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Data Science Empowering Software Development

These days data science experts are starting a tight collaboration with software developers to expand business horizons.

Deriving benefits from Big Data is a huge contribution to software generation advance that induces deep engagement and close cooperation between entire businesses and their divisions, which is why the ventures react to this challenge by the attraction of top-notch talents in showers. Based on the information provided by IBM in the recent report, the annual world necessity in data science analysts, developers, and engineers will amount to about ¾ million employees in a couple of years.

Yet, the scope of job openings available won’t reveal the actual trend in software development companies aspiring to data analysis reinforcement. In fact, data science's impact on the industry leads to the reconfiguration of processes and jobs throughout the industries, and this tendency is going to strengthen in the future. These days data science staff and software developers work in harness with each other, which is a clear proof that the segments of data science and software creation are consolidating.

In a nutshell, data scientists don’t have to be consummate peers in software generation and coding being enabled with cutting-edge instruments for processing data that is delivered via the cloud. Utilizing cloud technology as an evenly distributed ecosystem allows the arrival of the required data to a proper person at a specified time, which eliminates the redundant processes of data transfer through multiple intermediates followed by information loss or distortion.

Deployment of cloud technology to generate easy-to-use and effective interaction environment provides the data scientists with the possibility to concentrate their key knowledge and expertise to extract the most valuable content from the data. Being empowered with these innovative instruments, data science experts may consider their function to be of the same direction as software developers’ duties since both teams work on supplying improved solutions to the clients by making use of data analysis potential.

A union of methodology and technology

Methodology

The data science experts and software developers are in charge of different segments of production flow. Specifically, data scientists research information for new conclusions completion, and software developers utilize these conclusions to robotize the operation flow and to produce applications. Yet, both data scientists and developers pursue the same aims to supply functional applications, and this aim is better achieved upon tight well-organized cooperation.

The methodology of application production procedure comprises the components of scientific trials. For instance, in order to develop the app, data researchers analyze source data, perform analytical prototyping to derive helpful conclusions from the information. Then, these conclusions are transferred to the developers who transform the obtained data analytics into operational solutions delivered to the end-user by applying a suitable coding language. This procedure of interaction between data analytics and product development are circular and continued meant to produce innovative and efficient applications.

Essentially, tighter interaction is crucial to derive profits from the power of Big Data and to arrange the most productive application development workflow. Such cooperation can be organized by the deployment of agile process methods and integration of cloud technology to ensure quickest data exchange among the data scientists and software developers in a single ecosystem. In particular, this approach allows getting a long-running vision of the project prospects and getting the intermediate results at each point of the production process on both sides of the collaboration system. Besides, thanks to this bilateral interaction model, the development teams have more space and agility in reacting to end-users comments as well as the ability to combine the efforts of data scientists and developers focusing on the same task resolution. What really matters to make this cooperation successful is well-adjusted communication. Since the cloud is an outstanding instrument to distribute information among multiple members, it is also important to secure unblocked communication channels, so source information and end-users’ responds could flow smoothly.

As the potential of Big Data analysis in favor of improved software product generation is not fully comprehended yet, data scientists and developers are likely to elaborate new progressive tools to process huge volumes of information upon the condition of continuous cooperation.

Technologies

One of the cloud-based instruments suggesting faster data supply upon close interconnection is Jupyter notebook. The notebooks provide the users with the possibility to create and distribute code by using different languages such as Scala, Node.js, or Python. The datum can be placed and stored in any cloud being refined and ready for use in Artificial Intelligence forecasting systems, therefore. As a result, conclusions can be released immediately from a notebook in the form of virtual visualized solutions and Application Programming Interfaces. Compilations of data can be processed at the same time, which is really time-efficient since you can avoid applying a conventional respond circle that needs code translation into various coding languages and sending the results up and down.

Another amazing open-source component to be added to the notebooks in order to facilitate data research is PixieDust. This solution enables scientists and developers to produce data visualized objects without code swiftly and to release these conclusions as independent web applications. In a simple phrase, the data can be of easier access for even those users with a few experts on the technical side. The thing is data that is embodied in the form of visual objects is more adoptive that information in the form of code and digits.

Data deployment to address business solutions

Synthesis of data science and software development expertise, as well as cutting-edge instruments like Jupyter notebooks and PixieDust, drastically reinforce the innovational capacities, which can seem from one of the examples – data on wheat and corn harvests.

Data on harvests of wheat and corn can be collected and processed along with many other packages of information obtained from various sources to address business issues. For instance, wheat and corn harvests impact the world prices for food products that, in turn, affect the entire economic results. So, you can assemble harvest data to create a program that will allow forecasting world markets declines and growth by using Machine Learning principles. The retrospective data compilation on harvests together with current cultivation situation and market demand trends analysis can become a basis for the creation of an Application Programming Interface, which will be used by governments and international organizations to mitigate poverty in Africa or to ensure equal protein consumption distribution in Asia.

By studying the power of data science and software development expertise combination, it becomes obvious that the potential of this innovative approach is unlimited, and each next progress in the industry will overshadow all previous achievements.

Cooperation leads to innovation

These days cloud technology is on the ascendant producing more opportunities to research bigger volumes of data at much higher velocity. The cloud principles are driving the convergence of data science and software development industries that, in fact, used to function as rather isolated segments caused by using different approaches, instruments, and coding languages. Yet, these issues seem to be resolved since both scientists and developers have gained new advanced tools to boost workflows and increase interconnection experience. Thanks to upgraded productivity data science experts and software developers are enabled to produce improved user-friendly applications at a huge pace.

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