include, but are not restricted to:
- Mathematics
- Statistics
- Computer science
- Programming
- Statistical modeling
- Database technologies
- Signal processing
- Data modeling
- Artificial intelligence (AI)
- Machine learning
- Natural language processing
- Data visualization
- Predictive analytics
Data science is increasingly important to businesses and large organizations including government and public sector entities. The effective collection, selection and analysis of data can be used to help these organizations make more informed, data-led decisions and predictions. It can also improve operational efficiencies, identify new opportunities, find and eliminate mistakes and improve marketing, sales and many other areas.
In specific sectors, data science can be used to reduce fraudulent transactions in financial institutions, improve cyber-security and resilience, boost medical research and diagnostic techniques and help prevent breakdowns in industrial settings. In fact, there are few areas of modern life where data and data science do not have an impact.
What is big data?
When we talk about data, we usually think of digital or computable data. Data can be a collection of any facts and statistics that are gathered for reference or analysis (‘data’ is the plural of the Latin ‘datum’, meaning a given, or that which we take for granted and use as the basis of our calculations). The advent of widescale and powerful computing has made data very important in the modern era. Computers have been able to make calculations and manage information much faster than an unaided human. Big data involves working with datasets that are too big for what have now become traditional computing techniques and data processing software.
These datasets have greater volume and variety and arrive with greater velocity (the original ‘three Vs’ of big data). They are often unstructured, and specialist software and systems have been built —and are constantly being updated, redesigned, and replaced — in order to keep up with the demand for and ever-increasing scale of big data. Big data is increasingly being used in business and finance, as well as other settings, to monitor consumer insights, understand media usage, create targeted content, assist in scientific research and much, much more.
Technologies used in data science
There are numerous technologies that are either involved in enabling advances in data science, or which data science enables, including areas like the following:
- Machine learning
Machine learning refers to computer systems being used to learn and adapt without following specific instructions from human operators or other machines. It is a key element of artificial intelligence (AI) and typically requires large datasets with which to train the algorithms.
- Cloud computing
Cloud computing services like those offered under the umbrella of Amazon Web Services (AWS) are heavily used by data scientists. Individual offerings that may prove useful include Amazon Machine Learning (AML), Amazon Redshift, which is designed for data warehousing and analytics, Amazon Simple Storage Service (S3) which allows data scientists to access large volumes of information from distributed systems, and the image recognition system Amazon Rekognition.
- Text mining
Text mining involves trawling through large volumes of written information to look for trends and patterns that might not be apparent from individual documents such as patient records or social media posts. A few use cases for text mining include data extraction, topic modeling and sentiment analysis.
- Internet of Things (IoT)
The growing network of physical objects connected to the internet is becoming a major contributor to the growing deluge of data. In 2021, there were more than 10 billion active IoT devices and this is predicted to hit more than 25 billion by 2030. There are various uses for this data, such as predictive maintenance, which allows companies to monitor mechanical performance and act before breakdowns occur. Insurance companies also use the data from ‘black box’ technologies for risk assessment with some drivers.
- Streaming analytics
Streaming analytics allows data scientists to analyze data in real time — as opposed to batch processing, where data is collected, stored, and analyzed after the fact, to give retrospective insights. One common use of this sort of data analysis is in weather forecasting.
There are many other examples of data science driving technology and vice versa and, as these technologies and techniques continue to evolve, that looks to remain the case for the foreseeable future.
