The latest technology advances of data science and machine learning made them powerful business tools. Data Science-powered solutions can deliver actionable information that enables companies to improve efficiency, effectiveness, and customer satisfaction.
Data Science vs Machine Learning
Data science is the practice of taking a set of information and drawing conclusions from it. Analytics and data science go hand in hand, but they are not the same thing. Data analytics is a simple presentation and understanding of the data itself. Once you've begun to draw conclusions and make improvements, you've crossed into the realm of data science. While data science requires good investigation and analytics to be effective, it's primarily focused on using that data as a source of hidden insights to solve business problems.
Data science-based projects typically consist of well-defined stages, with the research stage taking up to a few months to complete. A wide range of methods are used to analyze data and produce an appropriate business model. Samples of data are prepared for further analysis down the line. With smart sequencing and pattern choices, this step of the data science process can be completed without training neural networks.
Machine learning, in its simplest cases, does not require scientific preparations or mathematical elaborations. It is more about using a dataset to train neural networks. However, we can always think of more complicated cases, given the uniqueness of every software project.
Data Science & Machine Learning: areas of usage
Data science has a wide range of applications. It can be used to recognize purchasing patterns for a retail company or predict hardware failures in an expansive network architecture. Almost anything that produces a pattern or sequence, from retail habits to employee productivity, might reap benefits from data science. But the main question is: how exactly can data science be used in very particular use-cases?
Data science for the retail industry
The retail industry is already seeing solutions that involve data science to predict demand and sales, increase customer retention, and create pricing models. Advertisers make avid use of data science to target specific demographics and deploy marketing campaigns effectively. This kind of statistical data is particularly effective in web-based environments, where user engagement can be meticulously tracked on a wide scale with huge datasets.
Data science for anomaly detection
Product and service security can also be improved by data science. Identifying anomalous patterns in transactions, customer activity, or network traffic can increase threat detection and prevention, allowing for a more effective security profile.
Data science and computer vision for medical imaging
Data science and computer vision can be used to analyze such medical images as MRI, X-Ray, and ultrasound. Sophisticated algorithms are able to more accurately interpret extracted data from images and provide a better diagnosis.
There has been impressive progress in this area, and current technological constraints are mostly associated with hardware limitations in functionality and computational power.
Data science and recognition
Biometric recognition is one of the more publicized uses of data science and machine learning, and it is already taking advantage of huge datasets to teach software how to recognize faces, voices, and handwritten text. This can provide improvements to everything from biometric authentication to speech-to-text conversion.
Natural language processing
Language analysis using data science can be used to quickly scan knowledge bases for similar information, providing a method to search books, papers, and articles for relevant information.
How to integrate Data Science & Machine Learning into your software project
Any project looking to integrate data science into its process will need a certain quantity of data for subsequent analysis. The larger the dataset, the better the results. Projects in the early stage of development are unlikely to have a sufficient amount of working data to extrapolate from, unless the data is being provided from other sources.
Existing data services like Google Cloud AI, IBM Watson Machine Learning, or Microsoft Azure Machine Learning, provide machine learning tools that have already been subjected to enormous datasets. These services can be a useful but expensive alternative to developing your own machine learning algorithm. A handful of machine learning APIs and open-source developments for specific tasks already exist, and could be used to advance your own projects.
When used properly, data science is a powerful tool that can be used to advance a variety of business goals. At its core, data science is a simple analysis and interpretation of datasets that has been scaled up to be an enhanced method of planning for the future. With this in mind, data science can be used in nearly any sector of business to improve the value of products and services offered.