Data science is a rapidly expanding field which employs mathematical, statistical, technological, and engineering techniques in order to extract practical knowledge and insights from large and complex data sets, and then apply those insights and facts in a wide variety of real-world areas. Data scientists frequently use mathematical algorithms to analyze large sets of data in an effort to find patterns and relationships among the data that they are studying. In the past few years there has been a focus on the advancement of data analysis technologies, as well as data mining techniques.
Data science relies on the core capabilities of several mathematical fields including mathematics, computer science, statistics, computer programming, engineering, and other mathematically-based disciplines. Statistics is one of the primary fields in which data science can be best applied. Statistics is an area of study which studies the way in which economic activities, such as production, sales, marketing, etc., are distributed across various elements within a societal structure. Economic statistics provide the knowledge to managers and executives so that they can determine which actions are most likely to yield positive results in terms of improved organizational performance. Thus, with the use of this statistical knowledge managers can improve their ability to create policies that are effective, implement business strategies that yield the best results, assess the performance of key employees, and take other necessary actions that increase organizational performance and achieve organizational objectives.
One of the most important tools in data science falls under the topic of statistical inference. In simple terms, statistical inference refers to the process of making statistical claims about the existence or non-existence of a certain property or effect. For example, if we wish to examine the relationship between intelligence, stereotypes, and business intelligence, we must perform a statistical analysis in order to make a meaningful hypothesis about the relationship; and, if our hypothesis is correct, then we should be able to discover a relation between the variables we have statistically studied and the observed characteristics of intelligence, business intelligence, or stereotypes.
The science data lifecycle consists of three main stages: Data Collection, Analysis, and Verification. In order to collect data or to obtain information that is needed to conduct a statistical analysis, a data scientist must either be a statistician (or a close relationship to one who is) or someone with exceptional analytical skills and a background in scientific methodologies. The analysis stage of the science data lifecycle begins with the collection of data. There are different methods to collect data and the selection will depend on the needs of the data set and the potential precision of the final outcome. Once data has been collected it is stored in either a RDBMS (relational database management system) such as SQL Server, Oracle, MySQL, or MS Access, or it can be stored on a specialized platform.
The next step in the science data lifecycle is the interpretation or modeling stage. Data scientists often begin by conducting short, qualitative interviews or surveys in order to gain a better understanding of target customers, organizational culture, etc. Once the data has been gathered and analyzed, it will need to be converted into a format that can be easily manipulated and used in a statistical analysis. The third stage of the lifecycle is the verification stage, which involves making sure that the collected data is correct and that the final output is accurate.
Data science skills are an important part of the success of any organization. Whether you are a small business with just 5 employees or a multinational company with hundreds of employees, data science skills are an essential part of your success. There are many tools used by a data scientist to analyze data and to extract valuable information from the data that is analyzed. Tools such as computers, data processing software, databases, graphics processing tools, and the web itself are all part of the tools used by a data scientist. Although data science skills are not difficult to learn and develop they are not easy to maintain, especially in a fast changing and a rapidly advancing world where your competitors can quickly and efficiently use the information that you have to better serve their customers and for more profit.
