With tremendous progressions in the world of Information Technology, Data Science and Analytics; Machine Learning (ML) and Deep Learning (DL) have become some of the most common buzzwords in the industry. But what do these terms mean? Is there any difference between the three or are they linked? All of these questions may seem complicated at first, and there is a huge misconception related to them. Thanks to the rapidly growing technological industry, data science, analytics, ML and DL are growing at an astronomical rate and organizations have now started looking for competent professionals who can sift through the treasure of data and help businesses to achieve objectives efficiently.
According to a report by IBM, the number of jobs for U.S. data experts will increase by 364,000 openings to 2,720,000 by 2020. With such a considerable increase in the demand for talent, students and working professionals are required to upgrade their skill sets to meet industry standards and challenges. The chasm between the demand for proficient specialists and expertise for the same has increased the number of jobs available in this domain. However, most of us are still perplexed about what exactly is data science, analytics, ML and DL, and how are they interlinked with each other. This blog will help you gain a better understanding of these often-used terms and how these technologies are closely associated. Keep reading to know more.
Data Science and Data Analytics– Often misused or used interchangeably, both the terms, data science and data analytics, are different from each other. Data science comprises of everything associated with data cleansing, preparation and analysis of humongous volumes of data, structured or unstructured (Big Data). It is a combination of statistics, mathematics, programming, capturing data from various sources and aligning the data to business objectives or goals. On the other hand, data analytics involves using an algorithmic method to extract insights from raw data. It concentrates on processing and performing statistical analysis of current datasets. Data analytics simply means capturing, processing and organizing data to unbolt actionable insights for business challenges.
Machine Learning (ML)– Machine Learning is the utilization of Artificial Intelligence (AI) that empowers systems to learn and improve without being categorically programmed. It focuses on the development of computer programs that can retrieve data by observing large sets of information and identify patterns to make better decisions in the future. The primary role of Machine Learning is to allow systems to learn automatically without human intercession or support and plan future actions accordingly. Undoubtedly one of the most prominent and powerful technologies in today’s world, Machine Learning is simply a tool for transforming information into knowledge. There are three types of Machine Learning methods- supervised learning, unsupervised learning and reinforcement learning. Machine Learning enables the interpretation of tremendous quantities of data. Combining Machine Learning with AI and other cognitive technologies can make business processes more effective when it comes to analyzing large volumes of information.
Deep Learning– Deep Learning is a subset of Machine Learning, that simulates the functioning of the human brain in organizing, processing data and generating patterns for improved decision-making. It has networks that are competent in learning from unstructured or unlabeled data. Deep Learning enables machines to solve intricate problems by using algorithms through various layers of neural network algorithms. Each of these layers is built on its previous layer with additional data that may take decades to connect together if processed by a human being.
The multifaceted field of Data Science uses core skills of a wide range of areas including Machine Learning, statistics, visualization and many more. It allows us to identify the significance of huge volumes of data to make informed decisions about a business. AI is applied based on Machine Learning, and Machine Learning is a component of Data Science that extracts features from algorithms to work on the information retrieved from multiple resources. Therefore, Data Science blends together with a bunch of algorithms acquired from Machine Learning to develop a solution. All in all, it is essential to integrate Data Science and Data Analytics processes into businesses since it enables organizations to recognize trends, identify patterns and ensure that the operations are on the right track.