Data Science is not a new term in the world of technology today, at least over the last decade. It has taken over the corporate world, health, and more data-driven organizations in the last few years. The demands for skilled data experts/professionals have increased, as organizations are on the steady post for data science professionals to resolve business complexities with efficient and productive data analysis. With many people utilizing the power of data today, we have adequate, or if I may say, unlimited resources from where one can learn what’s new in the domain. To make things easier and to understand data science better, I have gathered an almost exhaustive documentation to help you understand. Without further ado, let’s dive right into the many definitions of what data science is.
Many Definitions of Data Science
Data Science is the process of using data to understand things – to understand the world. For instance, you have a model or hypothesis of a problem, and you try to validate that hypothesis or model with your data – data science applied. Some also say, it is the art of uncovering the insights and trends that are hiding behind data. Data is real, it has real properties, and we need to study them if we're going to work on them. Data Science involves data and some science but it's more about data than it is about science. Hence, data science is one's attempt to work with data, to find answers to questions that they are exploring. If you have data, and you have curiosity, you can manipulate and analyze data to get answers. The actual exercise of analyzing data and attempting to find solutions from it is data science. And in the end, you translate the data into a story, use storytelling to generate insight and with these insights, you can make strategic choices for a company or an organization. Data could be anything from facts, statistics or quantities collected to derive answers for the system in question. For instance, you want to defend a proposal that women have higher IQ than men. So you decided to set up a chess game to gather data on performance, hence, using chess as the determinant for higher IQ. The side that wins most upon many trials has higher IQ. You gather win and loss data then later use data science techniques to support your hypothesis. And with the story, you uncover insights hiding behind the data collected.
Data science is important today because we have overwhelmingly enormous data available. In the past, the software was expensive, now it's open source and free as compared to a few decades past where we couldn't store large amounts of data, but now for a fraction of the cost, we can have zillions of datasets for a very low cost.
You will always get a slightly different description of what data science is, but most people agree that it has a significant data analysis component – to answer questions or make recommendations. Here again, data analysis isn't new, what’s new is the vast quantity of data available from massively varied sources: sensors collecting data all over, log files, sports performance, medical records and a lot other. As there are more data available than ever, we have the computing power needed to make a useful analysis and unveil new knowledge.
Terms Associated or Used Interchangeably with Data Science
There are many terms associated with data science and some that are used interchangeably, so let's explore the most common ones.
Big data alludes to data sets that are so huge, so quickly built, and so varied that they defy traditional analysis methods such as you might perform with a relational database; thus, they resist conventional data analysis. The concurrent improvement of computing power in distributed networks and new tools and techniques for data analysis means that organizations now have the power to analyze these vast data set.
Data mining as the name suggests, is the process of automatically searching and analyzing data, discovering previously unrevealed patterns. It involves preprocessing the data to organize it and remodeling it into an appropriate format. Once this is done, insights and patterns are mined and extracted using various tools and techniques starting from simple data visualization tools to machine learning and statistical models.
Machine learning is a subset of AI that utilizes computer algorithms to investigate or analyze data and make intelligent decisions based on what it has learned without being explicitly programmed. Machine learning algorithms are trained with large datasets and they learn from examples. They do not adhere to rules-based algorithms. Machine learning is what enables machines to solve problems on their own and make accurate predictions or help us make recommendations using the provided data.
Deep learning is a specialized subset of machine learning particularly designed to utilize layered neural networks that mimics human decision-making. Deep learning algorithms can label and classify information and recognize patterns. It is what enables AI systems to persistently learn on the job and improve the quality and accuracy of results by determining whether decisions were correct.
How Data Science Helps Organizations
Data science has helped organizations to get a better understanding of their environments, analyze existing issues, and unveil previously hidden opportunities. Data scientists use data analysis to add to the knowledge of the organization by investigating the most ideal approach to utilize it to offer some incentive or add value to the business. Good data scientists are curious people who ask questions to clarify the business needs. Data scientists can analyze structured (organized data that has noticeable patterns) and unstructured (clustered with no pre-defined manner) data from many sources depending on the nature of the problem.
Using various models to investigate the data reveals patterns and outliers; now and again, this will affirm what the organization suspects, however, at uncertain times it will be completely new knowledge which may lead the organization to use a new approach. When the data has revealed its insights, the role of the data scientist becomes that of a storyteller, the narrator, communicating the results to the project stakeholders. Data scientists may use powerful data visualization tools to help stakeholders understand the idea of the outcomes, not just projecting the results, and the recommended action to take.
Benefits of Data Science: Better Solution That Is Efficient?
Organizations can leverage the almost unlimited amount of data now accessible to them in a tremendous number of ways. In any case, all organizations ultimately use data science for the same reason—to discover ideal answers to existing problems, to find new ways, or make recommendations based on findings. Let’s take a look at one example of data science providing creative and innovative solutions for old problems. In transport, Uber collects real-time user data to discover how many drivers are available, if more are required, and if they should allow a sudden increased charge to attract more drivers. And this helps Uber make more refined decisions as to put the right number of drivers in the right place, at the right time, for a cost the rider is willing to pay.
Using Data Science techniques to understand and analyze the big data sets available today has a huge impact on human lives. It has provided targeted information to assist healthcare professionals give the best treatment to patients, also, helped in predicting natural disasters so people can prepare early, and far more besides. It will take time for an organization to refine best practices for data strategy using data science, but the advantages are worthwhile. In a nutshell, data science has made life easier for everyone.
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