Data Science

It is an applied, interdisciplinary science that uses methods and knowledge from numerous different fields such as mathematics, statistics, stochastics, computer science and industry know-how. The term Data Science has been used since the 1960s. At that time, it was still considered synonymous with computer science. Since the beginning of this millennium, data science has been a discipline in its own right.

There are separate degree programs for Data Science. People working in the field of data science are called data scientists. They do not need to have completed a Data Scientist degree program; they may be mathematicians, computer scientists, programmers, physicists, business economists, or statisticians who have acquired their knowledge by specializing in data science tasks.

The goal of data science is to generate knowledge from data in order to support decision-making processes or optimize corporate management, for example. In the Big Data environment, Data Science is used to analyze huge amounts of data with Machine Learning (ML) and Artificial Intelligence (AI). Data science is used in various industries and specialist areas.

Goals of data science

The goals of data science are:

  • Generate knowledge from data,
  • Derive recommendations for action from data,
  • Support decision making,
  • Optimize and automate business processes,
  • support corporate management, and
  • Making forecasts and predicting future events.

Disciplines of data science

Data Science is an interdisciplinary science that uses and applies knowledge and methods from different fields. Mathematics, statistics and stochastics form an important block of knowledge. It provides the basis for evaluating data, interpreting it, describing facts or making predictions. In order to predict future events in the context of predictive analytics, inductive statistics, for example, is used in addition to other statistical methods.

Another block of knowledge in data science is information technology and computer science. Information technology provides procedures and technical systems to collect, aggregate, store in databases, and analyze data. Important elements in this area are relational databases, database query languages such as Structured Query Language (SQL), programming and scripting languages such as Python and Perl, and some more.

In addition to concrete scientific knowledge, Data Science accesses so-called industry knowledge (domain knowledge or industry know-how). This knowledge is necessary to understand the processes in a particular organization or company in a specific industry. Domain knowledge can be, for example, business knowledge, logistics know-how or medical expertise. Big Data and data science

Due to the continuous increase in the amount of data that is generated and needs to be processed or analyzed, the term Big Data has become established. Big Data deals with methods, procedures, technical solutions and IT systems that can handle the flood of data and process large amounts of data in the desired form. Big Data is an important area of data science. Data Science provides knowledge and methods for collecting and storing large amounts of structured or unstructured data (for example, in a data lake), processing it in a high-performance manner using parallel processes, and analyzing it. Data science uses, among others, data mining, machine learning (ML), deep learning (DL), artificial neural networks (ANN) and artificial intelligence (AI).

What does a data scientist do?

People who deal with data science are called data scientists. They have acquired their knowledge either by completing a data science degree program or by specializing their expertise. Often, data scientists are computer scientists, mathematicians, statisticians, business economists, programmers, database experts or physicists who have undergone further training in data science.

In addition to subject-specific knowledge, a data scientist must be able to present the knowledge generated from the data in a clear manner and bring it closer to different target groups. Appropriate communication and presentation skills are necessary. In the corporate environment, the terms Data Scientist, Business Analyst or Data Analyst are often mixed up. In some cases, their tasks and areas of activity overlap.

While the data analyst performs classic practical data analysis, the data scientist pursues a more scientific approach with sophisticated methods, such as artificial intelligence or machine learning, and advanced analysis and prediction techniques. Compared to the business analyst, the data scientist differentiates themselves in that the focus of their activities is not purely the analysis of business models and business processes. Business analysts use the data prepared by data scientists and the data models and tools they provide, such as interactive dashboards or KPIs, for their analyses.

Application areas of data science

There are almost no limits to the possible applications of data science. Wherever large amounts of data are generated and decisions need to be made based on the data, the use of data science methods makes sense. Typical industries and disciplines where data science is of great importance include healthcare, logistics, online and brick-and-mortar retail, insurance, finance, manufacturing, and industry.