Can you easily differentiate data science from data analytics? In such a fast-paced world, it's no surprise that we sometimes confuse certain technical terms. Especially when they evolve at such dizzying speeds and new scientific fields seem to emerge overnight. That's why, in the world of big data , which involves working with enormous and complicated amounts of information, some people still confuse certain concepts, tasks, and roles within this emerging and growing discipline.
One of the main points of confusion in this field is the difference between data analysis and data science . Two closely related, yet distinctly different, areas.
Although both are at the crossroads of mathematics, statistics, and development, the purposes they serve have clearly differentiated tangents. Therefore, the profiles of a data scientist and a data analyst are very different. It's essential for anyone looking to specialize in big data to know what kind of knowledge and skills they'll need to acquire if they decide to focus on data analysis or data science. So, if you're currently interested in choosing one of these two paths, this information is of interest to you.
For decades, experts have tried to narrow down the scope of one discipline or another, but they haven't always been successful. However, since 1996, when the term "data science" began to be used thanks to an article by Gregory Piatetsky-Shapiro, definitions have advanced significantly. So it seems we can now clarify the scope of both fields.
Currently, data science is considered a branch of big data . Its objective is to extract and interpret information derived from the vast amount of data collected by a given company, whether for its own use or for operations it may carry out with third parties. To achieve this goal, data scientists are responsible for designing and implementing mathematical algorithms based on statistics, machine learning, and other methodologies. These algorithms allow companies to obtain the basis for acting in one way or another depending on the circumstances and the moment. It's not just about extracting information from the collected data and being able to use it. Data scientists are also tasked with ensuring that the patterns detected are correctly visualized, so that they are clear and legible for those making decisions based on said data.
Data analysis is generally seen as a more specific and precise application of data science . Therefore, in industries that have incorporated data analytics, the role of analysts has been to search for sources of raw information, trying to find trends and metrics that help companies make better decisions and achieve better results. In this case, we must be careful not to confuse their work with that of someone in business intelligence, who handles a much smaller amount of data, so their analytical and predictive capabilities are more limited.
As such, the main difference between data science and data analytics is the branch of big data each field focuses on: while the former is on the path to discovery with a wide-eyed eye, the latter is more focused on the operations of different businesses that apply and seek solutions to existing problems.
So, while a data scientist becomes adept at predicting the future and bases his or her predictions on past patterns detected in the data, data analysts extract the most relevant information from the same data sets. It could be said that, while the former asks questions to try to map out what will happen in the coming years, the latter is responsible for answering the questions that are already on the table.
On this basis, another major difference between the two disciplines is how they are applied in different industries. In fact, data science has had a major impact on search engines, which use algorithms to provide better answers to user queries in the shortest possible time. Similarly, data scientists have had a significant impact on the development of recommendation systems.
An example of this is Netflix, or shopping sites like Amazon. These systems offer customers much more precise recommendations, which greatly enriches the user experience.
In the case of data analytics, they are most frequently used in sectors such as healthcare, allowing health centers to serve their patients more efficiently. This discipline is also widely used in other industries such as energy management, as data analysis allows them to optimize the use of resources and even choose to automate certain services, thus avoiding unnecessary costs. Analysts are also highly sought after by the tourism industry, as they can help hotels discover travelers' preferences and offer them the options that best suit their tastes and needs.
As you can see, there are many factors to consider before delving into the world of big data. Data analysis and data science are closely related disciplines, but there are several differences between the two. Here's a summary of the main differences we've discussed throughout this post:
Data Science
• Allows you to create predictive models and algorithms
• Uses statistics and mathematics
• Knowledge of SQL , Python, R, SAS, and Scala is a requirement
• Requires advanced machine learning knowledge
• Tends to work with unstructured data
Data analysis
• Can be applied in sectors such as artificial intelligence, healthcare, blockchain, or web search engines• Provides insights from different data sources
• Employs data warehouses, ETL tools, and business intelligence
• Uses data manipulation
• Requires expertise in data visualization
• Requires business knowledge and decision-making skills
• Applies to industries such as retail, travel, healthcare, and marketing
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