Introduction to Cloudera Data Science Workbench

There is a lot, and maybe too much, hype around AI lately. Data Scientists have being referred to as rock stars because of the expectation AI could deliver to organizations as new opportunities.

A Data Scientist job consists of using data to impact the company business and operations. He gathers 3 mains skills: domain expertise, mathematics and computer science. Being a problem solver, he is expected to have good comprehension skills and to be a great communicator. He has some good math and statistic skills as well as a strong understanding of the business domain. He also has the responsibility to manipulate the data and write algorithms which are shippable into production environments. The reality of who we call Data Scientists has diverged a bit. Most of them have intermediate programming knowledge which doesn’t empower them to face the data challenges they are facing: working with batch and streaming data ingestion workflows, publishing KPI and APIs, sharing models between multiple platforms and environments, scaling or tuning performances, leveraging DevOps tools and practices, placing the code into production with security and operational constraints…

The Big Data stack is complex. Time is limited. The proliferation of tools and technologies and the pace of the industry can be overwhelming. Choosing the right tools for the right problem is crucial. The learning curve may be steep. Tools are expected to make life easier, not harder.

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