How is DevOps related to data science
What are data science platforms and why are they so important for data scientists?
Photo: Myriam Jessier / Unsplash Data Science represents the connection between technology and specialist area A data science platform provides data scientists with a tool landscape that allows them to work on business-relevant topics. Which these are depends on the respective company and the task at hand. Werner Rohrer, head of the Vienna office and managing director of the data science company solvistas Germany, sums up the term: “Data science represents the connection between technology and specialist area, is therefore part of the holistic understanding of business and is therefore becoming increasingly important. In addition, this interdisciplinary field allows extended insights into everyday business life and describes relationships that extend over several areas and provide the decision-maker with well-founded information that helps him to assess his situation and make the right decisions. ”In order to be able to guarantee this, you need Data science experts - who are usually brought in externally for companies and in the context of projects of any size, for startups, medium-sized companies or international corporations - an agile data science platform.
There is no one solution
Experienced data scientists know that data science platforms must be able to cover the essential areas: Data management, which includes the disciplines of data warehouse, big data, data quality, meta data management, data analytics, which includes machine learning, artificial intelligence, Predictive analytics and pure statistics count, as do business intelligence, including reporting and self-service BI. Werner Rohrer adds: "In addition to tools from different manufacturers, a data science platform includes an agile method set that is necessary to quickly retrieve quality-assured information from a heterogeneous data landscape." For the data science experts, it is therefore important that their work tools is modular and manufacturer-independent and offers good integration options. It is also important, especially with regard to complex tasks in which artificial intelligence and machine learning are integrated, that appropriate tools can be integrated.
Data science platforms today and tomorrow
Photo: solvistas Werner Rohrer, solvistas: "Methods from the areas of AI, neural networks and predictive analytics will have a decisive influence on how data scientists will work in the future" Technologies, tools and methods, for example from mathematics and statistics, have been used for more than 20 years to collect data, analyze it and extract information from it that can be used as a basis for decision-making. Companies use this knowledge, for example, in risk management and for trend forecasts. "The basis has not changed, but additional features, the increased use of analytical, highly automated methods from the areas of AI, neural networks and predictive analytics will have a significant impact on how data scientists will work in the future," says Werner Rohrer. A mobile phone, for example, is used to illustrate the topic. The basic functionality - being able to make cordless calls while on the move - has not changed. Numerous apps have been added to this day with which we can play games, edit photos and videos, sign documents, track the weather anywhere or do our banking.
Specially trained specialists are the be-all and end-all
Compared to smartphone applications, which in principle anyone can operate and use, working with data science platforms is reserved for highly trained data scientists. solvistas has founded its own academy to enable career starters and employees, consisting of statisticians, application developers, DWH and business intelligence experts and analysts, to achieve optimal development. Knowledge and best practice examples are conveyed in the hybrid lectures and workshops.
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