Data science is an interdisciplinary discipline that utilizes various scientific techniques, mathematical models, algorithms and strategic systems to extract relevant information and insights from large and complex data sets, and apply such insights and information from data over a wide range of application domains. Data science used to be an area of specialization within the faculty of information systems, but over the past decade or so it has become increasingly important as data is becoming an indispensable part of many business practices. Data science can also be used to refer to a set of other technologies and scientific areas such as computer science, engineering, statistics, or even business studies that deal more with the manipulation and organization of data.
The foundation of data science is mathematics and statistical principles. The main areas of focus for data science are statistical inference, biological design and machine learning. The primary tools used in data science are the computers and the mathematical and statistical methods for manipulating, managing and analyzing data sets. Examples of tools that are typically used in data mining techniques include neural networks, applications in data mining, supervised and unsupervised learning, decision trees and the R programming language.
Data visualization is a major part of the science data process. Data visualizations can represent data sets using both text and graphics format. These visualizations can be used for communicating findings in a clear and concise manner, as well as in providing users with insight into underlying trends or patterns.
There are many different things that data science can do, but two of the pillars of the discipline are statistics and research methodology. Statistically, data scientists use various techniques such as probability, sampling, and historical trends to evaluate and interpret data. They then create charts, graphs, and visual presentations to present findings from their findings.
For a data scientist to make a graphic representation of a study’s statistical results, he must perform some heavy lifting. One such thing that a statistician often does is create a visual image from raw data using computer software. He might begin by creating a simple bar graph (e.g. price versus size) and then add more variables, such as company size, product type, or geographic area. He can also average two or more variables together to get a more reliable picture of the data sample. After he has created the graphic, he will often write a report, which is typically a draft, detailing the image he has generated.
As previously mentioned, data visualization is a key part of the data science process. There are a number of tools available to data scientists to help them create visually appealing reports. Some of these tools even allow data scientists to write their own reports, although an in-house data visualization tool may be preferred for the creation of data-mining reports. Data visualizations are especially important for business intelligence (BI) purposes because they are so appealing. A business intelligence report can easily tell a story with just a few graphical elements.
Data mining and data analysis can also be performed by the business analyst who is not a computer scientist. In fact, a data analyst might perform data mining on his own, without having to consult computer science textbooks! Examples of data analysis jobs would be performing consumer research; analyzing the financial records of a corporation; or creating custom software applications (for example, a PDA application). The data analyst would also need to have statistical expertise; however, many of the job responsibilities would be fulfilled by the data analyst if he was a computer scientist. For example, if he were analyzing customer records, he would only need to access the accounting information in order to find the correlation between customer characteristics and overall company performance.
As you can see, there are many similarities between the two areas of data science and computer science. However, the focus of this article is to highlight the major differences. In general, one focuses on the collection of statistical data in order to support a particular hypothesis, while the other explores the relationships between that hypothesis and various variables. From there, it is often possible to draw inferences and draw a relationship between variables and the real world. In the case of business intelligence, data science explores how business intelligence is shaped by big data.