How does visual programming powers complex data science?

Modern programming technologies are trying to integrate a visual approach to program development. As a result, text programming languages have received visual development tools and become integrated development environments. In this article, we will discuss the impact of visual programming on complex data science.

What is Data Science?

Data Science includes all the tools, techniques, and technologies that help us process data and use it for our benefit. It is an interdisciplinary mixture of statistical inference, data analysis, algorithm development, and technology to solve analytically complex problems.

The three main pillars of Data Science:

  • Data organization – storage and formatting. It also includes data management practices.
  • Data aggregation – combining the original data into a new view and/or package.
  • Data delivery – providing access to arrays of aggregated data.

The main practical goal of a data scientist is to extract useful information for business from large amounts of information, identify patterns, develop and test hypotheses by modeling and developing new software.

For the IT industry, Big Data is an integral part of the work, because by analyzing user data, you can get to explore the prospects of a product, predict the market and customer behavior. In addition to IT, Big Data is used in marketing, finance, telecommunications, retail, the energy industry, the public sector (everything related to e-government), and so on.

The basis of visual programming

Since the 1970s, visual programming languages have begun to appear, allowing users to develop programs based on by manipulating graphic elements (blocks, arrows) used as elements of language syntax, in contrast to writing the source code. Today there are more than 70 visual programming languages.Programming technology is a set of methods and tools used in the software development process.

Like any other technology, programming technology is a set of technological instructions that include:

  • indication of the sequence of technological operations;
  • enumeration of the conditions under which this or that operation;
  • descriptions of the operations themselves, where for each operation the initial data, results, as well as instructions, norms, standards, criteria and methods of assessment, etc. are defined.

Visual programming can be used both during development and during software maintenance. In development – mainly in the design and analysis of the system, which precedes direct programming. Accompanied – when new developers study the software they inherited. Visual modeling can also be used in various activities of the software development process: mainly in analysis and design, but also in documenting, testing, requirements development, etc.

Visual modeling is applied in practice using methods, languages, and appropriate software tools. Visual languages are formalized sets of graphic symbols and rules for constructing visual models from them. Such visual modeling languages as UML and BPMN are now known and actively used in practice.

Among modern methods of visual modeling, perhaps the most widespread is RUP / USDP – an industrial method of creating software that uses UML on almost all stages and in all types of development activities.

Connection between visual programming and Data Science

To work with Big data, you should be able to program. For example, to download data, parse, synthesize new features or implement any other idea of yours. The main programming language of most Data Science professionals in Python. Python itself is a very simple language, it implements many libraries for data processing and analysis. The previously popular R and Matlab are less and less common today, so if you’re just starting to master Data Science, focus on learning Python.