Data Science and Machine Learning are two booming and fast-pacing technologies. Being the talk of the hour, they entice the attention from tech buffs and IT explorers from around the world. Organizations look for workers who sift through the data real quick and deliver insights to drive business decisions efficiently. People may often confuse the two technologies, for they are closely wired but have relatively different functions and goals.
Data Science and Machine Learning buzzwords are the most searched on the Internet nowadays. Hence, it is worth knowing why the two domains are exciting while looking for job potential as a fresher. And the skill sets one must own to gain a strong foothold in either of the fields in the long run. Here’s a draw out of the differentiating points between data science and machine learning.
Unbelievably, Google, Microsoft, Amazon, and Facebook store 1200 petabytes of information. Thus, there is a heavy dependence on quality data in the industrial sector, now more than ever. Therefore, it only makes sense to form this huge amount of data as quality information for business stakeholders and analysts to escalate businesses to unknown heights. These six points described below will help you explain how Data Science and Machine Learning help businesses to evolve together.
Also Read:Data Science vs Data Analytics
Data as information exists in textual, numerical, audio and video formats. Thus, data science deals with data extraction, sanctification, preparation and analysis to understand it from the business perspective.
Data science deals in gathering data from disparate sources in different structures and formats. The data engineers are then responsible for transforming, combining and processing the captured raw data into quality data readily available for further analysis. Data analysts and scientists pick up the processed data to extract critical information and significant patterns for analysis and predictive insights that impact invaluable industry decisions.
This stream handles the Big Data using pre-processing tools, predictive analysis and statistical models to derive regular patterns for enforcing reasonable acuities. For example, Netflix uses data science to study the user’s viewing interest patterns by mining his recent search results and viewing history.
Data Science is a field of study that approaches to find insights from raw data.
As a branch of computer science, Machine Learning is a study that enables the computer to solve problems without implementing explicit programs to solve them step-by-step. ML is implementable using different methods such as supervised, unsupervised and reinforcement learning methods. Every ML method has its pros and cons.
Using Machine Learning, your machine or system is learning by applying algorithms to the data set. And these algorithms act as the instructions for the ML method to perform a process. A machine determines the patterns itself from the given data and then learns from the approach to make its own decisions. One of the hyped machine learning techniques of the hour is Neural Networks, which require a machine to make decisions similar to a human brain.
In Neural Networks, machine learning applies the algorithms to process the data and train itself to deliver future forecasts without any human intervention. It enables the machine to learn from the past data and apply the resultant patterns to other given tasks automatically. For example, Google and Facebook use the inputs from ML as a set of instructions/ data/ observations for anticipating ads and notifications to users.
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Source: https://candid.technology/data-science-vs-machine-learning/