It's a Monday morning; you're in your cubicle with a cup of strong coffee. An email notification breaks the silence. You have a new message: "Hey handsome, this is Ms. BigData, a free spirit, live on this vibrant planet, would you marry me and would you like to have Modbies (Models) with me?" You may feel overwhelmed by the words, feel both flattered and at the same time uncertain. It's Monday, and you've not yet hit your stride.
Is your Data Science skillset up to date? What are some core Data Science skills that will make you stand out if you are looking for a job? Even more important, what skills will help make your job as a Data Scientist easier and more productive?
For years now, companies have been hiding behind the moniker "machine learning" as a way to avoid the stigma associated (or associated in the past from the AI winters) with "artificial intelligence". Well, no more. This week Google announced Google Neural Machine Translation (GNMT) that gets close to human performance.
Cloud giants like Amazon, Google, Azure and IBM have rushed into the big data analytics cloud market. They claim their tools will make developer tasks simple. For machine learning, they say their cloud products will free data scientists and developers from implementation details so they can focus on business logic.
The free 2016 O'Reilly Data Science Salary Survey has been released. This year, they applied a handy linear regression and spelled out the entire model on page 42. A couple of things jumped out at me. In terms of $1,000s of dollars:
Google Cloud Machine Learning (CML) is an effort to simplify and reduce time and cost of building a data science tech infrastructure for data science projects. It can plug into Google's other storage, querying, and data-handling products to generate machine learning models. Among the data sources is Google Cloud Dataproc - a managed Hadoop and Spark platform that is now in general availability.
Before the onset of Big Data, businesses had to wait on data warehouses and data analytics to organize and interpret data so as to provide batch intelligence to users. Today, organizations use Data Science and Big Data Analytics to analyze data in real time.
Why should you adopt Big Data Analytics in your organization?