Trends and Transformation of Data Science

Enterprises today depend on huge amounts of data that come from sources up and down their supply chains. The scope of data evolves from monolithic applications that held all the relevant data within themselves to big data warehouses, operational data stores and ETL (extract, transform and load) technologies that took data from different applications, transform it and move it to support more complex analytics, all within the boundaries of an enterprise. It's now further transform into a global, interdependent information supply chain that mirrors the way producers, suppliers and consumers worldwide are connected. In addition to the broadening scope of data, enterprises are challenged by its massive scale, including huge amounts of up-to-the-second data coming from operational technologies like sensors and devices, news feeds and social media. Businesses are also managing their historical data and often connecting that data with their new data for "instant" use cases, such as: IOT, Cloud Computing. The scope of relevant information for today's data-driven enterprises extends beyond the boundaries of their firewalls. The enormous scale of data and the tremendous speed at which the data must have to be processed for data Ecosystem for future Evolving Data Science. The field of data science is constantly evolving driven by advance in technology, new techniques and methodologies, and the increasing importance of data driven decision making in organizations like the ecosystem continues to evolve, several key trends are shaping the future of data science. Analyze the collected data to identify the key trends and patterns in the evolving data science ecosystem, based on the methodologies of the emergence of new tools and technologies, change in data processing.