We are excited to announce the general availability of the Amazon Lex and Genesys PureCloud integrated interactive voice response (IVR) solution. First launched in preview at re:Invent 2017, the solution allows Genesys customers to integrate Amazon Lex chatbots into the PureCloud  IVR flows to provide an enhanced conversational experience.

“Two decades ago the folks who prepared our reports, graphs, and visualizations were ‘data analysts’ who knew how to extract data from relational data warehouses and run it through reporting and visualization tools like Crystal Reports. Ten years ago, predictive models were built by ‘predictive modelers’ who understood both the extraction and preparation of the data as well as the specialized predictive analytic tools like SAS and SPSS that allowed them to prepare predictive models. In the last few years, Gartner now declares that we need ‘data scientist’ who have all the above skills but also understand the complexities of the new NoSQL databases like Hadoop and can marry data from many sources and types together to produce useful and profitable predictive models. The requirement for broader and deeper skills is real and must factor into any business decision to build in-house capacity, as well as vetting potential consultants.” William Vorhies ( August 19, 2014 )

The New York Times published an election map. A lot of people did not like the map, arguing that it was an inaccurate representation. Those who did like the map argued that one must consider intent before throwing a map to the flames.

So, it’s 2018 and the word is spread about Data boom. There are Tech Giants like Facebook, Amazon, and Google constantly working in the field of Machine learning and Data science.

Even as we continue to reach new technological milestones and solve the world’s most demanding problems, many companies are still confronted with the oldest of administrative nightmares: piles and piles of mail.

While I was refreshing my stats knowledge recently, I had some challenges following along with the explanation that was given in class, so I decided to write one out for myself for future reference.

Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python)