Data science is the use of scientific inquiry, algorithms, and models to analyze and interpret data in order to address some business concern or capture opportunity. The main differentiator between data science and other forms of analytics is the depth of analysis performed on the gathered data. Classical analytics is referred to as using descriptive analytics, which structures data into categorical variables. Data scientists go further than this, running statistical tests, discovering correlations, and interpreting the results of such tests and correlations. Data scientists often include fields from the natural sciences and social sciences. While these fields are often distinct they are all involved in the interpretation of data, and until recently were typically considered separate from one another.
The Foundations of Data Science?
Data science is an inter-disciplinary field that employs computational techniques, procedures, algorithms, and systems to derive information and observations from structured and unstructured data, as well as to extend knowledge and actionable insights from data through a variety of application domains. Data analysis, artificial learning, and big data are also facets of data science.
Preparing data for research, defining data science issues, evaluating data, designing data-driven solutions, and delivering results to guide high-level decisions in a wide variety of application domains are all part of this area. As a consequence, it blends computer science, analytics, information science, mathematics, data visualization, data integration, and complex systems skills.
Impact of Data Science
Big data is rapidly gaining popularity as a valuable platform for companies of all sizes. Big data's availability and analysis have changed old industry market models and allowed the emergence of new ones. In 2020, data-driven companies are expected to be worth $1.2 trillion, up from $333 billion in 2015. Data scientists are in charge of translating big data into useful knowledge and designing applications and algorithms that assist enterprises and organisations in deciding the best course of action. Because of their close association, big data and data technology continue to have a significant influence on the planet.
Techniques
Depending on the use, a number of various tools and techniques are used for data analysis. For data science and machine learning, full-featured, end-to-end systems have recently been developed and are widely used.
Linear regression
A linear approach to modeling the relationship between a scalar response and one or more explanatory variables is known as linear regression. Easy linear regression is used where there is just one explanatory variable; multiple linear regression is used where there are more than one. This is not to be confused with multivariate linear regression, which predicts multiple correlated dependent variables.
Decision tree
A decision tree is a decision-making aid that employs a tree-like model of decisions and their potential outcomes, such as chance case outcomes, resource costs, and utility. It's one way to present an algorithm made up entirely of conditional control statements.
A deep understanding of Mathematics along with programming language abilities is required for a career in Data Science.
