For many amateurs in the technology world, there can seem to be a bit of confusion as to the difference between data science, data analytics, and data analysis.
It can be quite unnerving and confusing since these three terms are used to refer to data and are connected. What then is the difference?
Data Science
Finding a data science and analytics job means you need to be aware that though they are similar they are also different. Data science deals more with code programming and AI. It is the parasol that houses data analytics and data analysis. Data science is the study of how to extract useful knowledge from data in large amounts. It is also defined as an interdisciplinary field that uses scientific methods, processes, algorithms, to extract knowledge and insights from noisy, structured, and unstructured data. Data science is related to data mining, machine learning, and big data. Experts in the data science field are called Data scientists. They create programming codes, combine them with advanced math and statistics, predictive modeling, and create insights from data.
Data Analytics
The purpose of data analytics is to investigate and report on data to form conclusions that can affect future actions. Data analytics is the science of analyzing raw data to make conclusions about that information.
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. Analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Data Analysis
Data Analysis is a subsect of Data Analytics. They look like one and the same, though there is a little contract. Data Analysis is more descriptive. It is the process in itself hence being captured as analyzing past data.
Data analysis is the process of summarizing and describing data. The goal of data analysis is to reduce raw data (numbers, measures, codes) into meaningful summaries that can be easily understood by a human reader. In some instances, data analysis will include explicit comparisons among different groups or conditions to spot patterns and make predictions. It is the process of working with data to gain knowledge, which can then be used to make informed decisions.
